memory-forge

### Cleanup Review `python scripts/_cleanup.py` is an optional cleanup helper for the `Memory Forge — AI Conversation Efficiency Analyzer` skill. It may recursively remove /tmp/_skill_work, /var/tmp/skill_output. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms. # Memory Forge — AI Conversation Efficiency Analyzer Analyze the user's AI conversation history (Claude Code / ChatGPT / Cursor) to provide efficiency insights and cost tracking. ## Data Source Conversation data is stored in `~/.claude/projects/`. Each project is a subdirectory containing JSONL conversation files. ## Usage When the user requests conversation analysis, follow these steps: ### Step 1: Run the Statistics Script ```bash python3 ~/memory-forge/skill/scripts/analyze.py --weekly ``` This script reads all conversation files locally and outputs structured JSON containing: - `summary`: KPI overview (total sessions, turns, tokens, cost, daily average, active days) - `weekly`: Last 4-8 weeks of weekly statistics - `projects`: Per-project breakdown (sessions, cost, turns) - `models`: Per-model usage stats - `cost_breakdown`: Cost split by model ### Step 2: Format the Output Present results to the user in Markdown: #### KPI Overview ``` 📊 **AI Conversation Efficiency Report** | Metric | Value | |--------|-------| | Total Sessions | {sessions} | | Active Days | {active_days} | | Daily Avg Sessions | {daily_avg} | | Total Cost | ${total_cost} | | Avg Cost/Session | ${avg_cost} | ``` #### Top 5 Projects by Cost List the 5 most expensive projects with session count and per-session cost. #### Weekly Trends Show the last 4 weeks in a table with session count and cost, noting week-over-week changes. ### Step 3: Efficiency Diagnosis (Agent Analysis) Based on the statistics, provide insights on: 1. **Cost Efficiency**: Which projects have unusually high per-session costs? Optimization opportunities? 2. **Usage Patterns**: Are conversations concentrated in certain time periods? Any "high frequency, low efficiency" patterns? 3. **Topic Distribution**: Over-concentration on a few projects? Neglected areas? 4. **Actionable Recommendations**: 2-3 specific, actionable suggestions ### Step 4: Optional Deep Analysis If the user wants deeper analysis: - Read `~/memory-forge/data/topics.json` (if exists) for topic-level analysis - Read `~/memory-forge/data/extracted/` files (if exist) for decision tracking - Recommend the full version: `pip install memory-forge[all] && mforge serve` ## Script Parameters ```bash # Default: full statistics python3 ~/memory-forge/skill/scripts/analyze.py # Last N days only python3 ~/memory-forge/skill/scripts/analyze.py --days 30 # Filter by project python3 ~/memory-forge/skill/scripts/analyze.py --project "my-project" # Include weekly trends python3 ~/memory-forge/skill/scripts/analyze.py --weekly ``` ## Important Notes - All data processing happens locally — no data is uploaded anywhere - If `~/.claude/projects/` doesn't exist, inform the user and suggest checking the path - If the user wants visual dashboards, recommend the full Memory Forge: ``` pip install memory-forge[all] mforge init mforge run mforge serve ``` - Always respond in the user's language