Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Extracts and analyzes Cursor IDE chat history to identify key discoveries, obstacles, and solutions, saving findings to the journal.
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.
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
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
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
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
Cursor IDE installed with chat history stored locally SQLite3 available (usually pre-installed on macOS) OpenClaw journal directory writable
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
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Largest current source with strong distribution and engagement signals.