AI Session Analysis

Overview

Analyze session data from local AI coding tools. Three bundled scripts extract raw data. You (the agent) read the output and form your own observations — there are no hardcoded rules or thresholds in the scripts.

Data Sources

Scripts read directly from these paths, no copying needed:

How to Run

# Text-based analysis (terminal output)
python3 ~/.claude/skills/ai-session-analysis/scripts/analyze.py
python3 ~/.claude/skills/ai-session-analysis/scripts/tool_analysis.py

# HTML report with charts (opens in browser)
python3 ~/.claude/skills/ai-session-analysis/scripts/generate_report.py [output.html]

Zero dependencies, Python 3 stdlib only. All scripts are pure data extractors — they present numbers, distributions, and timelines without any interpretation.

What Each Script Extracts

analyze.py — Overview:

• Per-tool: session count, message volume, tool distribution, project ranking, daily activity timeline, date range
• Cross-tool comparison table
• Recent 7-day activity: daily tool calls, active projects, tool mix, per-session timeline

tool_analysis.py — Deep dive:

• Shell command categories per tool (git, grep/find, npm/yarn, python, etc.)
• File operation counts (Read/Write/Edit) and file type distribution
• Project-level tool usage breakdown
• Codex: token usage, web search topics, errors
• Kimi: domain-specific tools beyond the standard set

generate_report.py — HTML report:

• Self-contained HTML file with embedded data and Chart.js visualizations
• Summary cards, daily activity timeline, tool distribution doughnut, project breakdown
• Shell command comparison across all three tools
• File operations, token usage, session timeline scatter plot
• Session duration distribution, hour-of-day activity heatmap
• Codex deep dive (patches, web searches, exit codes)
• Kimi domain tools

After Running

Read both outputs carefully. Then tell the user what you see. Consider:

• What are they actually doing? (exploring code vs building features vs debugging vs reviewing)
• Which projects are getting attention, which ones went quiet?
• Tool use patterns: are they leaning heavily on one tool? is the tool mix appropriate for their work?
• Cross-tool: did they try one tool then switch? what might that say?
• Recent vs historical: any shifts in behavior, intensity, or focus?
• Anything that looks like friction: lots of failed commands, retries, errors?

Do not apply fixed thresholds or rules ("if bash > 40% then warn"). Look at the whole picture and use judgment. If something stands out, mention it. If nothing does, say so.

Common Issues

• Claude Code shows few tools: Tools are in ~/.claude/projects/*.jsonl (assistant → content blocks with "type": "tool_use"), not in history.jsonl.
• Codex sessions empty: Check ~/.codex/sessions/2026/ subdirectories (organized by year/month/day).
• Kimi tool names missing: ToolCall entries are in wire.jsonl via payload.function.name.