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Skillsskill-retro

skill-retro

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Updated 10-15 23:45

About this skill

Skill Retro

Analyze how skills performed in the current session and apply improvements via skill-creator.

Process

Step 1: Preprocess Session

Run the preprocessing script to extract a clean transcript from the current session's JSONL log:

node ${CLAUDE_SKILL_DIR}/scripts/preprocess.mjs --cwd <current-working-directory>

Capture the JSON output. Key fields:

  • transcript_file — path to the temp file containing the full transcript (at ~/.claude/tmp/skill-retro-<session-id>.json)
  • stats.skill_invocations — count of skills used

The script writes the full transcript to a temp file to avoid flooding the main thread's context window. Only the summary (stats + file path) appears in stdout.

If the script fails (e.g., no session files found), report the error and stop.

If skill_invocations is 0, tell the user: "No skills were invoked in this session — nothing to analyze." and stop.

Step 2: Analyze (Sub-Agent)

Spawn an analysis sub-agent (using the Agent tool, model: sonnet):

  • Load the prompt from ${CLAUDE_SKILL_DIR}/references/analysis-prompt.md
  • Tell the agent to read the transcript file at the path from transcript_file in Step 1's output
  • The agent should read the SKILL.md file for each invoked skill to compare intended vs. actual behavior. Skill paths are discoverable from the transcript's system-reminder blocks (which list installed skill base directories) or from [SKILL INVOCATION] markers combined with installed plugin cache paths.
  • The agent returns a JSON object with findings, well_executed, and severity_summary

Parse the agent's response into structured findings.

Once the analysis agent has finished, delete the temp file created in Step 1:

rm <transcript_file>

If the agent returns 0 findings, tell the user: "All skills performed well this session. No improvements identified." Show the well_executed list and stop.

Step 3: Interpret and Recommend

Don't just present raw findings — interpret them. For each finding, add your own reasoning:

  1. What the fix looks like — Is it a one-line description tweak? A new section in SKILL.md? A script rewrite? Be specific about what changes would actually be made.
  2. Effort estimate — Quick fix (description/wording change), moderate (new section or error handling), or high-lift (architectural change, script rewrite, new scripts).
  3. Recommendation — Whether to action it now, defer it, or skip it entirely. Explain why.

Group findings by skill and present with your interpretation:

## Skill Performance Report

### scheduler:manage (3 findings)

**1. [high] gap_coverage — pause command doesn't verify unload succeeded**
The `_launchctl_unload` helper silently swallows errors, so the registry can say "paused" while the job is still running.
- **Fix:** Add return-code checking to `_launchctl_unload` in the manage script, and add a post-unload verification step in SKILL.md's Pause operation.
- **Effort:** Moderate — script change + SKILL.md update
- **Recommend: Action** — This caused real user confusion in this session.

**2. [medium] gap_coverage — No error recovery entry for "task still runs after pausing"**
The assistant had to improvise the entire investigation.
- **Fix:** Add a new row to the Error Recovery table in SKILL.md.
- **Effort:** Quick — one table row addition
- **Recommend: Action** — Low effort, high value.

**3. [low] execution_quality — list command shows registry status without cross-checking launchd**
Would require the list command to shell out to `launchctl list` and reconcile.
- **Fix:** Add launchd state reconciliation to the list operation in the manage script.
- **Effort:** High-lift — requires new script logic and testing across platforms.
- **Recommend: Defer** — Nice-to-have but significant work. Not worth addressing now.

### Well Executed
- list presented clean, accurate registry data
- Assistant correctly went outside the skill to diagnose the real issue

---
I recommend actioning findings 1 and 2. Finding 3 is high-lift and better deferred.

Proceed with these? (y/adjust/none)

Key principles:

  • Lead with your recommendation — don't make the user figure out what's worth doing
  • Be honest about high-lift items — don't propose changes that would require major rewrites unless they're clearly worth it
  • Group related findings that would be addressed together
  • The user can adjust your recommendation (add/remove items) or accept it

Wait for user confirmation. If "none", show summary and stop.

Step 4: Resolve Source Locations

For each skill with selected findings, determine where to edit:

Resolution order:

  1. Project-level skill — path contains .claude/skills/ relative to a project → Candidate: edit in place (user owns it)

  2. User-level skill — path is under ~/.claude/skills/ → Candidate: edit in place (user owns it)

  3. Installed plugin — path is under ~/.claude/plugins/cache/ → Try to trace to source: a. Extract plugin name from the path b. Check if cwd is a marketplace project containing that plugin's source (look for <plugin-name>/skills/<skill-name>/SKILL.md) c. If not found, ask user: "Where is the source code for the <plugin-name> plugin? Provide a path, or type 'installed' to edit the installed copy." → If "installed": warn that changes will be overwritten on plugin update, proceed only if user confirms → If user provides a path: verify it exists and contains the skill

  4. Current project contains source — cwd has ./<plugin-name>/skills/<skill-name>/SKILL.md → Candidate: edit in place

For each resolved path, confirm with the user before proceeding:

I'll make changes to <skill-name> at:
→ /path/to/resolved/source/SKILL.md

Is this the right location? (y/n or provide correct path)

Do NOT proceed to implementation until every path is confirmed.

Step 5: Implement Improvements (Parallel Sub-Agents)

For each affected skill (with confirmed source path), spawn an implementation sub-agent:

  • One agent per skill — can run in parallel since they edit different files

  • Agent prompt:

    "You are improving a Claude Code skill based on performance analysis findings.

    Skill: <skill-name> Source path: <confirmed-path-to-skill-directory> SKILL.md location: <confirmed-path>/SKILL.md

    Findings to address: <list all selected findings for this skill with full observation, evidence, and proposed_improvement>

    Instructions:

    1. Read the current SKILL.md at the source path
    2. Invoke the skill-creator:skill-creator skill
    3. When skill-creator asks what you want to do, explain you are improving an existing skill based on session analysis
    4. Provide the findings as the basis for changes
    5. Follow skill-creator's process to apply the improvements
    6. After changes are made, report what was modified"

Step 6: Summary

After all implementation agents complete, present a summary:

## Skill Retro Complete

### Changes Applied
| Skill | Source Path | Changes |
|-------|------------|---------|
| scheduler:manage | /path/to/SKILL.md | Added return-code checking to _launchctl_unload, post-unload verification step |
| scheduler:manage | /path/to/SKILL.md | Added "task still runs after pausing" to Error Recovery table |

### Deferred
- scheduler:manage #3 (launchd state reconciliation) — high-lift, deferred

### Well Executed
- list operation presented clean, accurate registry data

Important Notes

  • This skill is designed to run late in sessions when context may be full. All analysis and implementation happens in sub-agents to preserve main thread context.
  • The preprocessing script writes the full transcript to ~/.claude/tmp/skill-retro-*.json and only outputs a summary to stdout. Clean up the temp file after the analysis sub-agent has finished reading it.
  • The preprocessing script has zero dependencies beyond Node.js.
  • Never edit a skill without user confirmation of the source path.
  • When invoking skill-creator in implementation agents, let it guide the process — don't bypass its workflow.