Commit Archaeologist logo

Commit Archaeologist

CommunityPopular
Shubhamsaboo
commit-archaeologist

Reconstructs why code exists from local git history, including the introducing commit, later changes, current authors, repeated companion files, and likely intent. Use when the user asks "why does this code exist", "who wrote this function and why", or to "explain the history of this function" before a rewrite, refactor, or risky edit. Runs entirely locally.

Overview

PublisherShubhamsaboo
Repositoryawesome-llm-apps
Skill namecommit-archaeologist
Stars
138.7K
Forks
20.4K
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Commit Archaeologist 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/Shubhamsaboo/awesome-llm-apps.git /tmp/awesome-llm-apps
mkdir -p .claude/skills
cp -r /tmp/awesome-llm-apps/agent_skills/commit-archaeologist .claude/skills/commit-archaeologist
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commit Archaeologist 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 Commit Archaeologist 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 Commit Archaeologist 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.

Commit Archaeologist

git blame names the last person to touch a line. This skill reconstructs the reason the line exists. It traces a file or current line range through local git history, identifies its origin and later edits, finds files that repeatedly changed beside it, and extracts intent clues from commit messages.

Everything runs locally. No network calls, API keys, or repository writes.

When to use

  • The user asks why a block, function, or file exists
  • The user wants to know who introduced code and what changed afterward
  • A rewrite or refactor needs historical constraints and change risks
  • A workaround, temporary branch, or surprising design choice needs context

When not to use

  • The user only wants raw blame output or a commit list
  • The task is to squash, delete, or rewrite git history
  • The repository is remote and has not been cloned locally
  • The question is about project-wide architecture rather than one file or region

Gather the target

Get the repository path and tracked file path. Use a line range when the user names a current block or function. If either path is missing, ask only for the missing value. Do not scan sibling repositories.

Paths should be relative to the repository and use forward slashes. Line ranges use inclusive current line numbers such as 40-72.

Run the dig

From this skill directory:

bash
python3 scripts/archaeologist.py /path/to/repo src/cache.py --lines 40-72 --json

For the complete history of a file, omit --lines:

bash
python3 scripts/archaeologist.py /path/to/repo src/cache.py --json

The script is read-only. With a range it uses git line-log; without one it uses file history with rename following. It also reads current authorship with blame. If it rejects a path or range, report that error and ask for a corrected target.

Before interpreting the JSON, read references/reading-git-history.md. Its confidence rules prevent blame ownership, correlation, and commit-message hints from becoming false certainty.

Read the JSON

  • region: normalized repository, current and historical paths, mode, range, and co-change threshold
  • introduced_by: oldest commit in the selected history
  • timeline: commits ordered oldest to newest, with category, changed files, and detected renames
  • co_changed: files present beside the target in enough selected commits
  • authors: current blamed lines and historical commit counts, kept separate
  • intent_signals: issue references, reverts, workarounds, temporary markers, and unfinished-work markers found in commit messages

An empty signal list means the messages do not say why. It does not mean the change had no reason.

Write the report

Turn the JSON into a short "why this code exists" report:

  1. Bottom line. One or two sentences with the most likely explanation and a confidence label: high, medium, or low.
  2. Origin. Introducing hash, date, author, subject, and the neighboring files that make the initial purpose clearer.
  3. Timeline. Oldest to newest. Group mechanical edits when they do not change the story, but preserve reverts, fixes, and workarounds.
  4. Companion files. Explain repeated co-changes as a coupling clue. Do not treat a one-off file in changed_files as a dependency.
  5. Intent evidence. Quote short commit subjects or signal words. Label any conclusion beyond those facts as an inference.
  6. Change risk. Name the constraints, companion files, and unresolved temporary choices worth checking before an edit.

Keep hashes short in prose, but preserve enough characters to identify them. Do not dump the full JSON unless the user asks.

Evidence rules

  • Current blame ownership is not proof of original authorship.
  • The oldest selected commit is the region's introduction, not always the file's first commit.
  • Repeated co-change suggests coupling; it does not prove a dependency.
  • Commit categories are message heuristics, not verified issue types.
  • "Temporary" and "workaround" are strong intent clues, but only the message author or linked discussion can confirm whether the constraint still applies.
  • If the history is thin or messages are vague, say "the history shows" and "likely" instead of inventing a design rationale.

Follow-ups

End by offering one relevant follow-up, such as:

  • "Want me to assess whether this looks deliberate or accidental?"
  • "Want me to map what could break if this region changes?"

For a deliberate-choice question, weigh repeated edits, issue references, reverts, and explicit constraint words. For a change-risk question, inspect the repeated co-change files and the last behavior-changing commits before proposing edits. Do not modify code until the user asks.

Files

  • scripts/archaeologist.py: offline git history walker and JSON report builder
  • references/reading-git-history.md: interpretation and confidence guide

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Commit Archaeologist AI skill do?

Reconstructs why code exists from local git history, including the introducing commit, later changes, current authors, repeated companion files, and likely intent. Use when the user asks "why does this code exist", "who wrote this function and why", or to "explain the history of this function" before a rewrite, refactor, or risky edit. Runs entirely locally.

Why use Commit Archaeologist on TypingMind?

Because you install it once and use it with any model. Commit Archaeologist 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 Commit Archaeologist in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/commit-archaeologist. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Commit Archaeologist?

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 Commit Archaeologist?

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

Is the Commit Archaeologist AI skill free?

Yes. It is published on GitHub by Shubhamsaboo under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇