Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Trace how AI tooling developed in a Git repository by identifying configuration file origins, monthly commit activity, major changes, and relevant releases. Produce a Markdown report that explains adoption phases and turning points using Git evidence.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Produces a complete analysis of AI config evolution in a git repository. Finds when each AI configuration file was created, how AI-config commit velocity evolved month by month, which PRs structured the evolution, and identifies maturity phases.
Output: a single file {output_dir}/{slug}-git-archaeology.md
/git-ai-archaeology repo_path=/path/to/repo [output=./talks/slug] [slug=talk-name] [since=2025-01-01]
repo_path: absolute path to the target git repo (required)output: output directory (default: ./talks)slug: output filename (default: repo folder name)since: analysis start date (default: first repo commit)# Verify it's a git repo
git -C {repo_path} rev-parse --git-dir
# Global metrics
git -C {repo_path} log --oneline | wc -l # total commits
git -C {repo_path} tag --sort=version:refname | wc -l # total releases
git -C {repo_path} shortlog -sn --no-merges | wc -l # contributors
git -C {repo_path} log --pretty=format:"%ad" --date=short | tail -1 # first commit
git -C {repo_path} log --pretty=format:"%ad" --date=short | head -1 # last commit
git -C {repo_path} log --merges --oneline | wc -l # merged PRs
For each path, find the origin commit with --diff-filter=A:
# Paths to analyze, adapt based on what exists in the repo
PATHS=(
"CLAUDE.md"
".claude"
".claude/commands"
".claude/agents"
".claude/hooks"
".claude/skills"
".claude/rules"
".agents"
".cursor"
"doc/knowledge-base.md"
"doc/guides/ai-instructions"
"doc/guides/ai-review"
)
for path in "${PATHS[@]}"; do
git -C {repo_path} log --diff-filter=A --follow \
--format="%ad | %H | %s" --date=short \
-- "$path" | tail -1
done
Build the Section 1 table from results. Skip paths with no output (don't exist in this repo).
Also build the ASCII timeline:
{date} --- {path} --- {message}
Sorted chronologically.
Filter commits by AI-config-related keywords:
# All commits with AI-config keywords
git -C {repo_path} log --format="%H %s" | \
grep -iE "(claude|feat.ai|docs.ai|tech.ai|mcp|skill|hook|agent|llm|prompt)" \
> /tmp/ai_commits_filtered.txt
# Count AI-config commits per month
git -C {repo_path} log --format="%ad %H" --date=format:"%Y-%m" | \
while read month hash; do
if grep -q "$hash" /tmp/ai_commits_filtered.txt; then
echo "$month"
fi
done | sort | uniq -c
More direct alternative:
git -C {repo_path} log --format="%ad %s" --date=format:"%Y-%m" | \
grep -iE " (feat|fix|docs|tech|chore|refactor)\(ai\)|claude|mcp.*server|\.claude/|skill|hook.*security|guardrail" | \
awk '{print $1}' | sort | uniq -c
Compute per month:
Build ASCII distribution chart (horizontal or vertical bars).
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
grep -iE "\(ai\)|\(mcp\)|\[ai\]"
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
grep -iE "mcp|serena|grepai|perplexity|sonar|postgres.*mcp|cursor.*mcp"
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
grep -iE "feat\(skill|feat\(hook|feat\(agent|feat\(command|feat\(dx\)|feat\(ci\)" | \
grep -v "^$"
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
grep -iE "review|code-review|pr.*auto|ci.*review"
# Check if CHANGELOG.md exists
ls {repo_path}/CHANGELOG.md
# Extract releases with AI mentions
grep -n "## \[" {repo_path}/CHANGELOG.md | head -30
Read the CHANGELOG and build a table:
| Release | Date | AI-Related Content |
|---|
Only list releases with AI-config content (CLAUDE.md, MCP, agents, skills, hooks, guardrails, prompts, etc.).
Analyze collected data and identify maturity phases. Typical pattern:
| Phase | Characteristics | Commits | Label |
|---|---|---|---|
| Phase 1 | Basic config, solo usage, no structure | Low | "Config as Afterthought" |
| Phase 2 | Documentation, knowledge base, first MCP | Growing | "Config as Documentation" |
| Phase 3 | Infrastructure: skills/hooks/rules/MCP stack | Spike | "Config as Infrastructure" |
| Phase 4 | Engineering: tests, CI, guardrails, modules | Dense | "Config as Engineering Practice" |
Adapt phases to what the data actually reveals.
Identify the main inflection point: the month where AI-config commit volume spiked.
Compute the "recent vs historical" ratio (e.g., "81% of AI-config commits in the last 2 months").
# Git Archaeology: AI Config Evolution: {slug}
**Source**: Git history of repo `{repo_path}` ({total_commits}+ commits, {total_releases}+ releases)
**Method**: `git log --diff-filter=A` for first commits, filtered monthly distribution, major PRs
**Last updated**: {date}
---
## Section 1: First Commit per Key Path
| Path | Creation Date | Commit Message | Hash |
|------|--------------|----------------|------|
{rows}
### Creation Timeline
\```
{ascii_timeline}
\```
---
## Section 2: Monthly Distribution of AI-Config Commits
| Month | AI-Config Commits | % of Total | Context |
|-------|-------------------|-----------|---------|
{rows}
### Visualization
\```
{ascii_chart}
\```
**Inflection**: {insight on the commit spike}
---
## Section 3: Major PRs and Commits Related to AI Tooling
### 3.1 PRs `feat(ai):` / `tech(ai):` / `docs(ai):`
| Date | Hash | Message | Impact |
|------|------|---------|--------|
{rows}
### 3.2 MCP Server Integrations (chronological)
| Date | MCP Server | Hash / PR | Role |
|------|------------|-----------|------|
{rows}
### 3.3 Skills, Commands, Hooks, Agents
| Date | Hash | Message | Category |
|------|------|---------|----------|
{rows}
### 3.4 Code Review Automation
| Date | Hash | Message |
|------|------|---------|
{rows}
---
## Section 4: CHANGELOG AI Mentions by Release
{section if CHANGELOG available, otherwise "Not applicable"}
---
## Section 5: Evolution Phases
### Evidence-Based Timeline
| Milestone | Exact Git Date | Git Evidence |
|-----------|----------------|-------------|
{rows}
### {N} Evolution Phases
#### Phase 1: {Label} ({period}), {n} commits
{description}
#### Phase 2: {Label} ({period}), {n} commits
{description}
#### Phase 3: {Label} ({period}), {n} commits
{description}
#### Phase 4: {Label} ({period}), {n} commits
{description}
### Key Insight
{Summary paragraph: main inflection point, recent/historical ratio, what the data reveals about the project's AI maturity.}
---
*Generated by git-ai-archaeology, {date}*
*Repo: {repo_path} | {total_commits} commits | {total_releases} releases*
feat[ai] vs feat(ai)), adapt grep patterns아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assess a codebase across seven areas, including exposed credentials, security, dependencies, structure, and tests. Receive category scores, findings tied to file locations, and a prioritized improvement plan with smaller tasks that can be completed quickly.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Measure code patterns such as casts to any or disabled lint rules, then run repeated, targeted edits. Keep changes that improve the chosen metric, revert unsuccessful changes, and track results for each improvement loop.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Generate several separate solutions, compare them against criteria fixed in advance, and verify the selected result through executable checks or an independent reviewer. Keep a proof log documenting candidates, evidence, failures, and remaining limits.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Post-deploy monitoring: watch production after a deploy and alert on regressions
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Restore context after /clear by summarizing recent work and project state
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.