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Git Bisect Assistant

Community
ArabelaTso
git-bisect-assistant

Automatically performs git bisect to identify the first bad commit that introduced a bug or failure. Use when debugging regressions, tracking down when a test started failing, or identifying which commit broke functionality. Handles flaky tests with retry logic and provides comprehensive reports with bisect logs and confidence levels.

Overview

PublisherArabelaTso
RepositorySkills-4-SE
Skill namegit-bisect-assistant
Stars
258
Forks
25
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Git Bisect Assistant 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.
    https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/git-bisect-assistant
  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/ArabelaTso/Skills-4-SE.git /tmp/Skills-4-SE
mkdir -p .claude/skills
cp -r /tmp/Skills-4-SE/skills/git-bisect-assistant .claude/skills/git-bisect-assistant
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Git Bisect Assistant 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 Bisect Assistant 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 Bisect Assistant 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 Bisect Assistant

Automates the git bisect process to efficiently identify the first bad commit responsible for a bug or test failure.

Quick Start

Basic usage pattern:

bash
python scripts/git_bisect_runner.py \
  --good <known-good-commit> \
  --bad <known-bad-commit> \
  --test "<test-command>"

Example:

bash
python scripts/git_bisect_runner.py \
  --good v1.0.0 \
  --bad HEAD \
  --test "pytest tests/test_feature.py::test_specific_case"

Workflow

  1. Gather Information

    • Identify the known good revision (commit, tag, or branch)
    • Identify the known bad revision (defaults to HEAD)
    • Determine the test command that fails on bad commits and passes on good commits
  2. Run Bisect

    • Execute the git_bisect_runner.py script with appropriate parameters
    • The script will automatically test commits and narrow down the culprit
  3. Review Results

    • Examine the identified bad commit
    • Review the bisect log showing all tested commits
    • Check confidence level and assumptions

Parameters

Required

  • --good: Known good revision (commit hash, tag, or branch name)
  • --test: Shell command to test each commit. Exit code 0 = good, non-zero = bad

Optional

  • --bad: Known bad revision (default: HEAD)
  • --repo: Repository path (default: current directory)
  • --retries: Number of test runs per commit for flaky tests (default: 1)
  • --timeout: Test execution timeout in seconds (default: no timeout)

Handling Flaky Tests

For non-deterministic tests, use --retries to run the test multiple times per commit:

bash
python scripts/git_bisect_runner.py \
  --good abc123 \
  --bad HEAD \
  --test "npm test" \
  --retries 3

The script uses majority voting: if a test passes 2 out of 3 times, the commit is marked as good.

Test Command Guidelines

The test command should:

  • Exit with code 0 for good commits (test passes)
  • Exit with non-zero code for bad commits (test fails)
  • Be deterministic or use --retries for flaky tests
  • Complete within reasonable time or use --timeout

Examples:

bash
# Python test
--test "pytest tests/test_auth.py -v"

# Shell script
--test "./scripts/verify_build.sh"

# Compilation check
--test "make && ./bin/app --version"

# Multiple commands
--test "npm install && npm test"

Output Report

The script generates a comprehensive report including:

  • First Bad Commit: Hash and commit message of the culprit
  • Confidence Level: Assessment based on test stability and retry logic
  • Assumptions: Any assumptions made during bisect (retries, timeouts)
  • Tested Commits: Complete list of all commits tested with results
  • Bisect Log: Detailed log of the bisect process

Common Scenarios

Scenario 1: Test Started Failing

User: "The integration tests started failing sometime in the last 20 commits"

bash
python scripts/git_bisect_runner.py \
  --good HEAD~20 \
  --bad HEAD \
  --test "pytest tests/integration/"

Scenario 2: Feature Broke After Release

User: "Feature X worked in v2.1.0 but is broken now"

bash
python scripts/git_bisect_runner.py \
  --good v2.1.0 \
  --bad HEAD \
  --test "python -c 'import app; assert app.feature_x() == expected'"

Scenario 3: Flaky Test Investigation

User: "A test fails intermittently, need to find when it started"

bash
python scripts/git_bisect_runner.py \
  --good main \
  --bad feature-branch \
  --test "pytest tests/test_flaky.py" \
  --retries 5 \
  --timeout 30

Tips

  • Ensure clean state: Commit or stash changes before running bisect
  • Fast tests: Use focused tests rather than full test suites for faster bisect
  • Build requirements: Include build steps in test command if needed
  • Dependencies: Ensure test command handles dependency installation if needed across commits
  • Timeout wisely: Set timeout slightly longer than expected test duration
  • Retry count: Use 3-5 retries for flaky tests to get reliable results

Troubleshooting

Bisect fails to start: Verify good and bad revisions exist and are valid git references

Test command fails unexpectedly: Test the command manually on a known good/bad commit first

Inconsistent results: Increase --retries or check for environmental factors affecting tests

Timeout too short: Increase --timeout or optimize test command

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 Git Bisect Assistant AI skill do?

Automatically performs git bisect to identify the first bad commit that introduced a bug or failure. Use when debugging regressions, tracking down when a test started failing, or identifying which commit broke functionality. Handles flaky tests with retry logic and provides comprehensive reports with bisect logs and confidence levels.

Why use Git Bisect Assistant on TypingMind?

Because you install it once and use it with any model. Git Bisect Assistant 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 Bisect Assistant in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ArabelaTso/Skills-4-SE/tree/main/skills/git-bisect-assistant. 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 Git Bisect Assistant?

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 Bisect Assistant?

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

Is the Git Bisect Assistant AI skill free?

Yes. It is published on GitHub by ArabelaTso 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.

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