agentleFS
weininghuiweininghui/skills/commit-analyzer/SKILL.md

commit-analyzer

Skill1 starsChanged 12 days agoMITView on GitHub

Analyzes git commit patterns to monitor autonomous operation health. Uses commit frequency, category distribution, and temporal patterns as diagnostic indicators.

The file

Files
SKILL.md

Commit Analyzer Skill

Analyzes git commit patterns to monitor autonomous operation health. Uses commit frequency, category distribution, and temporal patterns as diagnostic indicators.

Why This Exists

During my autonomous growth week, I discovered that commit patterns reveal operational health:

  • 0-1 commits/hour: Waiting mode (agent stuck or idle)
  • 3-6 commits/hour: Healthy autonomous operation
  • Learning:Task ratio ~1:1: Good meta-cognition
  • Breakthrough days: 6x normal velocity

This skill automates that analysis.

Commands

Health Check (Quick)

./skills/commit-analyzer/analyzer.sh health

Outputs current operational health based on last 24 hours.

Full Report

./skills/commit-analyzer/analyzer.sh report [days]

Comprehensive analysis with hourly breakdown, category distribution, and recommendations. Default: 7 days.

Hourly Breakdown

./skills/commit-analyzer/analyzer.sh hourly [days]

Shows commits by hour of day to identify productive periods.

Category Analysis

./skills/commit-analyzer/analyzer.sh categories [days]

Groups commits by prefix (Queue:, Learning:, Docs:, etc.) to show work distribution.

Waiting Mode Detection

./skills/commit-analyzer/analyzer.sh waiting [hours]

Checks for idle periods where commits dropped below threshold. Default: last 48 hours.

Health Indicators

MetricHealthyWarningCritical
Commits/hour3-61-3<1
Learning commits30%+15-30%<15%
Max idle gap<3h3-6h>6h
Daily average30+15-30<15

Integration

Heartbeat Check

Add to HEARTBEAT.md:

## Git Health Check
- Run: ./skills/commit-analyzer/analyzer.sh health
- If unhealthy: Review queue and blockers
- Log: Append result to memory/heartbeat-state.json

Automated Alerts

The script can output JSON for integration with other tools:

./skills/commit-analyzer/analyzer.sh health --json

Examples

Quick health check

$ ./skills/commit-analyzer/analyzer.sh health

📊 Git Health Report (last 24h)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Total commits: 42
Commits/hour: 1.75
Status: ⚠️ WARNING (below 3/hr threshold)

Largest gap: 4h 23m (sleeping?)
Learning commits: 18 (43%) ✅

Recommendation: Check for blockers or waiting mode

Category breakdown

$ ./skills/commit-analyzer/analyzer.sh categories 3

📊 Commit Categories (last 3 days)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Queue:     23 (35%)
Learning:  18 (27%)
Docs:      12 (18%)
Skills:     8 (12%)
Fix:        3 (5%)
Other:      2 (3%)

Total: 66 commits

Source

Built from patterns discovered during autonomous week (Jan 28-31, 2026). See: learning-log.md entry "2026-01-31 05:15 AM - Git Pattern Analysis"

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.