sderosiaux/claude-warehouse/report

Claude Code and agent skill by sderosiaux/claude-warehouse

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Generate an analytics report on your AI-assisted development habits. Use when the user asks about their productivity, workflow patterns, session efficiency, or wants to understand how they use Claude Code. Covers token economics, tool usage patterns, session shapes, project maturity, and actionable improvement suggestions.

Install

npx vskill@latest install sderosiaux/claude-warehouse/report

SKILL.md

Report — AI Development Analytics

Generate a comprehensive analytics report on the user's Claude Code usage patterns.

Run ALL of the following queries and synthesize the results into a clear, actionable report.

Schema reference (for adapting queries)

When the user specifies a date range, add WHERE clauses. Use these join paths:

sessions.session_id  ←→  messages.session_id
sessions.session_id  ←→  tool_calls.session_id
messages.uuid        ←→  tool_calls.message_uuid   (NOT message_id)

Key column differences between tables:

  • sessions: created_at (TIMESTAMP), modified_at
  • messages: timestamp (TIMESTAMP) — NO created_at column
  • tool_calls: timestamp (TIMESTAMP) — NO created_at column
  • Ambiguous columns: tool_name exists in BOTH messages and tool_calls — always qualify with table alias

To filter tool_calls or messages by date, JOIN through sessions:

sql
SELECT tc.tool_name, COUNT(*) FROM tool_calls tc JOIN sessions s ON tc.session_id = s.session_id WHERE s.created_at >= '...' GROUP BY 1
SELECT model, COUNT(*) FROM messages m JOIN sessions s ON m.session_id = s.session_id WHERE s.created_at >= '...' GROUP BY 1

1. Overview (last 30 days vs previous 30 days)

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT 'last_30d' as period, COUNT(*) sessions, SUM(message_count) messages, SUM(total_input_tokens + total_output_tokens) total_tokens, ROUND(AVG(message_count), 1) avg_msgs, COUNT(DISTINCT project_name) projects FROM sessions WHERE created_at >= current_date - INTERVAL '30 days' UNION ALL SELECT 'prev_30d', COUNT(*), SUM(message_count), SUM(total_input_tokens + total_output_tokens), ROUND(AVG(message_count), 1), COUNT(DISTINCT project_name) FROM sessions WHERE created_at >= current_date - INTERVAL '60 days' AND created_at < current_date - INTERVAL '30 days'"

2. Token costs per project (last 30 days, with dollar estimates)

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT project_name, COUNT(*) sessions, SUM(total_input_tokens) input_tok, SUM(total_output_tokens) output_tok, SUM(total_cache_read) cache_read, ROUND(SUM(total_cache_read)::FLOAT / NULLIF(SUM(total_input_tokens + total_cache_read), 0) * 100, 1) as cache_hit_pct, ROUND(SUM(total_input_tokens) * 3.0 / 1000000 + SUM(total_output_tokens) * 15.0 / 1000000, 2) as est_cost_usd FROM sessions WHERE created_at >= current_date - INTERVAL '30 days' GROUP BY 1 ORDER BY est_cost_usd DESC LIMIT 15"

3. Session efficiency (short vs long, abandoned sessions)

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT CASE WHEN message_count <= 3 THEN 'abandoned (1-3 msgs)' WHEN message_count <= 10 THEN 'short (4-10 msgs)' WHEN message_count <= 30 THEN 'medium (11-30 msgs)' ELSE 'long (30+ msgs)' END as bucket, COUNT(*) sessions, ROUND(AVG(total_input_tokens + total_output_tokens)) avg_tokens, ROUND(AVG(message_count), 1) avg_msgs FROM sessions WHERE created_at >= current_date - INTERVAL '30 days' GROUP BY 1 ORDER BY MIN(message_count)"

4. Tool usage distribution (what Claude spends time doing)

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT tool_name, COUNT(*) calls, ROUND(COUNT(*)::FLOAT / SUM(COUNT(*)) OVER () * 100, 1) as pct FROM tool_calls WHERE timestamp >= current_date - INTERVAL '30 days' GROUP BY 1 ORDER BY 2 DESC LIMIT 15"

5. Edit/Read ratio trend (AI collaboration maturity)

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT DATE_TRUNC('week', timestamp)::DATE as week, COUNT(*) FILTER (WHERE tool_name IN ('Edit', 'MultiEdit', 'Write')) as writes, COUNT(*) FILTER (WHERE tool_name = 'Read') as reads, ROUND(COUNT(*) FILTER (WHERE tool_name IN ('Edit', 'MultiEdit', 'Write'))::FLOAT / NULLIF(COUNT(*) FILTER (WHERE tool_name = 'Read'), 0), 2) as write_read_ratio FROM tool_calls GROUP BY 1 ORDER BY 1 DESC LIMIT 8"

6. First prompt quality signal

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT CASE WHEN LENGTH(first_prompt) < 50 THEN 'short (<50 chars)' WHEN LENGTH(first_prompt) < 200 THEN 'medium (50-200)' ELSE 'detailed (200+)' END as prompt_length, COUNT(*) sessions, ROUND(AVG(message_count), 1) avg_msgs, ROUND(AVG(total_input_tokens + total_output_tokens)) avg_tokens FROM sessions WHERE created_at >= current_date - INTERVAL '30 days' AND first_prompt IS NOT NULL GROUP BY 1 ORDER BY MIN(LENGTH(first_prompt))"

7. Daily activity heatmap

bash
${CLAUDE_PLUGIN_ROOT}/scripts/query.py sql "SELECT created_at::DATE as day, COUNT(*) sessions, SUM(message_count) msgs FROM sessions WHERE created_at >= current_date - INTERVAL '14 days' GROUP BY 1 ORDER BY 1 DESC"

Report format

Present the results as a structured report with:

  1. Summary: Key numbers (sessions, tokens, estimated cost, active projects)
  2. Trends: Compare to previous period — what's changing?
  3. Efficiency: Session shape distribution, abandoned session rate, first-prompt quality correlation
  4. Tool patterns: What Claude spends time doing, write/read ratio trend
  5. Cost hotspots: Which projects burn the most tokens and why (low cache hits = poor project setup)
  6. Actionable suggestions: 3-5 specific things to improve based on the data:
  • High abandoned rate → prompts need more specificity
  • Low cache hits → add/improve CLAUDE.md files
  • Long sessions dominating → break tasks into smaller units
  • Read-heavy tool usage → improve project discoverability
  • Rising costs on one project → investigate complexity growth

Security

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  • Model reviewPassed

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Source

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