Skill

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 --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

# 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