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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 patternsまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
Codebase health audit scoring 7 categories with progression plan
日本語の概要は準備中です。原文の説明を表示しています。
Autonomous improvement loop: scan codebase metrics, scaffold experiment files, run agent-driven iterations until metric improves
日本語の概要は準備中です。原文の説明を表示しています。
Generate bounded independent candidates, score them against a frozen rubric, and verify the selected result with a proof log.
日本語の概要は準備中です。原文の説明を表示しています。
Post-deploy monitoring: watch production after a deploy and alert on regressions
日本語の概要は準備中です。原文の説明を表示しています。
Restore context after /clear by summarizing recent work and project state
日本語の概要は準備中です。原文の説明を表示しています。