Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Communication & Collaboration
Chat History Analyzer
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
skill openclawclawhub Free
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
⬇ 0 downloads ★ 0 stars Unverified but indexed
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- Open the source page and confirm the package flow manually.
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Package facts
- Download mode
- Manual review
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, _meta.json, scripts/analyze_logs.py, scripts/chat_history_analyzer.py, scripts/combined_analysis.py
Validation
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.5
Provenance
- Publisher
- RuneweaverStudios
- Source page
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Documentation
Description
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
Chat History Analyzer | OpenClaw Skill
Extracts chat history from Cursor IDE's local SQLite databases, analyzes the last hour of conversations for key discoveries, obstacles, and solutions, and saves structured findings to the OpenClaw journal directory.
Usage
As a scheduled cron job to continuously track insights from chat history Manually to analyze recent chat activity To identify recurring patterns, problems, or solutions in your workflow # Combined log and chat history analysis (for cron jobs) python3 /Users/ghost/.openclaw/workspace/skills/chat-history-analyzer/scripts/analyze_logs.py # Analyze last hour of chat history only python3 /Users/ghost/.openclaw/workspace/skills/chat-history-analyzer/scripts/chat_history_analyzer.py # Analyze last 2 hours python3 /Users/ghost/.openclaw/workspace/skills/chat-history-analyzer/scripts/chat_history_analyzer.py --hours 2 # Output JSON format python3 /Users/ghost/.openclaw/workspace/skills/chat-history-analyzer/scripts/analyze_logs.py --json
What this skill does
Extracts chat history from Cursor's SQLite databases (global and workspace-specific) Analyzes the last hour of messages for patterns indicating discoveries, obstacles, and solutions Saves structured findings to /Users/ghost/.openclaw/journal/ as markdown files Runs automatically via cron job every hour
Integration as a Cron Job
This skill is designed to run hourly via OpenClaw cron. The analyze_logs.py script combines both log analysis and chat history analysis. Example Cron Job Configuration: { "payload": { "kind": "agentTurn", "message": "Run analyze_logs.py script to analyze the last hour of logs and Cursor chat history, saving findings to journal.", "model": "openrouter/google/gemini-2.5-flash", "thinking": "low", "timeoutSeconds": 180 }, "schedule": { "kind": "cron", "cron": "0 * * * *" }, "delivery": { "mode": "announce" }, "sessionTarget": "isolated", "name": "Chat History & Log Analysis" } Or run directly via shell script: # Add to crontab (crontab -e) # Run every hour at minute 0 0 * * * * /Users/ghost/.openclaw/workspace/skills/chat-history-analyzer/scripts/analyze_logs.py --json >> /Users/ghost/.openclaw/logs/analyze_logs.log 2>&1
Output Format
Findings are saved to /Users/ghost/.openclaw/journal/chat_analysis_YYYY-MM-DD_HHMMSS.md with sections for: Key Discoveries: Successful findings, realizations, and implementations Obstacles Encountered: Errors, failures, and blockers Solutions Found: Fixes, workarounds, and resolutions
Requirements
Cursor IDE installed with chat history stored locally SQLite3 available (usually pre-installed on macOS) OpenClaw journal directory writable
How it works
Connects to Cursor's SQLite databases at ~/Library/Application Support/Cursor/User/globalStorage/state.vscdb and workspace-specific databases Extracts messages from the last N hours (default: 1 hour) Analyzes message content using pattern matching for discoveries, obstacles, and solutions Saves structured markdown report to the journal directory
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Config
- SKILL.md
- README.md
- scripts/analyze_logs.py
- scripts/chat_history_analyzer.py
- scripts/combined_analysis.py
- _meta.json