- Updated intro to emphasize prompt research + general discovery
- Added "Best Rap Songs Right Now" example (pure research use case)
- Added "Legal Prompting" example (hallucination prevention patterns)
- Added "Suno AI Music" example with actual MP3 output
- Updated usage examples and tagline
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Shows the viral trend result:
- Original golden retriever with harness and pink tag
- ChatGPT transformation: same hair color, tongue out, harness, tag
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Shows the full workflow: research → synthesis → prompt generation
Includes the actual generated prompt for a 50-second launch video
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
If research says "use JSON prompts", the generated prompt MUST be JSON.
Previously the skill would learn about JSON format then ignore it and
write plain prose - defeating the purpose of the research.
- Add PROMPT FORMAT as required field to capture during research
- Add explicit anti-pattern warning about ignoring format
- Update quality checklist to verify format matches research
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Detect if running in real terminal vs captured output
- In TTY: show animated spinner with carriage returns
- In non-TTY: print simple static messages (no animation)
- Prevents output file from exploding with spinner frames
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>
- Wrap xAI search in try/except like Reddit
- Show error message but continue with Reddit results
- Parse function checks for API errors before processing
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Show "⚡ CACHED RESULTS (Xh old)" in compact output header
- Add "use --refresh for fresh data" hint
- Track from_cache and cache_age_hours in Report schema
- Update UI to show cache age in stderr message
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>
- New ui.py module with colored output and animations
- Animated spinner during Reddit/X searches
- Progress tracking for enrichment phase [1/N]
- Fun random status messages per phase
- Mini ASCII banner at start
- Completion summary with timing
Makes the research feel more alive while waiting!
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>
Previously asked "What tool?" before running research, which was
backwards for exploratory queries like "iOS design mockups".
Now:
- If tool is specified in query, use it
- If tool NOT specified, run research first
- Ask about target tool AFTER showing results (with smarter options based on what research found)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The Report.to_dict() serializes range as {from, to} but constructor
expects range_from/range_to. Added from_dict() classmethod to properly
deserialize cached data, reconstructing all nested objects (Engagement,
Comment, SubScores, RedditItem, XItem).
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The skill was finding correct sources (e.g., ClawdBot content) but Claude
was synthesizing based on its pre-existing knowledge (Claude Code skills)
instead of what the research actually said.
Added strong grounding instructions:
- CRITICAL warning to base synthesis on actual research, not pre-existing knowledge
- Anti-pattern example: don't conflate "clawdbot skills" with "Claude Code skills"
- Self-check reminder before displaying summary
- Updated "What I learned" template to emphasize traceability to sources
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>
Show proof of work first:
1. Research stats (upvotes, likes, sources)
2. Key patterns discovered (5 bullet points)
3. "I'm now an expert in X"
4. "Share your vision..."
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>
- After stats summary, invite user to share what they want to create
- Wait for their response instead of auto-dumping generic prompts
- Write ONE tailored prompt based on their specific vision
- Only provide multiple options if they ask for more
- Stay in expert mode for follow-up requests
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Parse user intent for TOPIC and TARGET_TOOL upfront
- Ask follow-up if target tool unclear
- Show impressive stats summary after research (upvotes, likes, etc.)
- Primary output is now copy-paste-ready prompts for target tool
- Keep expert context for follow-up custom prompt requests
- Research is internalized, not dumped back at user
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