- 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>
Add Instagram Reels as the 8th research source via ScrapeCreators API.
One API key (SCRAPECREATORS_API_KEY) now covers both TikTok and Instagram.
- Add scripts/lib/instagram.py: keyword search, transcript extraction,
relevance scoring, engagement metrics (views, likes, comments)
- Add InstagramItem to schema, normalization, scoring, dedup, rendering
- Add Instagram to orchestrator pipeline, watchlist, and UI spinners
- Update SKILL.md: stats template, citation priority, item format,
URL-to-name extraction rules, anti-Sources instruction
- Update README and CHANGELOG for v2.8
- Fix: Instagram/TikTok not running in --search= web-only path
- Fix: web stats line showing full URLs instead of domain names
- Replace APIFY_API_TOKEN with SCRAPECREATORS_API_KEY throughout
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Root cause of empty TikTok results: Apify required monthly subscription.
ScrapeCreators is PAYG with 100 free credits and no subscription.
Key fix: ScrapeCreators nests items under aweme_info wrapper
(search_item_list[].aweme_info.{fields}), which the previous
implementation missed, causing all fields to be empty.
Changes:
- Rewrite tiktok.py to use ScrapeCreators REST API
- Add aweme_info unwrapping for correct field extraction
- Add transcript fetching via /video/transcript endpoint
- Add SCRAPECREATORS_API_KEY to env.py config
- Update last30days.py to use env.get_tiktok_token()
- Delete apify_client_wrapper.py (no longer needed)
- Update tests for new date field format (create_time)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove double quotes around $ARGUMENTS in SKILL.md so bash word-splits
the expansion, and change argparse topic from nargs="?" to nargs="*"
so multi-word topics still work. Also document --store, --include-web,
--diagnose, and --timeout flags in the Options section.
Closes#36
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Only print debug log lines when running in an interactive terminal.
In Claude Code (non-TTY), the spinner system handles progress display,
so these raw log lines just add noise.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Pass logger=None to Apify .call() to prevent the SDK from streaming
raw actor run logs (status messages, crawler stats, warnings) that
drown out the clean spinner UI in Claude Code.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove quiet=True from YouTube and TikTok spinners so they display
the same colored emoji progress lines as Reddit and X in non-TTY mode.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When running in Claude Code, the assistant has a built-in WebSearch tool
that's free and higher quality than Parallel AI/Brave/OpenRouter. Adding
--no-native-web to the SKILL.md invocation defers web search to the
assistant, saving API credits. OpenClaw invocations don't pass this flag,
so they continue using native web backends.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The websearch module import was dropped when the tiktok import was added,
causing the script to crash during the rendering phase after all data
was successfully collected.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add TikTok search, scoring, and rendering using the Apify platform
(clockworks/tiktok-scraper actor). Users bring their own APIFY_API_TOKEN
($5/month free credits, no CC required). The shared apify_client_wrapper
module is designed for reuse by future Facebook/Instagram sources.
- New modules: tiktok.py (search + caption extraction), apify_client_wrapper.py
- Schema: TikTokItem dataclass, shares field on Engagement, Report.tiktok
- Pipeline: normalize → filter → score → sort → dedupe → cross-link → render
- Scoring: 0.50*log1p(views) + 0.30*log1p(likes) + 0.20*log1p(comments)
- SKILL.md bumped to v2.7 with TikTok stats, citations, and security docs
- 26 unit tests covering relevance, normalize, score, dedupe, render, round-trip
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Inspired by PR #26 (wkbaran), whose early work on HN/YouTube sources helped
shape what we built in v2.5. Cherry-picks the source-filtering concept as a
clean implementation against our existing architecture.
--search=SOURCES accepts comma-separated: reddit, x, hn, youtube, polymarket, web
Example: --search reddit,hn (run only Reddit + Hacker News)
Also:
- bird_x: add noise words (trending, viral, plugin, skills) + last-chance retry
- render: show xAI tip for reddit-only mode regardless of missing_keys value
- tests: new test_bird_x.py (5 tests)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Cherry-picked from PR #24 (el-analista). Adds trending/viral/plugin/skill/tool
noise words to _extract_core_subject, and a last-chance retry that falls back
to the longest non-noise token when 2-word retry also returns 0 results.
cache.py and render.py env overrides were already on main.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: fix YAML error in argument-hint
* add codex auth support to responses API
* Use gpt-5.1-codex-mini as default model for Codex auth
Add CODEX_FALLBACK_MODELS chain (gpt-5.1-codex-mini → gpt-5.2) for
Codex endpoint which doesn't support standard OpenAI models like
gpt-4o-mini. Adds model fallback retry on 400 errors in the Codex
search path. Also adds test_codex_auth.py with 22 unit tests covering
JWT decoding, auth resolution, SSE parsing, and payload building.
* Pass .env credentials to Bird Node subprocesses for X auth
On platforms without browser cookie access (e.g. WSL2), Bird's
vendored Node.js module cannot read AUTH_TOKEN/CT0 from Firefox
or Chrome cookie stores. The .env config file already supports
these values, but they were only loaded into the Python config
dict — never exported to the environment of Node subprocesses.
- Add AUTH_TOKEN/CT0 to env.py config key loading
- Add set_credentials()/\_subprocess_env() to bird_x.py to inject
credentials into the env dict passed to subprocess.run/Popen
- Call set_credentials() in main() before Bird auth detection
---------
Co-authored-by: Justin Williams <jblwilliams@gmail.com>
The Gamma API only searches event titles/slugs, missing markets where the
topic is an outcome (e.g., "Arizona" in NCAA Tournament Winner). This adds:
- All-word query expansion (not just first word): "Arizona Basketball" now
searches "Arizona", "Basketball" independently
- Tag-based domain expansion: extracts category tags (e.g., "NCAA") from
first-pass results and searches those as a second pass
- Neg-risk binary market synthesis: shows team names from market questions
instead of generic Yes/No outcomes
- Question shortening: extracts "Arizona" from "Will Arizona win the NCAA
Tournament?" for clean display
- Increased depth (3 pages) and result caps (15) for more coverage
Live results: "Arizona Basketball" now finds NCAA Tournament Winner (12%),
#1 Seed (88%), Big 12 Champion (69%). "Iran War" returns 15 markets (up
from 9) with no regression.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add CROSS to sync targets with sed-patched name/version/description
- Switch cp to rsync to handle identical file edge case on APFS
- CROSS SKILL.md gets last30daysCROSS skill root injected into search path
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- _compute_text_similarity() now checks outcome names with bidirectional
substring matching (0.85) and token overlap (0.7), not just event titles
- Collect outcomes from ALL active markets per event, filter to >1% price
- Reorder outcome_prices to surface topic-matching outcome before top-3 truncation
- Add SKILL.md "Prediction Markets" synthesis section with structural/long-term
market preference, domain examples, citation format, and narrative weaving
- Add Polymarket to citation priority list between HN and Web
- Update stats box template to show up to 5 market odds
- Fix render.py "vol24h" label to "volume"
- Add NCAA seed fixture event for outcome-only matching tests
- 82 polymarket tests pass (14 new)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Polymarket results now rank by text similarity, volume, liquidity, price
movement, and competitive score instead of API return position. Also fixes
pagination (DEPTH_CONFIG now controls page count, not a no-op limit param)
and caps results after re-ranking.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Search Polymarket's free Gamma API for relevant prediction markets on any
topic. Uses smart multi-query expansion to cast a wider net (e.g., "Arizona
Basketball" also searches "Arizona"), merges and dedupes by event ID, and
shows price movement context ("up 22.5% this week"). No API key required.
Also hides sources with zero results from the stats output (all sources).
54 new tests, all passing. Full pipeline integration with scoring, dedupe,
cross-source linking, and rendering.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The resolved handle dedup was wrong: if entity_extract found @thedorbrothers
(from @mentions in Phase 1 results), the resolved handle search was skipped
entirely. But entity-extracted handles are searched WITH topic keywords
(from:handle topic), while resolved handles need UNFILTERED search
(from:handle) to find posts that don't mention the topic string.
Example: Dor Brothers' viral tweet (5.5K likes) says "We made a $300M movie
starring @LoganPaul" - no mention of "dor brothers" anywhere. The topic-
filtered entity search missed it. The unfiltered resolved search finds it.
Before: 30 X posts, 161+ likes (entity search only)
After: 40 X posts, 5549+ likes (resolved handle adds viral tweet)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When a topic is a person/brand (e.g. "Dor Brothers", "Jason Calacanis"),
the agent now resolves their X handle via WebSearch before running the
script, then passes --x-handle to search their posts unfiltered (no
topic keywords required). This finds posts the entity made without
mentioning their own name.
- SKILL.md + OpenClaw variant: Step 0.5 handle resolution instructions
- last30days.py: --x-handle CLI arg, passed through to _run_supplemental()
- bird_x.search_handles(): topic is now Optional[str] for unfiltered mode
- schema.py: resolved_x_handle field on Report
- render.py: show resolved handle in stats output
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ran 15-way blinded comparison (5 topics x 3 versions). CROSS won all 5 topics
(4.74/5.0 avg vs HN 4.10, Base 3.73). Then improved CROSS further:
- dedupe.py: hybrid similarity (token+trigram Jaccard) at 0.40 threshold,
cross-source links went from 3 to 13 items across 5 topics
- render.py: [xref: HN5, HN4] -> [also on: HN, Reddit] for human-readable tags
- youtube_yt.py: SYNONYMS dict so "hip hop" matches "rap" (0.33 -> 0.71 score)
- SKILL.md: instruction #7 tells Claude to lead with cross-platform signals
Validation: improved CROSS scores 4.38/5.0 vs original 3.98 (+0.40), wins 4/5
topics. Biggest gains in specificity (+0.8) and format compliance (+1.0).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube videos now get real relevance scores based on token overlap
between the search query and video title (was hardcoded at 0.7).
Uses ratio overlap with stopword removal, floored at 0.1.
Cross-source linking annotates items that discuss the same story
across different platforms (e.g., Reddit + HN + X). Items get
bidirectional cross_refs displayed as [xref: R3, HN5] in compact
output so Claude can triangulate multi-platform coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Reddit and X are the star of the show. In Claude Code (non-TTY),
suppress ⏳ start messages for HN and YouTube so Reddit/X are the
first visible lines. HN/YouTube still show ✓ completion messages.
Also suppress [HN] debug logs in non-TTY to reduce output clutter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
HN was appearing before YouTube in the stats block, sort tiebreaker,
and source status. Now consistently: Reddit > X > YouTube > HN > Web.
Also restored emoji + box-drawing chars in test skill SKILL.md.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add HN search via free Algolia API (no key needed). Two-phase approach:
search for stories, then enrich top ones with comments. Integrated into
the full pipeline (normalize, score, dedupe, render) running in parallel
with Reddit/X/YouTube. Source priority: Reddit > X > HN > YouTube > Web.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The Reddit search uses OpenAI's Responses API with web_search, which
frequently returns 429 rate limit errors. The previous retry logic used
linear backoff (1s, 2s, 3s) which is too aggressive for OpenAI's rate
limiter (often needs 10-60s waits).
Changes:
- Increase max retries from 3 to 5
- Switch from linear to exponential backoff (2s, 5s, 9s, 17s, 33s)
- Parse and respect Retry-After header from OpenAI 429 responses
- Fall back to cheaper models (gpt-4.1 → gpt-4o) on 429s, not just
on 400/403 access errors
- Remove gpt-4o-mini from fallback chain — it doesn't support
web_search with the filters parameter
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add CHANGELOG.md (Keep a Changelog format) and release-notes.md
for the v2.1.0 GitHub Release. Also includes queued copy updates:
plugin.json version bump to 2.1.0, YouTube added to descriptions,
SKILL.md security/permissions section and ClawdBot metadata,
updated watchlist examples, and v2.1 launch post refinements.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When X posts return engagement: null, dict.get("engagement", {})
returns None (key exists with null value), causing AttributeError.
Use `or {}` idiom to coalesce None to empty dict.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
OpenAI Responses API web_search takes 60-90s but the generic
future timeout was killing it at 30s (quick) / 60s (default).
Added reddit_future key to TIMEOUT_PROFILES (60/90/120s).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube: Add youtube_future timeout key (60/90/120s for quick/default/deep)
separate from the shared future timeout. YouTube needs more time because
it does search + parallel transcript fetching. Previously, 20 videos +
5 transcripts exceeded the 60s budget and all results were discarded.
Reddit 429: Propagate rate-limit errors instead of swallowing them.
Enrichment now uses 10s timeout / 1 retry (was 30s / 3 retries).
On first 429, cancel remaining enrichment and skip Phase 2 Reddit.
Total time wasted on 429 drops from ~75s to ~12s.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The hard date filter in the main script was double-filtering YouTube
results. youtube_yt.py already applies a soft date filter that prefers
recent videos but keeps older ones when < 3 are within range (for
evergreen topics like "youtube thumbnails"). The hard filter then
removed all of them, resulting in 0 YouTube items.
YouTube content has a longer shelf life than tweets/posts, so the
soft filter's fallback behavior is correct.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The `from . import bird_x` in __init__.py caused ImportError for all
other module imports (youtube_yt, render, etc.) when bird_x had issues,
crashing the entire script in Codex. Making it a bare package marker
lets Python resolve `from lib import X` normally.
Added scripts/sync.sh to deploy to all three skill locations
(~/.claude, ~/.agents, ~/.codex) with import verification.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace CLI-style promo banners with casual, conversational NUX message
that teaches users to say "last30" + natural language. Remove stale Bird
CLI npm install references and dead prompt_bird_install methods.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Script was hanging indefinitely when API sources were slow or
unresponsive. Now enforces bounded execution:
- Global timeout watchdog (180s default, 90s --quick, 300s --deep)
- Per-source future.result() timeouts (60s/30s/90s by depth)
- Parallel Reddit enrichment capped at 15 items / 45s total
- Subprocess process-group isolation (os.setsid + killpg)
- atexit cleanup kills all tracked child processes
- --timeout=N flag for user override
Also fixes the UX gap where missing sources were silently skipped:
- Pre-flight diagnostic banner shows source status before research
- Source status footer in compact output shows used/skipped/why
- Actionable fix commands for each missing source
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove 2>&1 from SKILL.md so stderr doesn't pollute model input
- Run script in foreground (not background) with 5min timeout
- Add explicit YouTube synthesis instruction for Claude
- Remove --flat-playlist which broke date filtering (all dates were None)
- Move date filtering to Python with soft fallback for evergreen topics
- Keep 'tips', 'tutorial', 'review', 'guide' in YouTube search queries
- Increase yt-dlp timeout from 60s to 120s for full metadata fetch
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- _search_web() dispatches to Parallel AI / Brave / OpenRouter based on config
- Web results flow through full pipeline: normalize → score → dedupe
- --diagnose shows all source availability (API keys, Bird, YouTube, web backends)
- --store persists findings to SQLite via store.py for watchlist/briefing system
- run_research() now returns web_items alongside reddit/x/youtube
- web_needed flag only set when no native web backend is available
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add agents/openai.yaml for Codex skill discovery
- Make SKILL.md script path portable (repo, Claude, Codex, agents dirs)
- Platform-neutral output text ("assistant" instead of "Claude")
- Sandbox-friendly cache/output dirs with env var overrides and tempdir fallback
- Add Codex installation docs to README
Inspired by PR #24 (el-analista) and PR #5 (jblwilliams).
Zero impact on existing Claude Code behavior.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube search and transcript extraction runs automatically when yt-dlp
is installed. Searches for topic videos from the last N days, fetches
auto-generated transcripts for top results, and feeds them through the
same scoring pipeline (relevance + recency + engagement) as Reddit/X.
New files:
- youtube_yt.py: search, transcript extraction, VTT cleanup
Modified files:
- schema.py: YouTubeItem dataclass, updated Report
- normalize.py: normalize_youtube_items()
- score.py: YouTube engagement scoring (views-dominated)
- dedupe.py: YouTube deduplication
- render.py: YouTube section in compact output
- env.py: is_ytdlp_available() check
- ui.py: YouTube progress messages
- last30days.py: _search_youtube(), parallel execution with Reddit/X
- SKILL.md: YouTube in stats box, citation priority
- README.md: YouTube docs, yt-dlp requirement, Peter shoutout
Inspired by Peter Steinberger's yt-dlp + summarize toolchain approach.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Vendor Bird's Twitter GraphQL search client directly into /last30days,
eliminating the dependency on `npm install -g @steipete/bird`. X search
now works out of the box with just Node.js 22+ and browser cookies.
- Add vendored bird-search.mjs wrapper (search-only subset of Bird v0.8.0)
- Vendor @steipete/sweet-cookie for browser cookie extraction
- Update bird_x.py to call vendored Node.js module instead of `bird` binary
- Update README.md and SKILL.md for v2.1 (remove Bird CLI install steps)
- Include Bird's MIT LICENSE in vendor directory
The fallback chain is: vendored search -> xAI API key -> web-only mode.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three bugs in _extract_core_subject():
1. Multi-word noise phrases ("what are", "how to") never matched
because code compared individual words against multi-word strings.
"what are people saying about DeepSeek R1" became "what are people
saying" — losing the entire topic.
2. Missing meta words — "prompt", "techniques", "tips" weren't
filtered (only "prompting" was). "vibe motion best prompt
techniques" kept 4 keywords instead of 2.
3. No retry on 0 results — Reddit retries with simplified queries
but X accepted 0 and moved on.
Fix: Two-phase extraction (strip multi-word prefixes/suffixes first,
then individual noise words), expanded noise set, max 3 words (was 4),
and automatic retry with first 2 words when Bird returns 0 results.
Before → After:
- "vibe motion best prompt techniques" → "vibe motion" (was 4 words, 0 results)
- "what are people saying about DeepSeek R1" → "deepseek r1" (was "what are people saying")
- "nano banana pro prompts for gemini" → "nano banana pro" (was 4 words)
Tested: vibe motion (12 X posts, was 0), DeepSeek R1 (12 posts),
kanye west (12 posts, no regression).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add V2 to title
- Document Bird CLI installation (free X search)
- Add --days=N to options table
- Update Requirements to show Bird CLI as recommended free option
- Replace How It Works with two-phase search architecture
- Add What's New in V2 section with all new features
- Credit community contributors (@JosephOIbrahim, @levineam, @jonthebeef)
- Bump http.py USER_AGENT from 1.0 to 2.0
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
After the initial broad search (Phase 1), extract key entities from results
and run targeted secondary searches to surface content the broad pass missed:
- New entity_extract.py: parses @handles, #hashtags, subreddits from results
- bird_x.py: search_handles() does targeted from:handle searches via Bird CLI
- openai_reddit.py: search_subreddits() uses Reddit's free .json search endpoint
- last30days.py: Phase 2 orchestration runs after enrichment, merges + dedupes
Tested with "kanye west" (+9 Reddit, +1 X) and "claude code skills" (+6 Reddit, +1 X).
Phase 2 is skipped on --quick mode. Default caps at 3 handles/subs, deep at 5.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Stats: replace BAD/GOOD examples with strict fill-in-the-blank template
- Reddit: add subreddit-targeted fallback search, soften scoring penalties
(engagement -10→-3, date confidence -10→-5), add minimum result guarantee
- Citations: limit to 1 per insight, short format, no engagement metrics
- Summary: add bold topic headers template for structured paragraphs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1. Add _extract_core_subject() to strip noise words before X search
- X search is literal keyword matching, not semantic
- "best open claw usecases" → "open claw" (27 results vs 0)
2. Fix ISO date detection bug
- Was: if "T" in created_at (matched "Tue" in "Tue Feb 03...")
- Now: if created_at[10] == "T" (proper ISO separator check)
- Dates now parse correctly: (2026-02-03) instead of (date unknown)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
1. last30days.py: Check isinstance(dict) before .get() - Bird returns
list on success, dict on error
2. bird_x.py: Update field mappings for Bird's actual response format:
- author.username not user.screen_name
- createdAt not created_at (camelCase)
- likeCount, retweetCount, etc. (camelCase)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Bird CLI doesn't support --since as a CLI flag.
Use X's search syntax: 'topic since:YYYY-MM-DD'
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Matt Van Horn