- name
- memory-forge
- description
- AI conversation efficiency analyzer. Analyze Claude/ChatGPT/Cursor conversation history for KPI stats, cost tracking, topic distribution, and efficiency insights. Use when: user asks to analyze their AI conversation history, track API costs, see topic distribution, review conversation efficiency, or get usage insights. NOT for: code review, debugging, or general productivity tips.
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
python3 ~/memory-forge/skill/scripts/analyze.py --weeklyThis 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 statisticsprojects: Per-project breakdown (sessions, cost, turns)models: Per-model usage statscost_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:
- Cost Efficiency: Which projects have unusually high per-session costs? Optimization opportunities?
- Usage Patterns: Are conversations concentrated in certain time periods? Any "high frequency, low efficiency" patterns?
- Topic Distribution: Over-concentration on a few projects? Neglected areas?
- 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
# 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 --weeklyImportant 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