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:

# If project argument provided, filter by it. Otherwise use current project.
# Subagent files live at: ~/.claude/projects/<encoded-path>/<session-id>/subagents/agent-*.jsonl
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:

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:

# .claude/agents/implementer.md — BEFORE
skills:
  - tanstack-start
  - typescript-rules
  - react-rules
  - uiux              # REMOVE (3% usage, ~1.5K tokens wasted per invocation)

# .claude/agents/implementer.md — AFTER
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