analyze-sessions

Analyze recorded session data from ALL Claude Code projects to identify patterns and automation opportunities.

Arguments

• •--project <name> — Analyze only sessions from the named project (matches the project field, which is the basename of the working directory)
• •--targets-only — Exclude toolkit sessions; only analyze target projects

Data Location

Session logs are stored centrally in ~/.claude/logs/sessions.jsonl in JSONL format (one compact JSON object per line).

Each entry contains:

{"session_id":"...","timestamp":"2026-01-23T12:00:00Z","project":"project-name","cwd":"/path/to/project","transcript_path":"/path/to/transcript"}

The session-end-logger hook is installed at the user level (~/.claude/hooks/session-end-logger.sh) so it captures sessions from every project.

Analysis Process

Copy this checklist and track progress:

Analyze Sessions Progress:
- [ ] Step 1: Read session logs
- [ ] Step 2: Calculate statistics (grouped by project)
- [ ] Step 3: Identify target projects
- [ ] Step 4: Analyze transcripts
- [ ] Step 5: Find automation opportunities
- [ ] Step 6: Cross-project insights
- [ ] Step 7: Generate report

1. Read Session Logs

Read ~/.claude/logs/sessions.jsonl and parse all entries.

If --project <name> was given, filter to entries where project matches. If --targets-only was given, identify toolkit projects (see step 3) and exclude them.

If the file doesn't exist or is empty:

NO SESSION DATA
===============
No sessions have been logged yet.

Session logging is installed at the user level (~/.claude/hooks/session-end-logger.sh).
Run a few Claude Code sessions in any project, then run /analyze-sessions again.

If the hook is not installed, run /install-hooks and select "session-end-logger".

2. Calculate Statistics (Grouped by Project)

SESSION ANALYSIS
================

Total sessions: {N}
Date range: {earliest} to {latest}
Unique projects: {count}

Sessions per project:
- {project1}: {count} sessions ({first_date} – {last_date})
- {project2}: {count} sessions ({first_date} – {last_date})

3. Identify Target Projects

Scan ~/Projects/ for directories containing .claude/toolkit-version.json. These are toolkit target projects. All other projects are either the toolkit itself or standalone.

Project Classification:
  Toolkit: {toolkit project name}
  Target projects: {list of target project names}
  Other: {list of other project names}

4. Analyze Transcripts (If Available)

For each session with an accessible transcript_path:

1. •Read the transcript file
2. •Look for these patterns:

Manual Interventions:

• •AskUserQuestion tool calls — Questions asked during sessions
• •Text containing "manually", "by hand", "human must"
• •Repeated similar questions across sessions

Common Blockers:

• •Text containing "BLOCKED", "blocked", "waiting for"
• •Error patterns that recur
• •Missing credentials/env vars

Tool Usage Patterns:

• •Count of each tool type used
• •Sequences of tools that repeat

Workflow Friction:

• •"cd" into directories (wrong directory issues)
• •Repeated file lookups (missing context)
• •Re-reading same files multiple times

5. Identify Automation Opportunities

Based on patterns found, identify:

High Impact (Repeated across 3+ sessions):

• •Manual steps that could be automated with curl/Bash
• •Questions that could be pre-answered in AGENTS.md
• •Browser checks that could use Playwright

Medium Impact (Repeated 2 times):

• •Verification steps that could be codified
• •Setup steps that could be scripted

Low Impact (Single occurrence but notable):

• •One-off manual interventions that might recur

6. Cross-Project Insights

Compare patterns across projects:

CROSS-PROJECT COMPARISON
========================

| Project | Sessions | Avg Questions/Session | Top Blocker | Autonomy Level |
|---------|----------|-----------------------|-------------|----------------|
| {proj}  | {N}      | {avg}                 | {blocker}   | {High/Med/Low} |

Autonomy Level = High (0-1 questions/session), Med (2-3), Low (4+)

Identify:

• •Which projects need more AGENTS.md guidance (high question rate)
• •Which projects have more context overflows (re-reading patterns)
• •Common patterns that appear in multiple projects (shared automation opportunities)
• •Skills from the toolkit that are underused in target projects

6b. Review Findings Before Writing

Before writing the report file, output a summary of key findings to the user:

SESSION ANALYSIS PREVIEW
========================
Sessions analyzed: {N}
Top patterns found: {count}

Key findings:
1. {finding 1}
2. {finding 2}
3. {finding 3}

Writing full report to .claude/logs/ANALYSIS_REPORT.md...

This gives the user visibility before the file is written.

7. Generate Report

Write analysis to .claude/logs/ANALYSIS_REPORT.md (in the toolkit).

See references/output-structure.md for the full report template including summary, per-project breakdown, cross-project insights, automation opportunities, recommended actions, and raw statistics tables.

8. Output Summary

After writing the report:

ANALYSIS COMPLETE
=================

Analyzed: {N} sessions across {M} projects
Report: .claude/logs/ANALYSIS_REPORT.md

Key Findings:
- {top finding 1}
- {top finding 2}
- {top finding 3}

Cross-Project:
- {cross-project insight 1}
- {cross-project insight 2}

Recommended next steps:
1. Review .claude/logs/ANALYSIS_REPORT.md for full details
2. Consider creating skills for high-priority patterns
3. Update AGENTS.md with guidance for common questions

Error Handling

Limitations

• •Transcript files may not be accessible (permissions, deleted)
• •Analysis quality depends on session data available
• •Some patterns require human judgment to identify
• •Cross-project analysis requires the user-level hook to be installed