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