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.

Security Note

The HTML report (generate_report.py) embeds your session data directly — file paths, shell commands, project names, and tool usage patterns. This is safe for personal use on your own machine, but do not share the generated HTML file with others or host it publicly. The terminal-based scripts (analyze.py, tool_analysis.py) only print aggregate statistics and are safe to share.

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.