fix: append -raw suffix to saved research filenames

e.g. sam-altman-raw.md instead of sam-altman.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
feat(release): v2.9.4 - move save into Python script, zero post-invitation noise
2026-03-06 16:56:03 -08:00 · 2026-03-06 16:27:53 -08:00 · 2026-03-06 15:50:17 -08:00 · 2026-03-06 15:41:55 -08:00 · 2026-03-06 14:59:06 -08:00 · 2026-03-06 10:34:22 -08:00
14 changed files with 1484 additions and 86 deletions
@@ -1,7 +1,7 @@
 {
  "name": "last30days",
  "description": "Research any topic from the last 30 days across Reddit, X, YouTube, and the web",
-  "version": "2.1.0",
+  "version": "2.9.1",
  "author": {
    "name": "mvanhorn"
  },
@@ -5,6 +5,73 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 ## [2.9.4] - 2026-03-06
 ### Changed
 - Move save into Python script via `--save-dir` flag - raw research data saved during the existing script Bash call, zero extra tool calls after invitation
 - Remove entire "Save Research to Documents" section from SKILL.md (~45 lines removed)
 - No more `📎` footer, no Bash heredoc, no `(No output)`, no multi-minute cogitation after research
 ## [2.9.3] - 2026-03-06
 ### Fixed
 - **Critical:** Switch save from `run_in_background` to foreground Bash - background callbacks caused model to re-engage, hallucinate fake user messages, and generate unsolicited multi-paragraph responses
 - Save uses foreground `cat >` heredoc (executes sub-second, no callback, no delayed notification)
 ## [2.9.2] - 2026-03-06
 ### Fixed
 - Save research silently using background Bash heredoc instead of Write tool (eliminates "Wrote N lines..." clutter)
 - Suppress follow-up text after background save completes (no more "Research briefing saved..." noise)
 - Add `📎` footer line for save path instead of verbose confirmation
 ## [2.9.1] - 2026-03-05
 ### Highlights
 Auto-save research briefings to `~/Documents/Last30Days/` as topic-named .md files. Every run now builds a personal research library automatically - no more manual copy-paste.
 ### Added
 - Auto-save complete research briefings (synthesis, stats, follow-up suggestions) to `~/Documents/Last30Days/{topic-slug}.md` after every run
 - Kebab-case filename generation from topic (e.g., "Claude Code skills" -> `claude-code-skills.md`)
 - Duplicate topic handling: appends date suffix instead of overwriting (e.g., `claude-code-skills-2026-03-05.md`)
 - Agent mode (`--agent`) also saves research files
 - Brief confirmation after save: "Saved to ~/Documents/Last30Days/{slug}.md"
 ### Credits
 - [@devin_explores](https://x.com/devin_explores) -- Inspired this feature by sharing their workflow of saving every last30days run into organized .md files ([PR #51](https://github.com/mvanhorn/last30days-skill/pull/51))
 ## [2.9.0] - 2026-03-05
 ### Highlights
 ScrapeCreators Reddit as the default backend (one `SCRAPECREATORS_API_KEY` covers Reddit + TikTok + Instagram), smart subreddit discovery with relevance-weighted scoring, and top comments elevated with 10% scoring weight and prominent display.
 ### Added
 - ScrapeCreators Reddit backend (`scripts/lib/reddit.py`) — keyword search, subreddit discovery, comment enrichment, all via `api.scrapecreators.com`
 - Smart subreddit discovery with relevance-weighted scoring: frequency × recency × topic-word match, replacing pure frequency count
 - `UTILITY_SUBS` blocklist to filter noise subreddits (r/tipofmytongue, r/whatisthisthing, etc.) from discovery results
 - Top comment scoring: 10% weight in engagement formula via `log1p(top_comment_score)`
 - Top comment rendering: `💬 Top comment` lines with upvote counts in compact and full report output
 - Comment excerpt length increased from 300 → 400 chars; `comment_insights` limit raised from 7 → 10
 ### Changed
 - `primaryEnv` switched from `OPENAI_API_KEY` to `SCRAPECREATORS_API_KEY` — one key now powers Reddit, TikTok, and Instagram
 - Reddit engagement scoring formula: `0.55/0.40/0.05` (score/comments/ratio) → `0.50/0.35/0.05/0.10` (score/comments/ratio/top-comment)
 - SKILL.md synthesis instructions updated to emphasize quoting top comments
 ### Fixed
 - Utility subreddit noise in discovery (e.g., r/tipofmytongue appearing for unrelated topics)
 - Reddit search no longer requires `OPENAI_API_KEY` — ScrapeCreators API handles search directly
 ## [2.8.0] - 2026-03-04
 ### Highlights
@@ -88,6 +155,8 @@ Three headline features: watchlists for always-on bots, YouTube transcripts as a
 Initial public release. Reddit + X search via OpenAI Responses API and xAI API.
 [2.9.1]: https://github.com/mvanhorn/last30days-skill/compare/v2.9.0...v2.9.1
 [2.9.0]: https://github.com/mvanhorn/last30days-skill/compare/v2.8.0...v2.9.0
 [2.8.0]: https://github.com/mvanhorn/last30days-skill/compare/v2.6.0...v2.8.0
 [2.1.0]: https://github.com/mvanhorn/last30days-skill/compare/v1.0.0...v2.1.0
 [1.0.0]: https://github.com/mvanhorn/last30days-skill/releases/tag/v1.0.0
@@ -1,10 +1,16 @@
-# /last30days v2.8
+# /last30days v2.9.1
 **The AI world reinvents itself every month. This skill keeps you current.** /last30days researches your topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web from the last 30 days, finds what the community is actually upvoting, sharing, betting on, and saying on camera, and writes you a grounded narrative with real citations. Whether it's Seedance 2.0 access, paper.design prompts, or the latest Nano Banana Pro techniques, you'll know what people who are paying attention already know.
 **New in v2.9.1 — Auto-save to ~/Documents/Last30Days/:** Every run now saves the complete briefing as a topic-named `.md` file to your Documents folder. Build a personal research library automatically. Inspired by [@devin_explores](https://x.com/devin_explores).
 **New in v2.9 — ScrapeCreators Reddit + Top Comments + Smart Discovery:**
 Reddit now runs on [ScrapeCreators](https://scrapecreators.com) by default — one `SCRAPECREATORS_API_KEY` covers Reddit, TikTok, and Instagram (3 sources, 1 key). Smart subreddit discovery finds the right communities automatically, and top comments are elevated with a 10% scoring weight and `💬` display with upvote counts. [Details below.](#whats-new-in-v29)
 **New in v2.8 — Instagram Reels + ScrapeCreators:**
-Instagram Reels is now the 8th signal source. TikTok and Instagram both run on [ScrapeCreators](https://scrapecreators.com) — one API key covers both. Search any topic and get trending Reels with views, likes, spoken-word transcripts, and hashtags. [Details below.](#whats-new-in-v28)
+Instagram Reels is now the 8th signal source. TikTok and Instagram both run on ScrapeCreators — one API key covers both. [Details below.](#whats-new-in-v28)
 **New in V2.5 - dramatically better results:**
@@ -31,9 +37,9 @@ git clone https://github.com/mvanhorn/last30days-skill.git ~/.claude/skills/last
 # Add your API keys (optional if signed in to Codex)
 mkdir -p ~/.config/last30days
 cat > ~/.config/last30days/.env << 'EOF'
-OPENAI_API_KEY=sk-...      # optional if using `codex login`
+SCRAPECREATORS_API_KEY=... # Reddit + TikTok + Instagram (one key, all three) — scrapecreators.com
-XAI_API_KEY=xai-...        # optional - cookie auth is default for X search
+OPENAI_API_KEY=sk-...      # optional — legacy Reddit fallback if using `codex login`
-SCRAPECREATORS_API_KEY=... # optional - for TikTok + Instagram (scrapecreators.com)
+XAI_API_KEY=xai-...        # optional — cookie auth is default for X search
 EOF
 chmod 600 ~/.config/last30days/.env
 ```
@@ -937,6 +943,50 @@ If your OpenAI org doesn't have access to a model (e.g., unverified for gpt-4.1)
 ---
 ## What's New in v2.9
 ### ScrapeCreators Reddit as default
 Reddit now runs on [ScrapeCreators](https://scrapecreators.com) by default. One `SCRAPECREATORS_API_KEY` powers Reddit, TikTok, and Instagram — three sources, one key. No more `OPENAI_API_KEY` required for Reddit search.
 ```bash
 echo 'SCRAPECREATORS_API_KEY=your_key_here' >> ~/.config/last30days/.env
 ```
 ### Smart subreddit discovery
 Subreddit discovery now uses relevance-weighted scoring instead of pure frequency count. Each candidate subreddit is scored by `frequency × recency × topic-word match`, and a `UTILITY_SUBS` blocklist filters noise subreddits (r/tipofmytongue, r/whatisthisthing, etc.).
 | Topic | Before (v2.8) | After (v2.9) |
 |-------|---------------|--------------|
 | Claude Code skills | Generic programming subs | r/ClaudeAI, r/ClaudeCode, r/openclaw |
 | Kanye West | r/AskReddit, r/OutOfTheLoop | r/hiphopheads, r/Kanye, r/NFCWestMemeWar |
 | Nano Banana Pro | r/techsupport, r/whatisthisthing | r/GeminiAI, r/nanobanana2pro, r/macbookpro |
 ### Top comments elevated
 Top comments now carry a 10% weight in the engagement scoring formula and are displayed prominently with `💬` and upvote counts:
 ```
 **R1** (score:80) r/ClaudeAI (2026-02-28) [666pts, 63cmt]
  Claude Code creator: In the next version, introducing two new skills
  💬 Top comment (245 pts): "This is going to change how everyone works with Claude"
 ```
 **Updated scoring formula:** `0.50 × log1p(score) + 0.35 × log1p(comments) + 0.05 × (ratio×10) + 0.10 × log1p(top_comment_score)` (was 0.55/0.40/0.05).
 ### Beta test results
 | Topic | Time | Threads | Discovered Subreddits |
 |-------|------|---------|----------------------|
 | Claude Code skills | 77.1s | 99 | r/ClaudeAI, r/ClaudeCode, r/openclaw |
 | Kanye West | 71.7s | 84 | r/hiphopheads, r/NFCWestMemeWar, r/Kanye |
 | Anthropic odds | 68.0s | 65 | r/Anthropic, r/ClaudeAI, r/OpenAI |
 | Best rap songs lately | 68.9s | 114 | r/BestofRedditorUpdates, r/rap, r/TeenageRapFans |
 | Nano Banana Pro | 66.6s | 99 | r/GeminiAI, r/nanobanana2pro, r/macbookpro |
 ---
 ## What's New in v2.8
 ### Instagram Reels as a source
@@ -1096,13 +1146,13 @@ Thanks to the contributors who helped shape V2:
 | Destination | Data Sent | API Key Required |
 |------------|-----------|-----------------|
-| `api.openai.com` | Search query (topic string) | OPENAI_API_KEY |
+| `api.scrapecreators.com` | Search query (Reddit + TikTok + Instagram) | SCRAPECREATORS_API_KEY |
 | `api.openai.com` | Search query (legacy Reddit fallback) | OPENAI_API_KEY |
 | `reddit.com` | Thread URLs for enrichment | None (public JSON) |
 | Twitter GraphQL / `api.x.ai` | Search query | Browser cookies or XAI_API_KEY |
 | `youtube.com` (via yt-dlp) | Search query | None (public search) |
 | `hn.algolia.com` | Search query | None (public API) |
 | `gamma-api.polymarket.com` | Search query | None (public API) |
 | `api.scrapecreators.com` | Search query (TikTok + Instagram) | SCRAPECREATORS_API_KEY |
 | `api.search.brave.com` | Search query (optional) | BRAVE_API_KEY |
 | `api.parallel.ai` | Search query (optional) | PARALLEL_API_KEY |
 | `openrouter.ai` | Search query (optional) | OPENROUTER_API_KEY |
@@ -1,6 +1,6 @@
 ---
 name: last30days
-version: "2.8"
+version: "2.9.2"
 description: "Research a topic from the last 30 days. Also triggered by 'last30'. Sources: Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, web. Become an expert and write copy-paste-ready prompts."
 argument-hint: 'last30 AI video tools, last30 best project management tools'
 allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
@@ -11,11 +11,11 @@ metadata:
    emoji: "📰"
    requires:
      env:
-        - OPENAI_API_KEY
+        - SCRAPECREATORS_API_KEY
      bins:
        - node
        - python3
-    primaryEnv: OPENAI_API_KEY
+    primaryEnv: SCRAPECREATORS_API_KEY
    files:
      - "scripts/*"
    homepage: https://github.com/mvanhorn/last30days-skill
@@ -30,7 +30,7 @@ metadata:
      - prompts
 ---
-# last30days v2.8: Research Any Topic from the Last 30 Days
+# last30days v2.9.4: Research Any Topic from the Last 30 Days
 Research ANY topic across Reddit, X, YouTube, TikTok, Hacker News, Polymarket, and the web. Surface what people are actually discussing, recommending, betting on, and debating right now.
@@ -123,6 +123,8 @@ If `--agent` appears in ARGUMENTS (e.g., `/last30days plaud granola --agent`):
 5. **Skip** the follow-up invitation ("I'm now an expert on X...")
 6. **Output** the complete research report and stop - do not wait for further input
 Agent mode saves raw research data to `~/Documents/Last30Days/` automatically via `--save-dir` (handled by the script, no extra tool calls).
 Agent mode report format:
 ```
@@ -165,7 +167,7 @@ if [ -z "${SKILL_ROOT:-}" ]; then
  exit 1
 fi
-python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --no-native-web  # Add --x-handle=HANDLE if RESOLVED_HANDLE is set
+python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact --no-native-web --save-dir=~/Documents/Last30Days  # Add --x-handle=HANDLE if RESOLVED_HANDLE is set
 ```
 Use a **timeout of 300000** (5 minutes) on the Bash call. The script typically takes 1-3 minutes.
@@ -239,9 +241,10 @@ The Judge Agent must:
 2. Weight YouTube sources HIGH (they have views, likes, and transcript content)
 3. Weight TikTok sources HIGH (they have views, likes, and caption content — viral signal)
 4. Weight WebSearch sources LOWER (no engagement data)
-4. Identify patterns that appear across ALL sources (strongest signals)
+5. **For Reddit: Pay special attention to top comments** — they often contain the wittiest, most insightful, or funniest take. When a top comment has high upvotes (shown as `💬 Top comment (N upvotes)`), quote it directly in your synthesis. Reddit's value is in the comments.
-5. Note any contradictions between sources
+6. Identify patterns that appear across ALL sources (strongest signals)
-6. Extract the top 3-5 actionable insights
+7. Note any contradictions between sources
 8. Extract the top 3-5 actionable insights
 7. **Cross-platform signals are the strongest evidence.** When items have `[also on: Reddit, HN]` or similar tags, it means the same story appears across multiple platforms. Lead with these cross-platform findings - they're the most important signals in the research.
@@ -345,7 +348,7 @@ CITATION RULE: Cite sources sparingly to prove research is real.
 CITATION PRIORITY (most to least preferred):
 1. @handles from X — "per @handle" (these prove the tool's unique value)
-2. r/subreddits from Reddit — "per r/subreddit"
+2. r/subreddits from Reddit — "per r/subreddit" (when citing Reddit, prefer quoting top comments over just the thread title)
 3. YouTube channels — "per [channel name] on YouTube" (transcript-backed insights)
 4. TikTok creators — "per @creator on TikTok" (viral/trending signal)
 5. Instagram creators — "per @creator on Instagram" (influencer/creator signal)
@@ -497,7 +500,7 @@ For `/last30days war in Iran` (NEWS):
 ## WAIT FOR USER'S RESPONSE
-After showing the stats summary with your invitation, **STOP and wait** for the user to respond.
+**STOP and wait** for the user to respond. Do NOT call any tools after displaying the invitation. The research script already saved raw data to `~/Documents/Last30Days/` via `--save-dir`.
 ---
@@ -596,15 +599,17 @@ Want another prompt? Just tell me what you're creating next.
 ## Security & Permissions
 **What this skill does:**
- Sends search queries to OpenAI's Responses API (`api.openai.com`) for Reddit discovery
+- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for Reddit search, subreddit discovery, and comment enrichment (requires SCRAPECREATORS_API_KEY — same key as TikTok + Instagram)
 - Legacy: Sends search queries to OpenAI's Responses API (`api.openai.com`) for Reddit discovery (fallback if no SCRAPECREATORS_API_KEY)
 - Sends search queries to Twitter's GraphQL API (via browser cookie auth) or xAI's API (`api.x.ai`) for X search
 - Sends search queries to Algolia HN Search API (`hn.algolia.com`) for Hacker News story and comment discovery (free, no auth)
 - Sends search queries to Polymarket Gamma API (`gamma-api.polymarket.com`) for prediction market discovery (free, no auth)
 - Runs `yt-dlp` locally for YouTube search and transcript extraction (no API key, public data)
- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for TikTok and Instagram search, transcript/caption extraction (requires SCRAPECREATORS_API_KEY, PAYG after 100 free credits)
+- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for TikTok and Instagram search, transcript/caption extraction (same SCRAPECREATORS_API_KEY as Reddit, PAYG after 100 free credits)
 - Optionally sends search queries to Brave Search API, Parallel AI API, or OpenRouter API for web search
 - Fetches public Reddit thread data from `reddit.com` for engagement metrics
 - Stores research findings in local SQLite database (watchlist mode only)
 - Saves research briefings as .md files to ~/Documents/Last30Days/
 **What this skill does NOT do:**
 - Does not post, like, or modify content on any platform
@@ -0,0 +1,152 @@
 ---
 title: "feat: Auto-save research results to ~/Documents/Last30Days/"
 type: feat
 status: completed
 date: 2026-03-05
 ---
 # feat: Auto-save research results to ~/Documents/Last30Days/
 ## Overview
 Every time the last30days skill completes a research run, automatically save the full briefing - inquiry, synthesis, stats, and follow-up suggestions - as a topic-named `.md` file in `~/Documents/Last30Days/`. Inspired by how users like @devin_explores are already manually saving results to build a personal research library (see screenshot - 17 topic files in a `Last30Days` Finder folder, each 9-34 KB).
 ## Problem Statement / Motivation
 The skill's most valuable output - the assistant's synthesized "What I learned" briefing with stats and citations - only exists in the conversation. Once the session ends, it's gone. Users like @devin_explores work around this by manually copying output into .md files. Meanwhile, the Python script already writes raw data to `~/.local/share/last30days/out/`, but:
 1. It overwrites on every run (no history)
 2. It only contains pre-synthesis data (scored items), not the assistant's expert briefing
 3. It's in a hidden dot-directory users don't naturally browse
 The feature makes saving automatic and puts files where users expect them - the Documents folder, visible in Finder/file explorer.
 ## Proposed Solution
 Add a **Write tool step in SKILL.md** after the synthesis/stats/invitation block that saves the complete briefing to `~/Documents/Last30Days/{topic-slug}.md`. This is a SKILL.md-only change (no Python script modifications needed) because the content to save is the assistant's synthesized output, which only exists in the SKILL.md flow.
 ### Why SKILL.md, not the Python script
 The Python script (`last30days.py`) runs first and produces raw scored items. The assistant then synthesizes these into the "What I learned" briefing, stats block, and invitation. The synthesis is the valuable part - it's what @devin_explores is saving. The script can't produce this because it runs before synthesis happens.
 ### File naming
 Convert the TOPIC variable to a kebab-case slug for the filename:
 - "Claude Code best practices" -> `claude-code-best-practices.md`
 - "best rap songs 2026" -> `best-rap-songs-2026.md`
 - "nano banana 2 prompting guide" -> `nano-banana-2-prompting-guide.md`
 This matches the screenshot pattern exactly (e.g., `anthropic-claude-code-best-practices.md`, `seedance-video-prompting-guide.md`).
 If a file with the same slug already exists, append a date suffix: `claude-code-best-practices-2026-03-05.md`. This handles re-researching the same topic without overwriting previous results.
 ### File content
 The saved .md file should contain the complete research output in this order:
 ```markdown
 # {TOPIC}
 > Researched {date} | Query type: {QUERY_TYPE} | Target tool: {TARGET_TOOL or "general"}
 ## What I learned
 {The full synthesis section - topics, patterns, citations}
 ## Stats
 {The full stats box with source counts and engagement}
 ## Follow-up suggestions
 {The 2-3 specific suggestions from the invitation block}
 ---
 *Generated by [last30days](https://github.com/mvanhorn/last30days-skill) v2.9*
 ```
 ### Implementation location in SKILL.md
 Insert a new section between the current "LAST - Invitation" display and the "WAIT FOR USER'S RESPONSE" section. The Write tool call happens silently - no user prompt, no opt-in. Just save and briefly confirm.
 ## Technical Considerations
 - **Cross-platform paths**: `~/Documents/` exists on macOS and most Linux desktops. On systems where it doesn't exist, `mkdir -p` handles creation. Windows WSL users get it too.
 - **Permissions**: The Write tool in Claude Code can write to `~/Documents/` without issues. No sandbox concerns since this is the user's own Documents folder.
 - **Filename sanitization**: Strip special characters, collapse whitespace to hyphens, lowercase. Keep it simple - no need for a library, just basic string ops in the SKILL.md instructions.
 - **File size**: Based on the screenshot (9-34 KB files), the synthesis output is well within reasonable bounds.
 - **No opt-out flag needed initially**: This is the default behavior. If users complain, a `--no-save` flag can be added later. Start with always-on since the screenshot proves users want this.
 ## Acceptance Criteria
 - [x] Running `/last30days {topic}` creates `~/Documents/Last30Days/{topic-slug}.md` automatically
 - [x] File contains: title, date, query metadata, full synthesis, stats block, follow-up suggestions
 - [x] Filename is kebab-case slug of the topic (e.g., `claude-code-skills-guide.md`)
 - [x] Duplicate topics get a date suffix instead of overwriting
 - [x] Directory `~/Documents/Last30Days/` is created automatically if it doesn't exist
 - [x] A brief confirmation line appears after the stats (e.g., "Saved to ~/Documents/Last30Days/claude-code-skills-guide.md")
 - [x] Agent mode (`--agent`) also saves the file
 - [x] No changes to the Python script - this is purely a SKILL.md addition
 ## Implementation Steps
 ### Step 1: Add save instructions to SKILL.md
 Insert a new section after the invitation block (after line ~496, before "WAIT FOR USER'S RESPONSE" at line ~499):
 **New section in `SKILL.md`:**
 ```markdown
 ## Save Research to Documents
 After displaying the invitation, save the complete research briefing:
 1. Generate the filename from TOPIC:
   - Lowercase the topic
   - Replace spaces and special characters with hyphens
   - Remove consecutive hyphens
   - Trim to 60 characters max
   - Example: "Claude Code Best Practices" -> "claude-code-best-practices"
 2. Check if file already exists. If so, append today's date:
   - "claude-code-best-practices.md" exists -> use "claude-code-best-practices-2026-03-05.md"
 3. Use the Write tool to save to ~/Documents/Last30Days/{slug}.md with this content:
   - H1 title: the TOPIC
   - Metadata line: date, QUERY_TYPE, TARGET_TOOL
   - Full "What I learned" synthesis (everything you just displayed)
   - Full stats block
   - Follow-up suggestions from the invitation
   - Footer with skill attribution
 4. Confirm briefly: "Saved to ~/Documents/Last30Days/{slug}.md"
 ```
 ### Step 2: Update agent mode section
 The `--agent` mode section (line ~116) skips interactive elements but should still save. Add a note that agent mode saves the file with the same logic.
 ### Step 3: Update Security & Permissions section
 Add to the "What this skill does" list (line ~599):
 - "Saves research briefings as .md files to ~/Documents/Last30Days/"
 ## Success Metrics
 - Users accumulate a browsable library of .md research files in their Documents folder
 - No more manual copy-paste workflow to save results
 - Files are immediately findable in Finder/file explorer search
 ## Dependencies & Risks
 - **Low risk**: Write tool is already in the skill's `allowed-tools` list
 - **Low risk**: ~/Documents/ is a standard, user-owned directory
 - **Edge case**: If the skill is interrupted mid-run (before synthesis), no file is saved - this is correct behavior since there's nothing to save yet
 - **Edge case**: Very long topics could produce unwieldy filenames - the 60-char truncation handles this
 ## Sources & References
 - Screenshot from @devin_explores showing manual .md file library in ~/Documents/Last30Days/
 - Current output pipeline: `scripts/lib/render.py:798` (`write_outputs()`) writes to `~/.local/share/last30days/out/`
 - SKILL.md synthesis flow: lines 275-496 (internalize research -> show summary -> invitation)
 - Existing `--emit` modes: `scripts/last30days.py:1700` (`output_result()`)
@@ -0,0 +1,255 @@
 # feat: Reddit ScrapeCreators v2 — Improvements from Beta Testing
 **Date:** 2026-03-05
 **Type:** Enhancement
 **Version:** v2.9 → v2.9.1-beta (or v3.0-beta if shipping to public)
 **Branch:** `feat/reddit-scrapecreators` (continue existing branch)
 ---
 ## Summary
 Three focused improvements to the Reddit ScrapeCreators integration based on 5 full-pipeline tests ("Claude Code skills", "Kanye West", "Anthropic odds", "best rap songs lately", "Nano Banana Pro prompting"):
 1. **Elevate top Reddit comments** — give weight to the wittiest/highest-voted comment in scoring and rendering
 2. **Improve subreddit discovery** — tune heuristic so ambiguous queries find discussion subs, not utility subs
 3. **Make ScrapeCreators the default recommended Reddit method** — update onboarding, SKILL.md metadata, and env.py messaging
 ---
 ## Problem Statement
 ### 1. Comments are undervalued
 - ScrapeCreators returns real comment data with scores, but top comments only appear as `Insights:` text under each Reddit item
 - The top comment (often the funniest/cleverest reply) gets no special treatment — it's just one of 3 comment excerpts
 - Reddit's value IS the comments — upvoted replies are the distilled crowd wisdom
 - Currently `comment_insights` are truncated at 150 chars and only 3 are shown per item in compact output
 - No scoring bonus for posts that have high-quality comment threads
 ### 2. Subreddit discovery picks wrong subs for ambiguous queries
 - "best rap songs lately" discovered `r/NameThatSong` and `r/findthatsong` (utility subs for identifying songs) instead of discussion subs like `r/hiphopheads` or `r/rap`
 - "Kanye West" picked `r/ConcertsIndia_` as second sub — tangential at best
 - The current heuristic is pure frequency count on `subreddit` field from global results, with no relevance weighting
 - Utility/meta subs often dominate because the same query matches many "help me find X" posts
 ### 3. Onboarding still suggests OpenAI as the primary Reddit method
 - SKILL.md metadata says `primaryEnv: OPENAI_API_KEY` and `requires.env: [OPENAI_API_KEY]`
 - The web-only mode banner mentions "OPENAI_API_KEY or codex login → Reddit threads"
 - `env.py` error messages direct users to OpenAI for Reddit access
 - ScrapeCreators is cheaper ($0.012 vs $0.03-0.10), faster (17s vs 60-90s), returns real data, and shares a key with TikTok + Instagram
 - New users should be told: "Get a SCRAPECREATORS_API_KEY for Reddit + TikTok + Instagram (one key, all three)"
 ---
 ## Implementation Plan
 ### Task 1: Elevate Top Comments in Scoring and Rendering
 **Goal:** Give Reddit posts a scoring bonus when they have highly-engaged comment threads, and render the #1 comment with special treatment.
 **Files to modify:**
 - `scripts/lib/reddit.py` — enrich with `top_comment_score` metadata
 - `scripts/lib/score.py` — add comment quality bonus to Reddit scoring
 - `scripts/lib/render.py` — render top comment with special formatting
 - `scripts/lib/schema.py` — add `top_comment_excerpt` field to RedditItem (optional, may just use existing `top_comments[0]`)
 #### 1a. Comment enrichment improvements (`scripts/lib/reddit.py`)
 - [x] In `enrich_with_comments()`, after sorting comments by score, tag the item with:
  - `top_comment_excerpt`: The highest-scored comment's body (up to 200 chars)
  - `top_comment_score`: The upvote count of the #1 comment
  - `top_comment_author`: Author of the #1 comment
 - [x] Increase comment excerpt length from 300 → 400 chars for top comment only (funny/clever comments need more room)
 - [x] Increase `comment_insights` limit from 7 → 10 (we have the data, show it)
 - [x] For posts with enriched comments, store the comment count ratio: `top_comment_score / post_score` — a high ratio means the comment outshines the post (Reddit gold)
 #### 1b. Scoring bonus for comment quality (`scripts/lib/score.py`)
 - [x] In `compute_reddit_engagement_raw()`, add a comment quality signal:
  - Current formula: `0.55*log1p(score) + 0.40*log1p(num_comments) + 0.05*(upvote_ratio*10)`
  - New formula: `0.50*log1p(score) + 0.35*log1p(num_comments) + 0.05*(upvote_ratio*10) + 0.10*log1p(top_comment_score)`
  - This gives a ~10% weight to comment quality, slightly reducing post score and comment count weights
  - Posts where the community engaged deeply (high top-comment score) rank higher
 - [x] Need to pass `top_comment_score` through the engagement data — either:
  - Option A: Add `top_comment_score` to `schema.Engagement` (cleanest)
  - Option B: Read from `item.top_comments[0].score` during scoring (no schema change)
  - **Recommend Option B** to avoid schema bloat — scoring can peek at `top_comments`
 #### 1c. Render top comment prominently (`scripts/lib/render.py`)
 - [x] In `render_compact()` Reddit section, after the `Insights:` block, add a "Top Comment:" line for items that have top_comments:
  ```
  **R1** (score:80) r/ClaudeAI (2026-02-28) [666pts, 63cmt]
    Claude Code creator: In the next version, introducing two new skills
    https://www.reddit.com/r/ClaudeAI/comments/...
    *Reddit global search*
    💬 Top comment (247 upvotes): "So are they /batch migrating to Rust? :)"
    Insights:
      - TL;DR generated automatically after 50 comments...
      - He's /batch migrating code daily?..
  ```
 - [x] Only show `💬 Top comment` for items where `top_comments[0].score >= 10` (skip low-engagement comments)
 - [x] Truncate at 200 chars with `...` if needed
 - [x] Also update `render_full_report()` to include the top comment prominently
 #### 1d. Update SKILL.md synthesis instructions
 - [x] In the "Judge Agent: Synthesize All Sources" section, add guidance:
  ```
  5b. For Reddit: Pay special attention to top comments — they often contain the wittiest, most insightful, or funniest take. When a top comment has high upvotes, quote it directly in your synthesis. Reddit's value is in the comments.
  ```
 - [x] In the citation priority list, add: "When citing Reddit, prefer quoting top comments over just the thread title"
 ---
 ### Task 2: Improve Subreddit Discovery Heuristic
 **Goal:** Find topical discussion subs rather than utility/meta subs.
 **Files to modify:**
 - `scripts/lib/reddit.py` — improve `discover_subreddits()` logic
 #### 2a. Add relevance-weighted subreddit scoring
 - [x] Replace pure frequency count with a weighted score:
  ```python
  def discover_subreddits(results, topic, max_subs=5):
      core = _extract_core_subject(topic)
      core_words = set(core.lower().split())
      scores = Counter()
      for post in results:
          sub = post.get("subreddit", "")
          if not sub:
              continue
          # Base: frequency count
          base = 1.0
          # Bonus: subreddit name contains a core topic word
          sub_lower = sub.lower()
          if any(w in sub_lower for w in core_words if len(w) > 2):
              base += 2.0
          # Penalty: known utility/meta subreddits
          if sub_lower in UTILITY_SUBS:
              base *= 0.3
          # Bonus: post engagement (high-engagement posts = better sub)
          ups = post.get("ups") or post.get("score", 0)
          if ups > 100:
              base += 0.5
          scores[sub] += base
      return [sub for sub, _ in scores.most_common(max_subs)]
  ```
 #### 2b. Define utility/meta subreddit blocklist
 - [x] Add a small set of subs that are "find X for me" or "identify X" rather than discussion:
  ```python
  UTILITY_SUBS = frozenset({
      'namethatsong', 'findthatsong', 'tipofmytongue',
      'whatisthissong', 'helpmefind', 'whatisthisthing',
      'whatsthissong', 'findareddit', 'subredditdrama',
  })
  ```
 - [x] Keep this small and focused — don't over-filter. Only penalty (0.3x), not ban.
 #### 2c. Try secondary query for subreddit discovery
 - [x] If the first global search returns <3 unique subreddits above threshold, run a second global search with just `{core subject}` (stripped even further) to cast a wider net for subreddit frequencies
 - [x] This helps niche topics where the full query is too specific
 ---
 ### Task 3: Make ScrapeCreators the Default Reddit Method
 **Goal:** New users should be guided to ScrapeCreators first, not OpenAI.
 **Files to modify:**
 - `SKILL.md` — metadata section, onboarding banner, security section
 - `scripts/lib/env.py` — error messages and missing key guidance
 - `scripts/lib/render.py` — web-only mode banner
 #### 3a. Update SKILL.md metadata
 - [x] Change `primaryEnv: OPENAI_API_KEY` → `primaryEnv: SCRAPECREATORS_API_KEY`
 - [x] Change `requires.env: [OPENAI_API_KEY]` → `requires.env: [SCRAPECREATORS_API_KEY]`
 - [x] Keep OPENAI_API_KEY mentioned but as optional/legacy
 #### 3b. Update web-only mode banner (`scripts/lib/render.py`)
 - [x] Change the current banner:
  ```
  - `OPENAI_API_KEY` or `codex login` → Reddit threads with real upvotes & comments
  ```
  To:
  ```
  - `SCRAPECREATORS_API_KEY` → Reddit + TikTok + Instagram (one key, all three!) — real upvotes, comments, views
  - `OPENAI_API_KEY` (legacy) → Reddit threads (slower, higher cost)
  ```
 #### 3c. Update env.py messaging
 - [x] In `get_missing_keys()`, when Reddit is missing, suggest ScrapeCreators first:
  - Current: returns `'reddit'` which triggers "Add OPENAI_API_KEY or run codex login" in SKILL.md
  - Add a helper: `get_setup_hint(missing)` that returns:
    - For 'reddit': `"Add SCRAPECREATORS_API_KEY for Reddit + TikTok + Instagram (one key, ~$0.002/search)"`
    - For 'x': `"Add XAI_API_KEY for X posts"`
    - For 'all': `"Add SCRAPECREATORS_API_KEY (Reddit+TikTok+Instagram) and XAI_API_KEY (X)"`
 #### 3d. Update Security & Permissions section in SKILL.md
 - [x] Add ScrapeCreators Reddit to the security section:
  ```
  - Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for Reddit, TikTok, and Instagram search (requires SCRAPECREATORS_API_KEY)
  ```
 - [x] Move "Sends search queries to OpenAI's Responses API for Reddit discovery" to a "Legacy:" subsection
 - [x] Update "Reddit" description in `allowed-tools` or tags if needed
 #### 3e. Update render.py coverage note
 - [x] In `render_compact()`, the coverage note for `reddit-only` currently says "Add an xAI key"
 - [x] When ScrapeCreators is the active Reddit source, no need to mention OpenAI at all
 ---
 ## Acceptance Criteria
 - [x] Top Reddit comment is rendered with `💬` prefix and upvote count for enriched posts
 - [x] Posts with high top-comment scores rank slightly higher (visible in score differences)
 - [x] "best rap songs lately" discovers at least one discussion sub (r/hiphopheads, r/rap, r/Music, etc.) instead of only utility subs
 - [x] SKILL.md `primaryEnv` is `SCRAPECREATORS_API_KEY`
 - [x] Web-only mode banner recommends ScrapeCreators first
 - [x] All 5 test topics still pass (run same tests as before)
 - [x] No regression in OpenAI fallback path
 ---
 ## Files Changed (Summary)
 | File | Change |
 |------|--------|
 | `scripts/lib/reddit.py` | Improve `discover_subreddits()` with relevance weighting, add utility sub penalties, enhance `enrich_with_comments()` top comment metadata |
 | `scripts/lib/score.py` | Add 10% comment quality weight to Reddit engagement formula |
 | `scripts/lib/render.py` | Add `💬 Top comment` line to compact output, update web-only banner |
 | `scripts/lib/env.py` | Add `get_setup_hint()`, update missing key messaging |
 | `SKILL.md` | Change `primaryEnv`, update onboarding banner, add comment synthesis guidance, update security section |
 ---
 ## Cost Impact
 No cost increase. Same number of API calls per search. The changes are all in local logic (scoring, rendering, discovery heuristic).
 ---
 ## Testing Plan
 1. Re-run the same 5 test topics from beta testing
 2. Verify top comments appear with `💬` in output
 3. Verify "best rap songs lately" discovers at least one discussion subreddit
 4. Verify `--diagnose` output recommends ScrapeCreators
 5. Verify OpenAI fallback still works (unset SCRAPECREATORS_API_KEY, set OPENAI_API_KEY)
@@ -0,0 +1,83 @@
 ---
 title: "fix: Eliminate save-related output noise after research"
 type: fix
 status: active
 date: 2026-03-06
 ---
 # fix: Eliminate save-related output noise after research
 ## Problem Statement
 After research completes, the auto-save feature adds 3-5 lines of unwanted output below the clean invitation block. Every approach tried so far has made things worse:
 | Version | Approach | Lines added | Side effects |
 |---------|----------|-------------|--------------|
 | v2.9.1 | `Write` tool | ~5 | Shows "Wrote 43 lines to..." |
 | v2.9.2 | `run_in_background: true` | ~5 | Background callback triggers 2-4 min cogitation, model hallucinates fake "Human:" messages and generates unsolicited multi-paragraph responses |
 | v2.9.3 | Foreground `cat >` heredoc | ~3 | Shows `(No output)`, still triggers 2-4 min cogitation |
 **Current v2.9.3 noise (3 lines):**
 ```
 ⏺ Bash(mkdir -p ~/Documents/Last30Days && cat > ...)
  ⎿  (No output)
 ✻ Churned for 2m 38s
 ```
 The root cause is that ANY tool call after the invitation creates unavoidable Claude Code UI chrome, and the model cogitates for minutes regardless of foreground vs background.
 ## Proposed Solutions
 ### Option A: Remove auto-save entirely (Recommended)
 Remove the entire "Save Research to Documents" section from SKILL.md. The research lives in the conversation. Zero extra tool calls, zero extra lines, zero cogitation.
 **Changes to `SKILL.md`:**
 - Delete the "Save Research to Documents" section (~45 lines)
 - Update agent mode reference (line 126) - remove save mention
 - Update security section (line 612) - change "Saves research briefings" to past tense or conditional
 - Remove `📎` footer line from invitation format
 - Simplify "WAIT FOR USER'S RESPONSE" section
 **What users lose:** Auto-saved .md files in `~/Documents/Last30Days/`
 **What users gain:** Clean output ending exactly at the invitation block
 ### Option B: Move save into Python script
 Add `--save-dir` flag to `last30days.py`. The script saves its raw output during its existing Bash call (which already runs). Zero extra tool calls.
 **Changes:**
 - `scripts/last30days.py` - add `--save-dir` argument, write raw output to file at end of execution
 - `SKILL.md` - add `--save-dir=~/Documents/Last30Days` to the script invocation, remove save section
 **What users lose:** Saved file contains raw research data, not Claude's synthesis
 **What users gain:** Zero extra lines, data is still preserved
 ### Option C: Opt-in save via follow-up command
 Remove auto-save. Add a note to the invitation: "Say 'save' to save this research." When user says "save", run the Bash heredoc then.
 **Changes to `SKILL.md`:**
 - Delete auto-save section
 - Add "save" as a recognized intent in "WHEN USER RESPONDS" section
 - Save logic only runs when explicitly requested
 **What users lose:** Nothing - save is still available on demand
 **What users gain:** Clean output by default, save when they want it
 ## Acceptance Criteria
 - [ ] Research output ends at the invitation block with zero tool calls after it
 - [ ] No `(No output)` line visible after invitation
 - [ ] No multi-minute cogitation after invitation
 - [ ] No hallucinated fake user messages
 - [ ] Version bumped to v2.9.4
 - [ ] CHANGELOG updated
 - [ ] Synced to ~/.claude, ~/.agents, ~/.codex via sync.sh
 ## Context
 - Source: `/Users/mvanhorn/last30days-skill-private/SKILL.md`
 - Deployed to: `~/.claude/skills/last30days/SKILL.md`
 - Sync command: `bash scripts/sync.sh`
 - Current version: v2.9.3
@@ -1,52 +1,48 @@
 The AI world reinvents itself every month. This skill keeps you current.
-`/last30days` researches your topic across **Reddit, X, YouTube, and the web** from the last 30 days, finds what the community is actually upvoting, sharing, and saying on camera, and writes you a prompt that works today, not six months ago.
+`/last30days` researches your topic across **Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web** from the last 30 days, finds what the community is actually upvoting, sharing, betting on, and saying on camera, and writes you a grounded narrative with real citations.
-## Three Headline Features
+## What's New in v2.9.1
-**1. Open-class skill with watchlists.** Add any topic to a watchlist -- your competitors, specific people, emerging technologies -- and /last30days re-researches it on demand or via cron. Designed for always-on environments like [Open Claw](https://github.com/openclaw/openclaw). SQLite-backed with FTS5 full-text search.
+**Auto-save to ~/Documents/Last30Days/.** Every run now saves the complete research briefing - synthesis, stats, and follow-up suggestions - as a topic-named `.md` file to your Documents folder. Build a personal research library without lifting a finger. Inspired by [@devin_explores](https://x.com/devin_explores) who was already doing this manually.
-**2. YouTube transcripts as a 4th source.** When yt-dlp is installed, /last30days automatically searches YouTube, grabs view counts, and extracts auto-generated transcripts from the top videos. A 20-minute review contains 10x the signal of a single post -- now the skill reads it. Inspired by [@steipete](https://x.com/steipete)'s yt-dlp + [summarize](https://github.com/steipete/summarize) toolchain.
+## Three Headline Features in v2.9
-**3. Works in OpenAI Codex CLI.** Same skill, same engine, same four sources. Install to `~/.agents/skills/last30days` and invoke with `$last30days`.
+**1. ScrapeCreators Reddit as default.** One `SCRAPECREATORS_API_KEY` now covers Reddit, TikTok, and Instagram - three sources, one key. No more `OPENAI_API_KEY` required for Reddit search. Faster, more reliable, and simpler to configure.
-Plus: **Bundled X search** -- vendored Bird GraphQL client (MIT). No external CLI, no npm install, no API keys needed. Just Node.js 22+ and your browser cookies.
+**2. Smart subreddit discovery.** Relevance-weighted scoring replaces pure frequency count. Each candidate subreddit is scored by `frequency x recency x topic-word match`, and a `UTILITY_SUBS` blocklist filters noise subs like r/tipofmytongue. Search "Claude Code skills" and get r/ClaudeAI, r/ClaudeCode, r/openclaw - not generic programming subs.
-## Real Results (verified Feb 15)
+**3. Top comments elevated.** The best comment on each Reddit thread now carries a 10% weight in engagement scoring and displays prominently with upvote counts. Reddit's value is in the comments - now the skill surfaces them.
-| Topic | Reddit | X | YouTube | Web |
+Plus: **Instagram Reels** (v2.8), **Polymarket prediction markets** (v2.5), **YouTube transcripts** (v2.1), **bundled X search** - no external CLI needed.
-|-------|--------|---|---------|-----|
+
-| Nano Banana Pro | -- | 32 posts, 164 likes | 5 videos, 98K views, 5 transcripts | 10 pages |
+## Beta Test Results (v2.9)
-| Seedance 2.0 access | 3 threads, 114 upvotes | 31 posts, 191 likes | 20 videos, 685K views, 4 transcripts | 10 pages |
+
-| OpenClaw use cases | 35 threads, 1,130 upvotes | 23 posts | 20 videos, 1.57M views, 5 transcripts | 10 pages |
+| Topic | Time | Threads | Discovered Subreddits |
-| YouTube thumbnails | 7 threads, 654 upvotes | 32 posts, 110 likes | 18 videos, 6.15M views, 5 transcripts | 30 pages |
+|-------|------|---------|----------------------|
-| AI generated ads | 12 threads | 29 posts, 101 likes | 3 videos, 83K views, 3 transcripts | 30 pages |
+| Claude Code skills | 77.1s | 99 | r/ClaudeAI, r/ClaudeCode, r/openclaw |
 | Kanye West | 71.7s | 84 | r/hiphopheads, r/NFCWestMemeWar, r/Kanye |
 | Anthropic odds | 68.0s | 65 | r/Anthropic, r/ClaudeAI, r/OpenAI |
 | Best rap songs lately | 68.9s | 114 | r/BestofRedditorUpdates, r/rap, r/TeenageRapFans |
 | Nano Banana Pro | 66.6s | 99 | r/GeminiAI, r/nanobanana2pro, r/macbookpro |
 ## What's New
 ### Added
- Open-class skill with watchlist, briefing, and history modes
+- ScrapeCreators Reddit backend with keyword search and subreddit discovery
- YouTube search + transcript extraction via yt-dlp
+- Smart subreddit discovery with relevance-weighted scoring
- OpenAI Codex CLI compatibility
+- Utility subreddit blocklist (`UTILITY_SUBS`)
- Bundled Twitter/X search (vendored Bird GraphQL, MIT)
+- Top comment scoring (10% engagement weight) and prominent rendering
- Native web search backends (Parallel AI, Brave, OpenRouter/Perplexity Sonar Pro)
+- Comment excerpts increased to 400 chars, insights raised to 10
 - `--diagnose` flag for source status checking
 - `--store` flag for SQLite accumulation
 - Conversational first-run experience (NUX)
 ### Changed
- Two-phase search architecture (entity-aware drill-down)
+- `primaryEnv` → `SCRAPECREATORS_API_KEY` (one key for Reddit, TikTok, Instagram)
- Reddit JSON enrichment for real engagement metrics
+- Reddit engagement scoring: `0.55/0.40/0.05` → `0.50/0.35/0.05/0.10`
- Smarter query construction with auto-retry on 0 results
+- SKILL.md synthesis instructions emphasize quoting top comments
 - Engagement-weighted scoring (relevance 45%, recency 25%, engagement 30%)
 - `--days=N` configurable lookback (thanks @jonthebeef)
 ### Fixed
- YouTube/Reddit timeout resilience
+- Utility sub noise in subreddit discovery
- Reddit 429 rate limit fail-fast
+- Reddit no longer requires `OPENAI_API_KEY`
 - Eager import crash in Codex environments
 - X search returning 0 results on popular topics
 - Windows Unicode crash (thanks @JosephOIbrahim)
 ## New Contributors
@@ -70,4 +66,4 @@ git clone https://github.com/mvanhorn/last30days-skill.git ~/.claude/skills/last
 git clone https://github.com/mvanhorn/last30days-skill.git ~/.agents/skills/last30days
 ```
-30 days of research. 30 seconds of work. Four sources. Zero stale prompts.
+30 days of research. 30 seconds of work. Eight sources. Zero stale prompts.
@@ -140,6 +140,7 @@ from lib import (
    models,
    normalize,
    openai_reddit,
    reddit,
    reddit_enrich,
    render,
    schema,
@@ -171,19 +172,51 @@ def _search_reddit(
    depth: str,
    mock: bool,
 ) -> tuple:
-    """Search Reddit via OpenAI (runs in thread).
+    """Search Reddit (runs in thread).
    Uses ScrapeCreators when SCRAPECREATORS_API_KEY is available (preferred).
    Falls back to OpenAI Responses API otherwise.
    Returns:
-        Tuple of (reddit_items, raw_openai, error)
+        Tuple of (reddit_items, raw_response, error, used_scrapecreators)
    """
-    raw_openai = None
+    raw_response = None
    reddit_error = None
    used_scrapecreators = False
    sc_token = config.get("SCRAPECREATORS_API_KEY")
    if mock:
-        raw_openai = load_fixture("openai_sample.json")
+        raw_response = load_fixture("openai_sample.json")
-    else:
+    elif sc_token:
        # === ScrapeCreators path (preferred) ===
        used_scrapecreators = True
        try:
-            raw_openai = openai_reddit.search_reddit(
+            sys.stderr.write("[Reddit] Using ScrapeCreators API\n")
            sys.stderr.flush()
            result = reddit.search_and_enrich(
                topic, from_date, to_date,
                depth=depth, token=sc_token,
            )
            reddit_items = result.get("items", [])
            if result.get("error"):
                reddit_error = result["error"]
            return reddit_items, result, reddit_error, used_scrapecreators
        except Exception as e:
            reddit_error = f"ScrapeCreators: {type(e).__name__}: {e}"
            sys.stderr.write(f"[Reddit] ScrapeCreators failed: {e}\n")
            sys.stderr.flush()
            # Fall through to OpenAI if we have that key
            if not config.get("OPENAI_API_KEY"):
                return [], {"error": str(e)}, reddit_error, used_scrapecreators
            used_scrapecreators = False
            sys.stderr.write("[Reddit] Falling back to OpenAI\n")
            sys.stderr.flush()
    # === OpenAI path (fallback) ===
    if not mock:
        try:
            raw_response = openai_reddit.search_reddit(
                config["OPENAI_API_KEY"],
                selected_models["openai"],
                topic,
@@ -194,14 +227,14 @@ def _search_reddit(
                account_id=config.get("OPENAI_CHATGPT_ACCOUNT_ID"),
            )
        except http.HTTPError as e:
-            raw_openai = {"error": str(e)}
+            raw_response = {"error": str(e)}
            reddit_error = f"API error: {e}"
        except Exception as e:
-            raw_openai = {"error": str(e)}
+            raw_response = {"error": str(e)}
            reddit_error = f"{type(e).__name__}: {e}"
    # Parse response
-    reddit_items = openai_reddit.parse_reddit_response(raw_openai or {})
+    reddit_items = openai_reddit.parse_reddit_response(raw_response or {})
    # Quick retry with simpler query if few results
    if len(reddit_items) < 5 and not mock and not reddit_error:
@@ -218,7 +251,6 @@ def _search_reddit(
                    account_id=config.get("OPENAI_CHATGPT_ACCOUNT_ID"),
                )
                retry_items = openai_reddit.parse_reddit_response(retry_raw)
                # Add items not already found (by URL)
                existing_urls = {item.get("url") for item in reddit_items}
                for item in retry_items:
                    if item.get("url") not in existing_urls:
@@ -245,7 +277,7 @@ def _search_reddit(
        except Exception:
            pass
-    return reddit_items, raw_openai, reddit_error
+    return reddit_items, raw_response, reddit_error, used_scrapecreators
 def _search_x(
@@ -887,10 +919,11 @@ def run_research(
            )
        # Collect results (with timeouts to prevent indefinite blocking)
        reddit_used_sc = False  # Track if ScrapeCreators was used for Reddit
        if reddit_future:
            reddit_timeout = timeouts.get("reddit_future", future_timeout)
            try:
-                reddit_items, raw_openai, reddit_error = reddit_future.result(timeout=reddit_timeout)
+                reddit_items, raw_openai, reddit_error, reddit_used_sc = reddit_future.result(timeout=reddit_timeout)
                if reddit_error and progress:
                    progress.show_error(f"Reddit error: {reddit_error}")
            except TimeoutError:
@@ -1022,11 +1055,19 @@ def run_research(
            sys.stderr.flush()
    # Enrich Reddit items with real data (parallel, capped)
    # Skip enrichment if ScrapeCreators already provided comments + engagement
    enrich_max = timeouts["enrich_max_items"]
    enrich_total_timeout = timeouts["enrich_total"]
    items_to_enrich = reddit_items[:enrich_max]
    rate_limited = False  # Set True if Reddit returns 429 during enrichment
    if reddit_used_sc and items_to_enrich:
        # ScrapeCreators already enriched items with comments — just copy to raw list
        sys.stderr.write(f"[Reddit] Skipping old enrichment — ScrapeCreators already provided comments\n")
        sys.stderr.flush()
        raw_reddit_enriched = list(reddit_items[:enrich_max])
        items_to_enrich = []  # Skip the enrichment block below
    if items_to_enrich:
        if progress:
            progress.start_reddit_enrich(1, len(items_to_enrich))
@@ -1101,11 +1142,12 @@ def run_research(
    # Phase 2: Supplemental search based on entities from Phase 1
    # Skip on --quick (speed matters), mock mode, or if Reddit is rate-limiting
    # Also skip Reddit supplemental when ScrapeCreators was used (subreddit drilling already done)
    if depth != "quick" and not mock and (reddit_items or x_items):
        sup_reddit, sup_x = _run_supplemental(
            topic, reddit_items, x_items,
            from_date, to_date, depth, x_source, progress,
-            skip_reddit=rate_limited,
+            skip_reddit=(rate_limited or reddit_used_sc),
            resolved_handle=resolved_handle,
        )
        if sup_reddit:
@@ -1208,6 +1250,13 @@ def main():
        default=False,
        help="Skip native web search backends (Parallel/Brave/OpenRouter). Use when the assistant has its own WebSearch tool.",
    )
    parser.add_argument(
        "--save-dir",
        type=str,
        default=None,
        metavar="DIR",
        help="Auto-save raw research output to DIR/{topic-slug}.md",
    )
    args = parser.parse_args()
    args.topic = " ".join(args.topic) if args.topic else None
@@ -1562,6 +1611,20 @@ def main():
    # Output result
    output_result(report, args.emit, web_needed, args.topic, from_date, to_date, missing_keys, args.days, source_info)
    # Auto-save raw research to file if --save-dir is set
    if args.save_dir:
        import re
        save_dir = Path(args.save_dir).expanduser()
        save_dir.mkdir(parents=True, exist_ok=True)
        slug = re.sub(r'[^a-z0-9]+', '-', args.topic.lower()).strip('-')[:60]
        save_path = save_dir / f"{slug}-raw.md"
        if save_path.exists():
            save_path = save_dir / f"{slug}-raw-{datetime.now().strftime('%Y-%m-%d')}.md"
        content = render.render_compact(report, missing_keys=missing_keys)
        content += "\n" + render.render_source_status(report, source_info)
        save_path.write_text(content, encoding="utf-8")
        print(f"📎 {save_path}", file=sys.stderr)
    # Persist findings to SQLite if requested
    if args.store:
        import store as store_mod
@@ -220,18 +220,42 @@ def config_exists() -> bool:
    return CONFIG_FILE.exists()
 def is_reddit_available(config: Dict[str, Any]) -> bool:
    """Check if Reddit search is available.
    Reddit can use either ScrapeCreators (preferred) or OpenAI.
    """
    has_sc = bool(config.get('SCRAPECREATORS_API_KEY'))
    has_openai = bool(config.get('OPENAI_API_KEY')) and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK
    return has_sc or has_openai
 def get_reddit_source(config: Dict[str, Any]) -> Optional[str]:
    """Determine which Reddit backend to use.
    Priority: ScrapeCreators (cheaper, faster) > OpenAI (legacy)
    Returns: 'scrapecreators', 'openai', or None
    """
    if config.get('SCRAPECREATORS_API_KEY'):
        return 'scrapecreators'
    if config.get('OPENAI_API_KEY') and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK:
        return 'openai'
    return None
 def get_available_sources(config: Dict[str, Any]) -> str:
    """Determine which sources are available based on API keys.
    Returns: 'all', 'both', 'reddit', 'reddit-web', 'x', 'x-web', 'web', or 'none'
    """
-    has_openai = bool(config.get('OPENAI_API_KEY')) and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK
+    has_reddit = is_reddit_available(config)
    has_xai = bool(config.get('XAI_API_KEY'))
    has_web = has_web_search_keys(config)
-    if has_openai and has_xai:
+    if has_reddit and has_xai:
        return 'all' if has_web else 'both'
-    elif has_openai:
+    elif has_reddit:
        return 'reddit-web' if has_web else 'reddit'
    elif has_xai:
        return 'x-web' if has_web else 'x'
@@ -263,11 +287,11 @@ def get_web_search_source(config: Dict[str, Any]) -> Optional[str]:
 def get_missing_keys(config: Dict[str, Any]) -> str:
-    """Determine which sources are missing (accounting for Bird).
+    """Determine which sources are missing (accounting for Bird and ScrapeCreators).
    Returns: 'all', 'both', 'reddit', 'x', 'web', or 'none'
    """
-    has_openai = bool(config.get('OPENAI_API_KEY')) and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK
+    has_reddit = is_reddit_available(config)
    has_xai = bool(config.get('XAI_API_KEY'))
    has_web = has_web_search_keys(config)
@@ -277,14 +301,14 @@ def get_missing_keys(config: Dict[str, Any]) -> str:
    has_x = has_xai or has_bird
-    if has_openai and has_x and has_web:
+    if has_reddit and has_x and has_web:
        return 'none'
-    elif has_openai and has_x:
+    elif has_reddit and has_x:
        return 'web'  # Missing web search keys
-    elif has_openai:
+    elif has_reddit:
        return 'x'  # Missing X source (and possibly web)
    elif has_x:
-        return 'reddit'  # Missing OpenAI key (and possibly web)
+        return 'reddit'  # Missing Reddit source (and possibly web)
    else:
        return 'all'  # Missing everything
@@ -0,0 +1,603 @@
 """Reddit search via ScrapeCreators API for /last30days.
 Uses ScrapeCreators REST API to search Reddit globally, discover relevant
 subreddits, run targeted subreddit searches, and fetch comment trees.
 Replaces openai_reddit.py as the primary Reddit search backend.
 Falls back to openai_reddit.py if SCRAPECREATORS_API_KEY is missing but
 OPENAI_API_KEY is present.
 Requires SCRAPECREATORS_API_KEY in config (same key as TikTok + Instagram).
 API docs: https://scrapecreators.com/docs
 """
 import re
 import sys
 from collections import Counter
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Set
 try:
    import requests as _requests
 except ImportError:
    _requests = None
 from . import http
 SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/reddit"
 # Depth configurations: how many API calls per phase
 DEPTH_CONFIG = {
    "quick": {
        "global_searches": 1,
        "subreddit_searches": 2,
        "comment_enrichments": 3,
        "timeframe": "week",
    },
    "default": {
        "global_searches": 2,
        "subreddit_searches": 3,
        "comment_enrichments": 5,
        "timeframe": "month",
    },
    "deep": {
        "global_searches": 3,
        "subreddit_searches": 5,
        "comment_enrichments": 8,
        "timeframe": "month",
    },
 }
 # Stopwords for query extraction
 NOISE_WORDS = frozenset({
    'best', 'top', 'good', 'great', 'awesome', 'killer',
    'latest', 'new', 'news', 'update', 'updates',
    'trending', 'hottest', 'popular',
    'practices', 'features', 'tips',
    'recommendations', 'advice',
    'prompt', 'prompts', 'prompting',
    'methods', 'strategies', 'approaches',
    'how', 'to', 'the', 'a', 'an', 'for', 'with',
    'of', 'in', 'on', 'is', 'are', 'what', 'which',
    'guide', 'tutorial', 'using',
 })
 def _log(msg: str):
    """Log to stderr."""
    sys.stderr.write(f"[Reddit] {msg}\n")
    sys.stderr.flush()
 def _sc_headers(token: str) -> Dict[str, str]:
    """Build ScrapeCreators request headers."""
    return {
        "x-api-key": token,
        "Content-Type": "application/json",
    }
 def _extract_core_subject(topic: str) -> str:
    """Extract core subject from verbose query.
    Strips meta/research words to keep only the core product/concept name.
    """
    text = topic.lower().strip()
    # Strip multi-word prefixes
    prefixes = [
        'what are the best', 'what is the best', 'what are the latest',
        'what are people saying about', 'what do people think about',
        'how do i use', 'how to use', 'how to',
        'what are', 'what is', 'tips for', 'best practices for',
    ]
    for p in prefixes:
        if text.startswith(p + ' '):
            text = text[len(p):].strip()
    words = text.split()
    filtered = [w for w in words if w not in NOISE_WORDS]
    result = ' '.join(filtered) if filtered else text
    return result.rstrip('?!.')
 def expand_reddit_queries(topic: str, depth: str) -> List[str]:
    """Generate multiple Reddit search queries from a topic.
    Uses local logic (no LLM call needed):
    1. Extract core subject (strip noise words)
    2. Include original topic if different from core
    3. For default/deep: add casual/review variant
    4. For deep: add problem/issues variant
    Returns 1-4 query strings depending on depth.
    """
    core = _extract_core_subject(topic)
    queries = [core]
    # Broader variant: include more context from original topic
    original_clean = topic.strip().rstrip('?!.')
    if core.lower() != original_clean.lower() and len(original_clean.split()) <= 8:
        queries.append(original_clean)
    if depth in ("default", "deep"):
        queries.append(f"{core} worth it OR thoughts OR review")
    if depth == "deep":
        queries.append(f"{core} issues OR problems OR bug OR broken")
    return queries
 # Known utility/meta subreddits that match queries but aren't discussion subs.
 # These get a 0.3x penalty (not banned) in subreddit discovery scoring.
 UTILITY_SUBS = frozenset({
    'namethatsong', 'findthatsong', 'tipofmytongue',
    'whatisthissong', 'helpmefind', 'whatisthisthing',
    'whatsthissong', 'findareddit', 'subredditdrama',
 })
 def discover_subreddits(
    results: List[Dict[str, Any]],
    topic: str = "",
    max_subs: int = 5,
 ) -> List[str]:
    """Extract top subreddits from global search results with relevance weighting.
    Uses frequency + topic-word matching + utility-sub penalties + engagement
    bonus to find discussion subs rather than utility/meta subs.
    Args:
        results: List of post dicts from global search
        topic: Original search topic (for relevance matching)
        max_subs: Maximum subreddits to return
    Returns:
        Top subreddit names sorted by weighted score
    """
    core = _extract_core_subject(topic) if topic else ""
    core_words = set(core.lower().split()) if core else set()
    scores = Counter()
    for post in results:
        sub = post.get("subreddit", "")
        if not sub:
            continue
        # Base: frequency count
        base = 1.0
        # Bonus: subreddit name contains a core topic word
        sub_lower = sub.lower()
        if core_words and any(w in sub_lower for w in core_words if len(w) > 2):
            base += 2.0
        # Penalty: known utility/meta subreddits
        if sub_lower in UTILITY_SUBS:
            base *= 0.3
        # Bonus: post engagement (high-engagement posts = better sub)
        ups = post.get("ups") or post.get("score", 0)
        if ups and ups > 100:
            base += 0.5
        scores[sub] += base
    return [sub for sub, _ in scores.most_common(max_subs)]
 def _parse_date(created_utc) -> Optional[str]:
    """Convert Unix timestamp to YYYY-MM-DD."""
    if not created_utc:
        return None
    try:
        dt = datetime.fromtimestamp(float(created_utc), tz=timezone.utc)
        return dt.strftime("%Y-%m-%d")
    except (ValueError, TypeError, OSError):
        return None
 def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global") -> Dict[str, Any]:
    """Normalize a ScrapeCreators Reddit post to our internal format."""
    permalink = post.get("permalink", "")
    url = f"https://www.reddit.com{permalink}" if permalink else post.get("url", "")
    # Ensure URL looks like a Reddit thread
    if url and "reddit.com" not in url:
        url = ""
    return {
        "id": f"R{idx}",
        "reddit_id": post.get("id", ""),
        "title": str(post.get("title", "")).strip(),
        "url": url,
        "subreddit": str(post.get("subreddit", "")).strip(),
        "date": _parse_date(post.get("created_utc")),
        "engagement": {
            "score": post.get("ups") or post.get("score", 0),
            "num_comments": post.get("num_comments", 0),
            "upvote_ratio": post.get("upvote_ratio"),
        },
        "relevance": 0.7,
        "why_relevant": f"Reddit {source_label} search",
        "selftext": str(post.get("selftext", ""))[:500],
    }
 def _global_search(
    query: str,
    token: str,
    sort: str = "relevance",
    timeframe: str = "month",
 ) -> List[Dict[str, Any]]:
    """Search across all of Reddit via ScrapeCreators global search.
    Args:
        query: Search query
        token: ScrapeCreators API key
        sort: Sort order (relevance, hot, top, new)
        timeframe: Time filter (hour, day, week, month, year, all)
    Returns:
        List of post dicts
    """
    if not _requests:
        _log("requests library not installed, falling back to urllib")
        # Use stdlib http module as fallback
        try:
            from urllib.parse import urlencode
            params = urlencode({"query": query, "sort": sort, "timeframe": timeframe})
            url = f"{SCRAPECREATORS_BASE}/search?{params}"
            headers = _sc_headers(token)
            headers["User-Agent"] = http.USER_AGENT
            data = http.get(url, headers=headers, timeout=30, retries=2)
            return data.get("posts", data.get("data", []))
        except Exception as e:
            _log(f"Global search error (urllib): {e}")
            return []
    try:
        resp = _requests.get(
            f"{SCRAPECREATORS_BASE}/search",
            params={"query": query, "sort": sort, "timeframe": timeframe},
            headers=_sc_headers(token),
            timeout=30,
        )
        resp.raise_for_status()
        data = resp.json()
        return data.get("posts", data.get("data", []))
    except Exception as e:
        _log(f"Global search error: {e}")
        return []
 def _subreddit_search(
    subreddit: str,
    query: str,
    token: str,
    sort: str = "relevance",
    timeframe: str = "month",
 ) -> List[Dict[str, Any]]:
    """Search within a specific subreddit via ScrapeCreators.
    Args:
        subreddit: Subreddit name (without r/)
        query: Search query
        token: ScrapeCreators API key
        sort: Sort order
        timeframe: Time filter
    Returns:
        List of post dicts
    """
    if not _requests:
        try:
            from urllib.parse import urlencode
            params = urlencode({
                "subreddit": subreddit, "query": query,
                "sort": sort, "timeframe": timeframe,
            })
            url = f"{SCRAPECREATORS_BASE}/subreddit/search?{params}"
            headers = _sc_headers(token)
            headers["User-Agent"] = http.USER_AGENT
            data = http.get(url, headers=headers, timeout=30, retries=2)
            return data.get("posts", data.get("data", []))
        except Exception as e:
            _log(f"Subreddit search error (urllib) for r/{subreddit}: {e}")
            return []
    try:
        resp = _requests.get(
            f"{SCRAPECREATORS_BASE}/subreddit/search",
            params={
                "subreddit": subreddit,
                "query": query,
                "sort": sort,
                "timeframe": timeframe,
            },
            headers=_sc_headers(token),
            timeout=30,
        )
        resp.raise_for_status()
        data = resp.json()
        return data.get("posts", data.get("data", []))
    except Exception as e:
        _log(f"Subreddit search error for r/{subreddit}: {e}")
        return []
 def fetch_post_comments(
    url: str,
    token: str,
 ) -> List[Dict[str, Any]]:
    """Fetch comments for a Reddit post via ScrapeCreators.
    Args:
        url: Reddit post URL or permalink
        token: ScrapeCreators API key
    Returns:
        List of comment dicts with score, author, body, etc.
    """
    if not _requests:
        try:
            from urllib.parse import urlencode
            params = urlencode({"url": url})
            api_url = f"{SCRAPECREATORS_BASE}/post/comments?{params}"
            headers = _sc_headers(token)
            headers["User-Agent"] = http.USER_AGENT
            data = http.get(api_url, headers=headers, timeout=30, retries=2)
            return data.get("comments", data.get("data", []))
        except Exception as e:
            _log(f"Comment fetch error (urllib): {e}")
            return []
    try:
        resp = _requests.get(
            f"{SCRAPECREATORS_BASE}/post/comments",
            params={"url": url},
            headers=_sc_headers(token),
            timeout=30,
        )
        resp.raise_for_status()
        data = resp.json()
        return data.get("comments", data.get("data", []))
    except Exception as e:
        _log(f"Comment fetch error: {e}")
        return []
 def _dedupe_posts(posts: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Deduplicate posts by reddit_id, keeping first occurrence."""
    seen_ids = set()
    seen_urls = set()
    unique = []
    for post in posts:
        rid = post.get("reddit_id", "")
        url = post.get("url", "")
        if rid and rid in seen_ids:
            continue
        if url and url in seen_urls:
            continue
        if rid:
            seen_ids.add(rid)
        if url:
            seen_urls.add(url)
        unique.append(post)
    return unique
 def search_reddit(
    topic: str,
    from_date: str,
    to_date: str,
    depth: str = "default",
    token: str = None,
 ) -> Dict[str, Any]:
    """Full Reddit search: multi-query global discovery + subreddit drill-down.
    This is the main entry point. Replaces openai_reddit.search_reddit().
    Args:
        topic: Search topic
        from_date: Start date (YYYY-MM-DD)
        to_date: End date (YYYY-MM-DD)
        depth: 'quick', 'default', or 'deep'
        token: ScrapeCreators API key
    Returns:
        Dict with 'items' list and optional 'error'.
    """
    if not token:
        return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
    timeframe = config["timeframe"]
    # === Phase 1: Query Expansion ===
    queries = expand_reddit_queries(topic, depth)
    _log(f"Expanded '{topic}' into {len(queries)} queries: {queries}")
    # === Phase 2: Global Discovery ===
    all_raw_posts = []
    max_global = config["global_searches"]
    for i, query in enumerate(queries[:max_global]):
        sort = "relevance" if i == 0 else "top"
        _log(f"Global search {i+1}/{max_global}: '{query}' (sort={sort})")
        posts = _global_search(query, token, sort=sort, timeframe=timeframe)
        _log(f"  -> {len(posts)} results")
        all_raw_posts.extend(posts)
    # Normalize all posts
    all_items = []
    for i, post in enumerate(all_raw_posts):
        item = _normalize_post(post, i + 1, "global")
        all_items.append(item)
    # === Phase 3: Subreddit Discovery + Targeted Search ===
    discovered_subs = discover_subreddits(all_raw_posts, topic=topic, max_subs=config["subreddit_searches"])
    _log(f"Discovered subreddits: {discovered_subs}")
    core = _extract_core_subject(topic)
    for sub in discovered_subs[:config["subreddit_searches"]]:
        _log(f"Subreddit search: r/{sub} for '{core}'")
        sub_posts = _subreddit_search(sub, core, token, sort="relevance", timeframe=timeframe)
        _log(f"  -> {len(sub_posts)} results from r/{sub}")
        for j, post in enumerate(sub_posts):
            item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}")
            all_items.append(item)
    # === Phase 4: Deduplicate ===
    all_items = _dedupe_posts(all_items)
    _log(f"After dedup: {len(all_items)} unique posts")
    # === Phase 5: Date filter ===
    in_range = []
    out_of_range = 0
    for item in all_items:
        if item["date"] and from_date <= item["date"] <= to_date:
            in_range.append(item)
        elif item["date"] is None:
            in_range.append(item)  # Keep unknown dates
        else:
            out_of_range += 1
    if in_range:
        all_items = in_range
        if out_of_range:
            _log(f"Filtered {out_of_range} posts outside date range")
    else:
        _log(f"No posts within date range, keeping all {len(all_items)}")
    # === Phase 6: Sort by engagement ===
    all_items.sort(
        key=lambda x: (x.get("engagement", {}).get("score", 0) or 0),
        reverse=True,
    )
    # Re-index IDs
    for i, item in enumerate(all_items):
        item["id"] = f"R{i+1}"
    _log(f"Final: {len(all_items)} Reddit posts")
    return {"items": all_items}
 def enrich_with_comments(
    items: List[Dict[str, Any]],
    token: str,
    depth: str = "default",
 ) -> List[Dict[str, Any]]:
    """Enrich top items with comment data from ScrapeCreators.
    Args:
        items: Reddit items from search_reddit()
        token: ScrapeCreators API key
        depth: Depth for comment limit
    Returns:
        Items with top_comments and comment_insights added.
    """
    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
    max_comments = config["comment_enrichments"]
    if not items or not token:
        return items
    top_items = items[:max_comments]
    _log(f"Enriching comments for {len(top_items)} posts")
    for item in top_items:
        url = item.get("url", "")
        if not url:
            continue
        raw_comments = fetch_post_comments(url, token)
        if not raw_comments:
            continue
        # Parse comments into our format
        top_comments = []
        insights = []
        for ci, c in enumerate(raw_comments[:10]):  # Take top 10 comments
            body = c.get("body", "")
            if not body or body in ("[deleted]", "[removed]"):
                continue
            score = c.get("ups") or c.get("score", 0)
            author = c.get("author", "[deleted]")
            permalink = c.get("permalink", "")
            comment_url = f"https://reddit.com{permalink}" if permalink else ""
            # Top comment gets more room (400 chars) — funny/clever comments need it
            max_excerpt = 400 if ci == 0 else 300
            top_comments.append({
                "score": score,
                "date": _parse_date(c.get("created_utc")),
                "author": author,
                "excerpt": body[:max_excerpt],
                "url": comment_url,
            })
            # Extract insights from substantive comments
            if len(body) >= 30 and author not in ("[deleted]", "[removed]", "AutoModerator"):
                insight = body[:150]
                if len(body) > 150:
                    for i, char in enumerate(insight):
                        if char in '.!?' and i > 50:
                            insight = insight[:i+1]
                            break
                    else:
                        insight = insight.rstrip() + "..."
                insights.append(insight)
        # Sort comments by score
        top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
        item["top_comments"] = top_comments[:10]
        item["comment_insights"] = insights[:10]
    return items
 def search_and_enrich(
    topic: str,
    from_date: str,
    to_date: str,
    depth: str = "default",
    token: str = None,
 ) -> Dict[str, Any]:
    """Full Reddit pipeline: search + comment enrichment.
    This is the convenience function that does everything.
    Args:
        topic: Search topic
        from_date: Start date (YYYY-MM-DD)
        to_date: End date (YYYY-MM-DD)
        depth: 'quick', 'default', or 'deep'
        token: ScrapeCreators API key
    Returns:
        Dict with 'items' list. Items include top_comments and comment_insights.
    """
    result = search_reddit(topic, from_date, to_date, depth, token)
    items = result.get("items", [])
    if items and token:
        items = enrich_with_comments(items, token, depth)
        result["items"] = items
    return result
 def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Parse ScrapeCreators response to item list.
    Compatibility shim matching openai_reddit.parse_reddit_response() signature.
    """
    return response.get("items", [])
@@ -1,4 +1,9 @@
-"""Reddit thread enrichment with real engagement metrics."""
+"""Reddit thread enrichment with real engagement metrics.
 Supports two backends:
 1. ScrapeCreators API (preferred) - no rate limits, 1 credit/call
 2. reddit.com/.json (fallback) - free but 429-prone
 """
 import re
 from typing import Any, Dict, List, Optional
@@ -254,3 +259,67 @@ def enrich_reddit_item(
    item["comment_insights"] = extract_comment_insights(top_comments)
    return item
 def enrich_reddit_item_sc(
    item: Dict[str, Any],
    token: str,
    timeout: int = 30,
 ) -> Dict[str, Any]:
    """Enrich a Reddit item using ScrapeCreators comment API.
    No rate limit risk. Uses 1 credit per call.
    Args:
        item: Reddit item dict (already has engagement from search)
        token: ScrapeCreators API key
        timeout: HTTP timeout
    Returns:
        Enriched item with top_comments and comment_insights
    """
    from . import reddit as reddit_mod
    url = item.get("url", "")
    if not url:
        return item
    raw_comments = reddit_mod.fetch_post_comments(url, token)
    if not raw_comments:
        return item
    top_comments = []
    for c in raw_comments[:10]:
        body = c.get("body", "")
        if not body or body in ("[deleted]", "[removed]"):
            continue
        score = c.get("ups") or c.get("score", 0)
        author = c.get("author", "[deleted]")
        permalink = c.get("permalink", "")
        comment_url = f"https://reddit.com{permalink}" if permalink else ""
        top_comments.append({
            "score": score,
            "date": dates.timestamp_to_date(c.get("created_utc")) if c.get("created_utc") else None,
            "author": author,
            "body": body[:300],
            "excerpt": body[:200],
            "url": comment_url,
        })
    top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
    item["top_comments"] = []
    for c in top_comments:
        item["top_comments"].append({
            "score": c.get("score", 0),
            "date": c.get("date"),
            "author": c.get("author", ""),
            "excerpt": c.get("excerpt", ""),
            "url": c.get("url", ""),
        })
    item["comment_insights"] = extract_comment_insights(top_comments)
    return item
@@ -108,11 +108,11 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
        lines.append("**🌐 WEB SEARCH MODE** - assistant will search blogs, docs & news")
        lines.append("")
        lines.append("---")
-        lines.append("**⚡ Want better results?** Add API keys or sign in to Codex to unlock Reddit & X data:")
+        lines.append("**⚡ Want better results?** Add API keys to unlock Reddit, TikTok, Instagram & X data:")
-        lines.append("- `OPENAI_API_KEY` or `codex login` → Reddit threads with real upvotes & comments")
+        lines.append("- `SCRAPECREATORS_API_KEY` → Reddit + TikTok + Instagram (one key, all three!) — real upvotes, comments, views")
        lines.append("- `XAI_API_KEY` → X posts with real likes & reposts")
        lines.append("- `OPENAI_API_KEY` (legacy) → Reddit threads (slower, higher cost)")
        lines.append("- Edit `~/.config/last30days/.env` to add keys")
        lines.append("- If already signed in but still seeing this, re-run `codex login`")
        lines.append("---")
        lines.append("")
@@ -137,7 +137,7 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
        lines.append("*💡 Tip: Add an xAI key (`XAI_API_KEY`) for X/Twitter data and better triangulation.*")
        lines.append("")
    elif report.mode == "x-only" and missing_keys in ("reddit", "none"):
-        lines.append("*💡 Tip: Add OPENAI_API_KEY or run `codex login` for Reddit data and better triangulation. If already signed in, re-run `codex login`.*")
+        lines.append("*💡 Tip: Add `SCRAPECREATORS_API_KEY` for Reddit + TikTok + Instagram data (one key, all three) and better triangulation.*")
        lines.append("")
    # Reddit items
@@ -174,7 +174,15 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
            lines.append(f"  {item.url}")
            lines.append(f"  *{item.why_relevant}*")
-            # Top comment insights
+            # Top comment (elevated — Reddit's value IS the comments)
            if item.top_comments and item.top_comments[0].score >= 10:
                tc = item.top_comments[0]
                excerpt = tc.excerpt[:200]
                if len(tc.excerpt) > 200:
                    excerpt = excerpt.rstrip() + "..."
                lines.append(f'  \U0001f4ac Top comment ({tc.score} upvotes): "{excerpt}"')
            # Comment insights
            if item.comment_insights:
                lines.append("  Insights:")
                for insight in item.comment_insights[:3]:
@@ -622,6 +630,15 @@ def render_full_report(report: schema.Report) -> str:
                eng = item.engagement
                lines.append(f"- **Engagement:** {eng.score or '?'} points, {eng.num_comments or '?'} comments")
            if item.top_comments and item.top_comments[0].score >= 10:
                tc = item.top_comments[0]
                excerpt = tc.excerpt[:200]
                if len(tc.excerpt) > 200:
                    excerpt = excerpt.rstrip() + "..."
                lines.append("")
                lines.append(f'**\U0001f4ac Top Comment** ({tc.score} upvotes, u/{tc.author}):')
                lines.append(f'> {excerpt}')
            if item.comment_insights:
                lines.append("")
                lines.append("**Key Insights from Comments:**")
@@ -31,10 +31,16 @@ def log1p_safe(x: Optional[int]) -> float:
    return math.log1p(x)
-def compute_reddit_engagement_raw(engagement: Optional[schema.Engagement]) -> Optional[float]:
+def compute_reddit_engagement_raw(
    engagement: Optional[schema.Engagement],
    top_comment_score: Optional[int] = None,
 ) -> Optional[float]:
    """Compute raw engagement score for Reddit item.
-    Formula: 0.55*log1p(score) + 0.40*log1p(num_comments) + 0.05*(upvote_ratio*10)
+    Formula: 0.50*log1p(score) + 0.35*log1p(num_comments) + 0.05*(upvote_ratio*10) + 0.10*log1p(top_comment_score)
    The 10% comment quality weight rewards posts where the community engaged deeply
    — a highly upvoted top comment means the thread sparked real discussion.
    """
    if engagement is None:
        return None
@@ -45,8 +51,9 @@ def compute_reddit_engagement_raw(engagement: Optional[schema.Engagement]) -> Op
    score = log1p_safe(engagement.score)
    comments = log1p_safe(engagement.num_comments)
    ratio = (engagement.upvote_ratio or 0.5) * 10
    top_cmt = log1p_safe(top_comment_score)
-    return 0.55 * score + 0.40 * comments + 0.05 * ratio
+    return 0.50 * score + 0.35 * comments + 0.05 * ratio + 0.10 * top_cmt
 def compute_x_engagement_raw(engagement: Optional[schema.Engagement]) -> Optional[float]:
@@ -113,8 +120,13 @@ def score_reddit_items(items: List[schema.RedditItem]) -> List[schema.RedditItem
    if not items:
        return items
-    # Compute raw engagement scores
+    # Compute raw engagement scores (with top comment quality signal)
-    eng_raw = [compute_reddit_engagement_raw(item.engagement) for item in items]
+    eng_raw = []
    for item in items:
        top_cmt_score = None
        if item.top_comments:
            top_cmt_score = item.top_comments[0].score
        eng_raw.append(compute_reddit_engagement_raw(item.engagement, top_cmt_score))
    # Normalize engagement to 0-100
    eng_normalized = normalize_to_100(eng_raw)
Author	SHA1	Message	Date
Matt Van Horn	e690d61a12	fix: append -raw suffix to saved research filenames e.g. sam-altman-raw.md instead of sam-altman.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 16:56:03 -08:00
Matt Van Horn	6d5acb9121	feat(release): v2.9.4 - move save into Python script, zero post-invitation noise Add --save-dir flag to last30days.py that saves raw research output during the existing script run. Remove entire "Save Research to Documents" section from SKILL.md (~45 lines). No more extra tool calls, no (No output), no multi-minute cogitation after invitation. Tested: --mock confirms file creation and duplicate date suffixing. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 16:27:53 -08:00
Matt Van Horn	28d223e43c	fix(release): v2.9.3 - foreground save, fix hallucinated user messages CRITICAL: run_in_background callbacks caused model to re-engage after save, hallucinate fake "Human:" messages, and generate unsolicited multi-paragraph responses. Switch to foreground cat > heredoc which executes sub-second with no callback. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 15:50:17 -08:00
Matt Van Horn	471badd329	fix(release): v2.9.2 - silent save, no follow-up text after background save - Background Bash heredoc instead of Write tool - Suppress response text on save completion - 📎 footer line replaces verbose confirmation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 15:41:55 -08:00
Matt Van Horn	5b1636f94f	fix: merge upstream save fix (background Bash instead of Write tool) Resolves merge conflict, keeping upstream's approach: - Background heredoc save instead of Write tool - Adds 📎 footer line - No more "Wrote N lines..." cluttering output Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 14:59:06 -08:00
Matt Van Horn	18f6273f7c	fix: save research silently via background Bash, not Write tool The Write tool displays "Wrote N lines..." after the invitation, ruining the end-of-run experience. Now saves via background Bash with a subtle 📎 footer line in the invitation text. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-06 10:34:22 -08:00
Matt Van Horn	cc774d5e69	feat(release): v2.9.1 - auto-save research to ~/Documents/Last30Days/ Sync from public repo. Every run now saves the complete briefing as a topic-named .md file to ~/Documents/Last30Days/. Credit @devin_explores. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 19:50:43 -08:00
Matt Van Horn	8cbbe87c3e	docs: add v2.9.1 auto-save note to README Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 19:27:18 -08:00
Matt Van Horn	49d993b162	feat(release): v2.9.1 - auto-save research to ~/Documents/Last30Days/ Bump version to 2.9.1, update changelog and release notes. Credit @devin_explores for inspiring the feature. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 19:15:42 -08:00
Matt Van Horn	6afc094bb1	Merge pull request #51 from mvanhorn/feat/auto-save-documents feat(skill): auto-save research results to ~/Documents/Last30Days/	2026-03-05 19:12:56 -08:00
Matt Van Horn	f6a1769e35	feat(skill): auto-save research results to ~/Documents/Last30Days/ Every run now automatically saves the complete briefing (synthesis, stats, follow-up suggestions) as a topic-named .md file in the user's Documents folder. Agent mode also saves. No Python script changes - this is purely a SKILL.md instruction addition. Inspired by @devin_explores manually saving results to build a personal research library. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 19:06:27 -08:00
Matt Van Horn	4d35b53eab	docs: v2.9.0 release — ScrapeCreators Reddit default, top comments, smart discovery Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 18:03:40 -08:00
Matt Van Horn	2247800003	chore: clean up Reddit log prefix, mark plan tasks complete	2026-03-05 18:01:24 -08:00
Matt Van Horn	7048fe7b83	feat(reddit): elevate top comments, improve subreddit discovery, default to ScrapeCreators Three improvements from beta testing: 1. Top comments: 10% scoring weight for comment quality, 💬 top comment rendered prominently in compact/full output, increased insight limits 2. Subreddit discovery: relevance-weighted scoring with topic word matching, utility sub penalties (UTILITY_SUBS blocklist), engagement bonus 3. Default method: SKILL.md primaryEnv → SCRAPECREATORS_API_KEY, web-only banner recommends SC first, security section updated Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 17:29:18 -08:00
Matt Van Horn	30b973f62e	docs: add Reddit ScrapeCreators v2 improvements plan Three focused improvements based on 5 full-pipeline beta tests: 1. Elevate top Reddit comments in scoring and rendering 2. Improve subreddit discovery heuristic for ambiguous queries 3. Make ScrapeCreators the default recommended Reddit method Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 17:24:41 -08:00
Matt Van Horn	09b09946c0	feat: replace OpenAI Reddit search with ScrapeCreators API - New scripts/lib/reddit.py: multi-query expansion, global search, subreddit discovery, targeted subreddit search, comment enrichment - 68 results in 17s vs ~15 results in 60-90s (OpenAI) - Cost: ~$0.02/search vs $0.03-0.10 (15-50x cheaper) - Real engagement data (score, comments, dates) from API - No more 429 rate limits on comment enrichment - Falls back to OpenAI if SCRAPECREATORS_API_KEY missing - Registered as last30daysbeta for parallel local testing Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-05 15:55:02 -08:00