Git Ai ArchaeologyCAUTION

The most comprehensive Claude Code guide: agentic workflows, hooks, skills, MCP servers, quizzes, and production-ready templates. 430K+ lines.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: git-ai-archaeology
description: "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."
allowed-tools: Write Read Bash
effort: medium
---

# git-ai-archaeology

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`

## Expected input

```
/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)

## Workflow

1. **Verify the repo**: ensure the path exists and is a git repo
2. **Global metrics**: total commits, releases, contributors, time period
3. **Section 1: First commits**: find creation date for key AI-config paths
4. **Section 2: Monthly distribution**: commits filtered by AI-config keywords
5. **Section 3: Major PRs**: extract and categorize significant AI-config commits
6. **Section 4: CHANGELOG**: if CHANGELOG.md exists, extract releases with AI mentions
7. **Section 5: Phases**: synthesize evolution phases
8. **Save** the output file

---

## Step 1: Verification and global metrics

```bash
# 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
```

---

## Step 2: Section 1: First commits per AI-config path

For each path, find the origin commit with `--diff-filter=A`:

```bash
# 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.

---

## Step 3: Section 2: Monthly distribution of AI-config commits

Filter commits by AI-config-related keywords:

```bash
# 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:

```bash
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:
- AI-config commit count
- % of monthly total (cross-reference with all-category monthly total)
- Context (if notable period)

Build ASCII distribution chart (horizontal or vertical bars).

---

## Step 4: Section 3: Major PRs and commits

### 3.1: feat(ai): / docs(ai): / tech(ai): commits

```bash
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
  grep -iE "\(ai\)|\(mcp\)|\[ai\]"
```

### 3.2: MCP Server integrations

```bash
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
  grep -iE "mcp|serena|grepai|perplexity|sonar|postgres.*mcp|cursor.*mcp"
```

### 3.3: Skills, commands, hooks, agents

```bash
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 "^$"
```

### 3.4: Code review automation

```bash
git -C {repo_path} log --format="%ad | %H | %s" --date=short | \
  grep -iE "review|code-review|pr.*auto|ci.*review"
```

---

## Step 5: Section 4: CHANGELOG Analysis (if available)

```bash
# 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.).

---

## Step 6: Section 5: Evolution phases

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 poin

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (2)

whitepapers/recap-cards/en/_extensions

whitepapers/recap-cards/fr/_extensions

Gates applied: no_behavioural_pass.