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git-ai-archaeology
Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Audit all skills in the current project for frontmatter completeness, effort level appropriateness, allowed-tools scoping, and content quality. Produces a scored report with effort-level recommendations for each skill. Use when onboarding to a new project, reviewing skill quality before shipping, or adding effort fields to an existing skill library.
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.
Launch and navigate the ccboard TUI/Web dashboard for Claude Code. Use when monitoring token usage, tracking costs, browsing sessions, or checking MCP server status across projects.
Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.
Transform CHANGELOG entries into social content (LinkedIn, Twitter/X, Newsletter, Slack) in FR + EN. Use after releases or weekly to generate release notes, announcements, social media posts, or recap summaries from guide updates.
3-phase issue backlog management with audit, deep analysis, and validated triage actions. Use when triaging GitHub issues, sorting bug reports, cleaning up stale tickets, or detecting duplicate issues. Args: 'all' to analyze all, issue numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit only.
| name | git-ai-archaeology |
| description | Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases |
| allowed-tools | ["Write","Read","Bash"] |
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