| name | analyze-skills |
| description | Analyze ~/.claude session files to find unused agent skills and reduce context token waste. Scans subagent JSONL files for injected skills and cross-references with agent responses. |
| user-invocable | true |
| argument-hint | [project-filter] |
Analyze Agent Skill Usage
You are analyzing Claude Code session files to determine which agent skills are actually used vs wasted context tokens.
What This Does
Scans ~/.claude/projects/ for subagent JSONL files, detects which skills were injected into each agent, and checks whether the agent actually referenced that skill's content. Produces a report with recommendations to optimize agent configurations.
Step 1: Find Subagent Files
Find all subagent JSONL files for the target project:
find ~/.claude/projects/ -path "*/subagents/agent-*.jsonl" -type f 2>/dev/null
If $ARGUMENTS.project is provided, filter paths containing that project name.
If not provided, use the current working directory to determine the project.
Step 2: Read Agent Definitions
Read all agent files from .claude/agents/*.md in the current project. Extract:
- Agent name (from frontmatter
name:)
- Skills list (from frontmatter
skills:)
Step 3: Analyze Each Subagent File
For each subagent JSONL file:
3a. Identify the agent type
Look at the first user message — it usually contains the task description from the Task tool prompt. Cross-reference with the parent session if needed.
3b. Extract injected skills
Search the first 20 lines for <command-name>SKILL_NAME</command-name> markers. This tells you which skills were loaded into the agent's context.
3c. Check skill usage
For each injected skill, search the agent's assistant messages for distinctive keywords:
| Skill | Keywords to search for |
|---|
uiux | gray-950, terracotta, design system, bg-gray, border-gray |
tanstack-start | createServerFn, server function, TanStack Start, SSR |
typescript-rules | strict typing, Zod, z.object, z.infer, unknown, type guard |
react-rules | useQuery, useSuspenseQuery, queryOptions, named export, TanStack Query |
testing | vitest, describe, it, expect, vi.mock, happy-dom, testing-library |
playwright-cli | playwright, browser, e2e, spec.ts, page.goto |
sdlc | pipeline, SDLC, acceptance criteria |
A skill is "used" if ANY of its keywords appear in the agent's assistant responses.
Step 4: Build Usage Matrix
Aggregate results into a table:
Agent Type | Skill | Injected | Used | Usage % | Recommendation
-----------|-------|----------|------|---------|---------------
architect | tanstack-start | 15 | 8 | 53% | KEEP
...
Step 5: Generate Report
Output a structured report with:
5a. Per-Agent Summary
For each agent type, show:
- Current skills (from
.claude/agents/<name>.md)
- Usage rates from session data
- Recommended changes (KEEP / REMOVE / ADD)
5b. Token Savings Estimate
For each removed skill, estimate context tokens saved:
- Read the skill's SKILL.md file
- Count approximate tokens (chars / 4)
- Multiply by number of agent invocations
5c. Actionable Changes
List the exact frontmatter changes needed for each agent file. Example:
skills:
- tanstack-start
- typescript-rules
- react-rules
- uiux
skills:
- tanstack-start
- typescript-rules
- react-rules
5d. Skills Not Assigned to Any Agent
List skills in .claude/skills/ that are NOT in any agent's frontmatter. These are either:
- Main-context-only skills (like
/feature, /review) — expected
- Potentially useful skills missing from agents — flag for review
Step 6: Ask User
After presenting the report, use AskUserQuestion to ask:
"Would you like me to apply the recommended changes to the agent configuration files?"
If yes, update the .claude/agents/*.md files with the optimized skill lists.
Important Rules
- Read-only for ~/.claude — never modify files in
~/.claude/
- Agent config files (
.claude/agents/*.md) are in the project — those CAN be modified
- Base recommendations on data, not assumptions
- A skill with < 15% usage rate across 5+ invocations is a REMOVE candidate
- A skill with 0% usage across ANY number of invocations is a definite REMOVE
- Always show the data before recommending changes
- Consider that some skills may be critical for rare but important tasks — flag these as REVIEW rather than REMOVE