Skill Monitor

Closed-loop skill effectiveness monitoring. Reads session metrics, computes per-skill signals, identifies what's working and what needs improvement.

Inspired by the deploy-monitor-evaluate-improve feedback loop: skills get better over time instead of staying static.

Requirements

Requires .claude/session-metrics/metrics.jsonl from /session-scan. If no data: suggest running /session-scan first.

Usage

/skill-monitor                     # Dashboard: all skills
/skill-monitor --skill review      # Deep-dive on one skill
/skill-monitor --improve           # Generate improvement recommendations
/skill-monitor --window 30d        # Change comparison window (default: 7d)

What Main Context Does

Step 1: Parse Arguments

Extract from $ARGUMENTS:

• --skill NAME: Focus on one skill (e.g., review, plan, investigate)
• --improve: Spawn analysis agent for improvement recommendations
• --window PERIOD: Comparison window (7d, 30d, all; default: 7d)

Step 2: Load Metrics

Read .claude/session-metrics/metrics.jsonl. For each entry, extract the skill_effectiveness field (added by compute-metrics.py v2).

Filter by window period. Count sessions with and without skill usage.

If no skill_effectiveness data exists in metrics: "Metrics were computed before skill tracking was added. Run /session-scan --rescan to recompute."

OTel invocation_trigger (CC v2.1.126+): when compute-metrics.py ingests claude_code.skill_activated events, each invocation carries an invocation_trigger of "user-slash", "claude-proactive", or "nested-skill". If absent (older sessions), default to "unknown", do NOT assume "user-slash".

Step 3: Compute Per-Skill Aggregates

For each skill found across all sessions, aggregate: