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Git Ai Archaeology

CommunityPopular
FlorianBruniaux
git-ai-archaeology

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.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill namegit-ai-archaeology
Stars
6K
Forks
782
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FlorianBruniaux on GitHub. Read the source before you install it.

Installation

Install the Git Ai Archaeology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/git-ai-archaeology .claude/skills/git-ai-archaeology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Git Ai Archaeology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Git Ai Archaeology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Git Ai Archaeology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

ReleaseDateAI-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:

PhaseCharacteristicsCommitsLabel
Phase 1Basic config, solo usage, no structureLow"Config as Afterthought"
Phase 2Documentation, knowledge base, first MCPGrowing"Config as Documentation"
Phase 3Infrastructure: skills/hooks/rules/MCP stackSpike"Config as Infrastructure"
Phase 4Engineering: tests, CI, guardrails, modulesDense"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

markdown
# 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

Frequently asked questions

What does the Git Ai Archaeology AI skill do?

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.

Why use Git Ai Archaeology on TypingMind?

Because you install it once and use it with any model. Git Ai Archaeology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Git Ai Archaeology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/git-ai-archaeology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Git Ai Archaeology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Git Ai Archaeology?

As many as you like. As long as a model supports skills, you can use Git Ai Archaeology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Git Ai Archaeology AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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