metrics

name	metrics
description	Collect agent usage metrics from git history and generate health reports. Use when measuring agent adoption, reviewing system health, or producing periodic dashboards. Collects Invocation Rate, Coverage, Infrastructure Review, and Usage Distribution. Use when you say "collect agent metrics", "generate metrics dashboard", or "weekly metrics report".
license	MIT
metadata	null
version	1.0.0
model	haiku
model-rationale	cost. The 'haiku' rolling alias resolves via the platform model_tiers map to a tier priced below the sonnet-tier harness default; this unit is routing/mechanical work where the cheaper tier suffices (ADR-080 rule 3).

Agent Metrics Collection Utility

Purpose

This utility collects and reports metrics on agent usage from git history. It collects 4 of the metrics defined in docs/agent-metrics.md (Invocation Rate, Coverage, Infrastructure Review, Usage Distribution) for measuring agent system health, effectiveness, and adoption.

Triggers

Trigger Phrase	Operation
collect agent metrics	Run collect_metrics.py with default 30-day window
generate metrics dashboard	Run with markdown output for reporting
check agent adoption rate	Run and highlight Metric 2 (agent coverage)
weekly metrics report	Run with 7-day window, markdown output
export metrics as JSON	Run with JSON output for automation

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When to Use

Use this skill when:

• Measuring agent system health or adoption trends
• Producing periodic dashboards or reports
• Evaluating whether agent usage is balanced across types
• Checking infrastructure review coverage

Use manual git log inspection instead when:

• Investigating a single commit's agent attribution
• Debugging a specific CI run's metrics workflow

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Process

1. Run the metrics collection script for the desired time range
2. Review generated reports for agent usage patterns
3. Identify trends and anomalies in adoption metrics

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Anti-Patterns

Avoid	Why	Instead
Running without specifying time window	Default 30 days may not match your intent	Use --since with explicit day count
Comparing metrics across different time windows	Misleading trends	Normalize to same window size
Ignoring zero agent coverage	Indicates broken detection patterns	Verify commit message conventions match patterns
Manual commit counting	Error-prone, misses patterns	Use the script for consistent detection
Storing JSON output without markdown	Loses human-readable context	Generate both formats for archival

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Verification

After execution:

• Script exits with code 0
• Output contains all 4 collected metrics (Invocation Rate, Coverage, Infrastructure Review, Distribution)
• Agent coverage percentage is plausible (not 0% unless truly no agent commits)
• Time window matches intended period
• For markdown output: report file created at expected path

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Available Scripts

Script	Platform	Usage
collect_metrics.py	Python 3.8+	Cross-platform

Quick Start

# Basic usage (30 days, summary output)
python .claude/skills/metrics/collect_metrics.py

# Last 90 days as markdown
python .claude/skills/metrics/collect_metrics.py --since 90 --output markdown

# JSON output for automation
python .claude/skills/metrics/collect_metrics.py --output json

Metrics Collected

The utility collects the following metrics:

Metric	Description	Target
Metric 1: Invocation Rate	Agent usage distribution	Proportional to task types
Metric 2: Agent Coverage	% of commits with agent involvement	50%
Metric 4: Infrastructure Review	% of infra changes with security review	100%
Metric 5: Usage Distribution	Agent utilization patterns	Balanced distribution

Detection Patterns

Agent Detection

The utility detects agents in commit messages using these patterns:

• Direct agent names: orchestrator, analyst, architect, etc.
• Review attribution: Reviewed by: security
• Agent tags: agent: implementer or [security-agent]

Infrastructure Files

Infrastructure commits are identified by these patterns:

• .github/workflows/*.{yml,yaml}
• .github/actions/**
• Root lefthook and .lefthook configs, with optional -local suffix
• .config/lefthook configs, with optional -local suffix
• Lefthook config extensions: .yml, .yaml, .json, .jsonc, .toml
• build/**, scripts/**
• Dockerfile*
• docker-compose*
• *.tf, *.tfvars
• .env*
• .agents/**

Commit Types

Conventional commit prefixes are classified:

• feat: - Feature
• fix: - Bug fix
• docs: - Documentation
• ci: - CI/CD
• refactor: - Refactoring

Output Formats

Summary (Default)

Human-readable console output with key metrics highlighted.

Markdown

Formatted markdown suitable for dashboards and reports. Can be saved directly to .agents/metrics/ for archival.

JSON

Structured data for programmatic consumption and CI integration.

CI Integration

See .github/workflows/agent-metrics.yml for automated weekly metrics collection.

The workflow:

1. Runs weekly on Sundays
2. Collects metrics for the previous 7 days
3. Generates a markdown report
4. Creates a PR with the report (if significant changes)

Manual Report Generation

To generate a monthly dashboard report:

# Generate report
python .claude/skills/metrics/collect_metrics.py \
    --since 30 \
    --output markdown \
    > .agents/metrics/report-$(date +%Y-%m).md

# Review and commit
git add .agents/metrics/
git commit -m "docs(metrics): add monthly metrics report"

Extending the Utility

Adding New Metrics

1. Define the metric in docs/agent-metrics.md
2. Add collection logic to both scripts
3. Update the output formatters
4. Add tests if applicable

Adding New Agent Patterns

Update the AGENT_PATTERNS / $AgentPatterns arrays to detect new agent references.

Adding Infrastructure Patterns

Update the INFRASTRUCTURE_PATTERNS / $InfrastructurePatterns arrays for new infrastructure file types.

Troubleshooting

No Agents Detected

• Ensure commit messages reference agents explicitly
• Check that conventional commit format is used
• Verify the patterns match your team's conventions

Git Errors

• Confirm you're in a git repository
• Check that the repository has commits in the date range
• Verify git is available in PATH

Related Documents

Backticked paths below are in the rjmurillo/ai-agents repository. They do not ship with this skill; a consumer install cannot resolve them.

• docs/agent-metrics.md. Agent metrics definitions.
• .agents/metrics/dashboard-template.md. Dashboard template.
• .agents/metrics/baseline-report.md. Baseline report.
• .github/workflows/agent-metrics.yml. CI workflow.