tool-time: Tool Usage Analysis (Codex CLI)

You are analyzing 7 days of tool usage data for the current project. Your job is to find problems and offer to fix them — not narrate numbers.

Codex CLI has no hooks, so data comes from transcript parsing.

Data

1. Run python3 /root/projects/tool-time/backfill.py to parse recent transcripts
2. Run python3 /root/projects/tool-time/summarize.py to refresh stats
3. Read ~/.claude/tool-time/stats.json

The file contains:

• total_events: total tool calls in the period
• tools: per-tool {calls, errors, rejections} — rejections are user denials, errors are tool failures
• edit_without_read_count: how many times Edit was called on a file not previously Read in that session

Analysis

Look for these signals (not an exhaustive list — use your judgment):

• Tools with error rates above ~10% — what's failing and why?
• Tools with high rejection rates — the agent is doing something the user doesn't want
• Edit-without-read > 0 — the agent is editing files it hasn't read, leading to blind edits
• Bash dominance (>50% of calls) — may indicate the agent should use Read, Edit, Grep instead
• Low tool diversity — are available tools being underutilized?

Then read the project's CLAUDE.md (if it exists):

• Does it already address the problems you found? If so, the rules aren't working.
• Is it missing guidance that would prevent the patterns you see?

Optionally check AGENTS.md for similar gaps.

Output

Present findings as a short bulleted list. For each finding:

• What the data shows (concrete numbers)
• Why it matters
• What to do about it

Then offer to apply fixes. Use Edit to update CLAUDE.md or AGENTS.md directly, with user approval. Don't just suggest — propose the exact text.

If the data looks healthy, say so briefly and stop. Don't invent problems.

Community Comparison (if enabled)

Check ~/.claude/tool-time/config.json — if community_sharing is true, also compare local stats to community baselines:

1. Fetch https://tool-time-api.mistakeknot.workers.dev/v1/api/stats
2. Compare local error rates to community averages
3. Flag tools where local error rate is >2x the community average
4. Note tools the community uses heavily that the user doesn't use at all

Skill Recommendations

After analysis, suggest relevant skills from the playbooks.com directory:

1. Detect the project's primary language by checking for:

   • package.json or tsconfig.json → TypeScript/JavaScript
   • pyproject.toml, setup.py, or requirements.txt → Python
   • Gemfile or *.gemspec → Ruby
   • go.mod → Go
   • Cargo.toml → Rust
   • *.swift or Package.swift → Swift

2. Build 1-2 search queries combining the language with patterns from the data:

   • If error rates are high → search for "testing" or "debugging" skills
   • If no test runner usage detected → search for testing skills in that language
   • If heavy web/API usage → search for relevant API/fetch skills
   • Default: search for the primary language name

3. Fetch from the API: https://playbooks.com/api/skills?search=<query>&limit=5

4. Filter results to only show skills that are relevant to the project (use judgment — skip generic or unrelated results).

5. Present as a short list:

   • Skill name and one-line description
   • Install: playbooks.com/skills/<repoOwner>/<repoName>/<skillSlug>
   • Why it's relevant based on the user's data

If no relevant skills are found, skip this section entirely. Don't force recommendations.

Data Deletion

If the user asks to delete their community data (triggers: "delete my data", "remove my data", "forget me", "GDPR delete"):

1. Read ~/.claude/tool-time/config.json to get the submission_token
2. If no token exists, tell the user they have no community data to delete
3. Show the user their token and confirm they want to proceed
4. Run: curl -s -X DELETE "https://tool-time-api.mistakeknot.workers.dev/v1/api/user/<token>"
5. Report the result
6. Offer to also set community_sharing to false in config.json to stop future uploads