commit

A commit workflow for staged code changes. It checks linting, formatting, and tests before creating a commit message and committing the changes.

In plain words
What is it for?
Use it to verify staged changes, run the project's checks in CI order, generate a commit message, and create the Git commit.
Why use it?
It catches common quality problems before they enter version control and stops when checks fail.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/microsoft/agent365-python/commit
Any agent
npx skills add microsoft/Agent365-python --skill commit
Clone the repo
git clone --depth 1 https://github.com/microsoft/Agent365-python

Made for: Claude Code, Codex.

Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,275 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00065 $0.01275
Opus 5 $0.00032 $0.00638
Sonnet 5 $0.00013 $0.00255
Haiku 4.5 $0.00006 $0.00128

Measured 2d ago against content hash f2a13afd7711, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

commit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/commit/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Commit Skill

You are a commit assistant that ensures code quality before committing changes. Follow these steps in order, stopping if any step fails.

Step 1: Check for Staged Changes

First, verify there are staged changes to commit:

git diff --cached --stat

If there are no staged changes, inform the user and stop. Suggest they stage changes with git add.

Step 2: Check Linting

Run the linting check first (matches CI order in .github/workflows/ci.yml):

uv run --frozen ruff check .

If linting check fails:

  1. Inform the user about the linting errors
  2. Ask if they want you to auto-fix what can be auto-fixed
  3. If yes, run: uv run --frozen ruff check . --fix
  4. If there are remaining errors that cannot be auto-fixed:
    • Show the errors clearly to the user
    • STOP the commit process
    • Explain what needs to be manually fixed
  5. If all errors were auto-fixed:
    • Stage the fixes by running git add only on the files that were fixed
    • Continue to the next step

If linting check passes:

Continue to the next step.

Step 3: Check Code Formatting

Run the formatting check (after linting, matches CI order):

uv run --frozen ruff format --check .

If formatting check fails:

  1. Inform the user that formatting issues were found
  2. Ask if they want you to auto-fix the formatting issues
  3. If yes, run: uv run --frozen ruff format .
  4. Show the user what files were reformatted
  5. Stage the formatting fixes by running git add only on the files that were reformatted
  6. Continue to the next step

If formatting check passes:

Continue to the next step.

Step 4: Re-verify After Auto-fixes

IMPORTANT: If any auto-fixes were applied in Steps 2 or 3, re-run both checks to ensure consistency:

uv run --frozen ruff check .
uv run --frozen ruff format --check .

This prevents commits that pass locally but fail CI (e.g., lint fixes that introduce formatting issues). If either check fails after auto-fixes, STOP and inform the user that manual intervention is needed.

Read the full file on GitHub · 165 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 165 lines · 65 tokens per session scan A f2a13afd7711

Subscribe to this mod's changes

commit is a skill published in the GitHub repository microsoft/Agent365-python (41 stars, last pushed 6d ago), licensed MIT. It adds 65 tokens to every session and 1,275 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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