Claude-skill-registry-data metrics

Collect and report metrics on agent usage from git history implementing the 8 key metrics defined in agent-metrics.md for measuring agent system health effectiveness and adoption.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry-data

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/metrics" ~/.claude/skills/majiayu000-claude-skill-registry-data-metrics && rm -rf "$T"

manifest: data/metrics/SKILL.md

source content

Agent Metrics Collection Utility

Purpose

This utility collects and reports metrics on agent usage from git history. It implements the 8 key metrics defined in

docs/agent-metrics.md

for measuring agent system health, effectiveness, and adoption.

Available Scripts

Script	Platform	Usage
`collect_metrics.py`	Python 3.8+	Cross-platform
`collect-metrics.ps1`	PowerShell 5.1+	Windows/Linux/macOS

Quick Start

Python

# 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

PowerShell

# Basic usage (30 days, summary output)
.\.agents\utilities\metrics\collect-metrics.ps1

# Last 90 days as markdown
.\.agents\utilities\metrics\collect-metrics.ps1 -Since 90 -Output Markdown

# JSON output for automation
.\.agents\utilities\metrics\collect-metrics.ps1 -Output Json | ConvertFrom-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
```
```
.githooks/*
```
```
Dockerfile*
```
```
*.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:

Runs weekly on Sundays
Collects metrics for the previous 7 days
Generates a markdown report
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

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

Adding New Agent Patterns

Update the

AGENT_PATTERNS

$AgentPatterns

arrays to detect new agent references.

Adding Infrastructure Patterns