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>
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>
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>
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>
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>
- 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>
Previously Reddit was returning ~60% old content (some from 2022).
This commit adds multiple layers of date enforcement:
- Reddit prompt: Explicit from_date/to_date with "fewer results > older results"
- Hard filter: filter_by_date_range() in normalize.py excludes old content
- WebSearch Date Detective: Extracts dates from URLs (/2026/01/24/) and
snippets ("January 24, 2026", "3 days ago")
- WebSearch scoring: +10 bonus for verified dates, -20 penalty for unknown
The skill now guarantees only content from the last 30 days.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add Claude's built-in WebSearch tool as a third research source for
/last30days. This enables the skill to work out of the box with zero
API keys while preserving Reddit/X as the primary sources.
Key changes:
- Add WebSearchItem schema for web results (no engagement metrics)
- Add score_websearch_items() with 55/45 relevance/recency weighting
- Apply -15pt source penalty so WebSearch ranks below Reddit/X
- Add --include-web CLI flag to opt-in to WebSearch
- Return 'web' mode when no API keys configured (zero-config)
- Update render.py with [WEB] source label formatting
When WebSearch is enabled, the script outputs instructions for Claude
to use its built-in WebSearch tool, then synthesize results together.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Research topics across Reddit + X from the last 30 days using
OpenAI and xAI APIs. Features:
- Auto model selection (GPT-5.x, Grok-3)
- Popularity-aware scoring (relevance + recency + engagement)
- Reddit thread enrichment with real metrics
- Near-duplicate detection
- Multiple emit modes (compact, json, context, path)
- 24h caching with --refresh bypass
- NUX for API key setup
- 87 passing unit tests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Matt Van Horn