OpenAI's web_search doesn't reliably find recent Reddit content.
Strategy change: request MORE threads and let server-side date
filtering handle it.
Changes:
- Increased depth config (20-30 → 30-50 default)
- Simplified prompt to search broadly and include all matches
- Let server-side date filter remove old content
- Removed aggressive pre-filtering that was limiting results
Tradeoff: Some topics have more X coverage than Reddit due to
web_search limitations. This is acceptable.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The model was finding old threads (Nov-Dec 2025) that got filtered out,
leaving 0 results even for popular topics like "nano banana prompts".
Changes:
- Add explicit date filters: "after:{from_date}" and "2026"/"January 2026"
- Emphasize RECENT content is critical, old threads are filtered anyway
- Tell model to verify dates before including threads
Before: 0-2 Reddit threads for "nano banana prompting practices"
After: 16 Reddit threads from January 2026
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add EXTRACT CORE KEYWORDS step to search for main subject, not full phrase
("killer features of clawdbot" → search "clawdbot")
- Remove hardcoded subreddit list that biased results toward design/dev subs
- Simplify search strategies and URL validation rules
- Make prompt more concise - GPT-5.2 responds better to clearer instructions
Before: 0 threads found for "killer features of clawdbot"
After: 7 threads found including r/LocalLLaMA (43pts), r/selfhosted (26pts)
Co-Authored-By: Claude Opus 4.5 <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 LAST30DAYS_DEBUG env var / --debug flag
- Log HTTP requests, responses, and errors
- Show API error details when debug enabled
- Helps diagnose API failures
Usage: python3 last30days.py "topic" --debug
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update prompt to search for "site:reddit.com/r/ {topic}"
- Add explicit filtering for /r/*/comments/* URLs
- Block garbage domains (developers.reddit.com, business.reddit.com)
- Make prompt lenient for older threads when recent ones scarce
- Require returning threads found, never empty items
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
1. Output order: What I learned → TARGET TOOL → KEY PATTERNS → Research Complete → Share vision
2. Fixed Reddit returning empty results - OpenAI was finding URLs but not extracting content
- Updated prompt to explicitly require extraction from search results
- Added "MUST include threads" instruction to prevent empty items
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- SKILL.md: Move "What I learned" BEFORE "Research Complete" stats
- Add error tracking to Report schema (reddit_error, x_error fields)
- Wrap OpenAI API calls in try/catch with clear error messages
- Show explicit error or "no results" messages in compact output
- Fix false positive error detection for null error fields
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- quick: 8-12 sources each, faster response
- default: 20-30 sources each (unchanged behavior)
- deep: 50-70 Reddit, 40-60 X for comprehensive research
Adjusts API timeouts based on depth. Cache keys include depth
so different depths are cached separately.
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