Claude Skill

git-ai-archaeology

Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases

Editor's Note

Analyze AI config evolution in a git repo — first commits per path, monthly distribution, major PRs, maturity phases Covers expected input, workflow, step 1: verification and global metrics.

Page Outline

Expected InputWorkflowStep 1: Verification and Global MetricsStep 2: Section 1 — First Commits per AI-Config PathStep 3: Section 2 — Monthly Distribution of AI-Config CommitsStep 4: Section 3 — Major PRs and CommitsStep 5: Section 4 — CHANGELOG Analysis (if available)Step 6: Section 5 — Evolution Phases

Source Content

Normalized top-level metadata comes from the directory layer. The body below is the upstream source content for this item.

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

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

---

Step 1: Verification and Global Metrics

# 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`:

# 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:

# 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:

  • 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

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

3.2 — MCP Server integrations

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

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

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)

# 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 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").

---

Output Format: {slug}-git-archaeology.md

# 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*

---

Important Rules

  • **Read-only**: no git commands that modify repo state
  • **Verify before asserting**: a date not found in git = note "unverified"
  • **Adapt paths**: Section 1 paths must be filtered to what actually exists in this repo
  • **Extensible keywords**: if the repo uses different conventions (e.g., `feat[ai]` vs `feat(ai)`), adapt grep patterns
  • **Section 4 optional**: if no CHANGELOG.md or no AI mentions, note "Not applicable" and skip to Section 5
  • **Adaptive phases**: 4 phases is a common pattern, not a rule — 2 phases or 6 phases are equally valid

Anti-Patterns

  • Inventing data not found in git
  • Rounding numbers without flagging it
  • Analyzing paths that don't exist in this repo
  • Confusing a rename commit with a creation
  • Omitting "flat" months (0 AI-config commits also tells a story)

Validation Checklist

  • [ ] Repo verified and readable
  • [ ] Section 1: only paths that exist in this repo
  • [ ] Section 2: distribution covers the full repo period
  • [ ] Section 3: commits sorted chronologically, hash included
  • [ ] Section 4: cleanly skipped if no CHANGELOG
  • [ ] Section 5: phases based on data, not the template
  • [ ] Output file saved

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