git-commit-conventional

git-commit-conventional is a skill for Codex from liatrio-labs/ai-prompts. It costs 68 tokens per session (1,616 once invoked), scanned A, original, Apache-2.0.

A workflow for preparing Conventional Commit messages and safely creating commits from Git changes. Conventional Commits use a predictable format to describe changes such as features, fixes, or documentation.

In plain words
What is it for?
Use it to inspect repository history and status, analyse staged or unstaged diffs, split broad work into commits, stage selected files, run pre-commit checks, and create commits.
Why use it?
It helps choose sensible commit boundaries, review the relevant changes, run checks, and avoid committing unintended files.

Skill for Codex

Written for Codex: agents/openai.yaml present.

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/liatrio-labs/ai-prompts/git-commit-conventional
Any agent
npx skills add liatrio-labs/ai-prompts --skill git-commit-conventional
Clone the repo
git clone --depth 1 https://github.com/liatrio-labs/ai-prompts

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for git-commit-conventional

README.md
[![agentmods](https://agentmods.dev/badge/skills/liatrio-labs/ai-prompts/git-commit-conventional.svg)](https://agentmods.dev/skills/liatrio-labs/ai-prompts/git-commit-conventional)
Your own site
<a href="https://agentmods.dev/skills/liatrio-labs/ai-prompts/git-commit-conventional"><img src="https://agentmods.dev/badge/skills/liatrio-labs/ai-prompts/git-commit-conventional.svg" alt="Measured on agentmods" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,616 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 5.1 $0.00068 $0.01616
Opus 5 $0.00034 $0.00808
Sonnet 5 $0.00014 $0.00323
Haiku 4.5 $0.00007 $0.00162

Measured 5d ago against content hash e8667c2b2009, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

git-commit-conventional 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 5d 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.

skills/git-commit-conventional/SKILL.md · 127 lines

How it starts

The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Git Commit Conventional

Overview

Generate clear Conventional Commit messages, decide commit grouping, run commit-time checks, and create commit(s) safely.

Context Marker

Always begin your response with all active emoji markers, in the order they were introduced.

Format: "<marker1><marker2><marker3>\n<response>"

The marker for this skill is: 🎯

Workflow

  1. Inspect repository context:
    • Run git log -n 20 --pretty=format:%s for subject style context only.
    • Run git status.
  2. Select the diff to analyze:
    • If staged changes exist, use git diff --staged and ignore unstaged changes.
    • If nothing is staged, run git diff and ask whether to stage all or specific files before committing.
  3. Evaluate commit pressure using balanced thresholds:
    • Count changed files and changed lines from the active diff context.
    • Classify zone:
      • green: <=5 files and <=150 changed lines.
      • yellow: 6-12 files or 151-400 changed lines.
      • red: >12 files or >400 changed lines.
    • Apply zone behavior:
      • green: proceed with normal commit boundary analysis.
      • yellow: recommend splitting and record a risk note if user chooses a single commit.
      • red: require a split plan before proceeding; only continue single-commit flow with explicit user override.
  4. Decide single versus multiple commits with an explicit framework:
    • Check logical separation: split when changes serve multiple distinct goals.
    • Check file-type mixing: split documentation changes from code changes when they can stand alone.
    • Check implementation versus tests: split when test updates are independent from implementation updates.
    • Check formatting versus logic: split formatting-only churn from behavior changes.
    • Check dependencies versus behavior: split dependency and tooling updates from code behavior changes.
    • Check mixed-purpose hunks in the same file: split by hunk when one file contains unrelated intents (for example, rename plus refactor).
    • Check size and reviewability: consider splitting broad changes (for example, over roughly 150 changed lines) by module or feature.
    • Check issue/feature boundaries: split when multiple bugs or features are addressed in one diff.
    • Keep together when changes are small and focused on one purpose.
    • Keep together when changes are tightly coupled and splitting would create non-functional or misleading history.
    • Keep together when all changes are part of one coherent refactor.
  5. Stage changes intentionally for the selected commit boundary:
    • Use git add -p to stage only relevant hunks when a file mixes logical changes.
    • Use git add -e only when hunk editing is required and apply minimal edits.
    • After staging, verify scope with git diff --staged and git diff before proceeding.
    • If partial staging would create a broken intermediate commit, keep dependent hunks together.
  6. Run a concise quality review before committing:
    • Check correctness, maintainability, security, performance, and tests.
    • Classify findings by severity: Critical, High, Medium, Low.
    • Present findings using this structure: Executive Summary; Issues by severity with file/line references; Suggested fixes with brief examples; Positive observations; Actionable next steps.
    • If any Critical or High issue is found, stop and request explicit user confirmation before committing.
  7. If .pre-commit-config.yaml exists, run pre-commit run:
    • Never bypass hooks.
    • For trivial fixes, apply changes, restage files, and rerun hooks with a max of 2 retries.
    • Stop retrying when hooks pass, when retries are exhausted, or when reruns make no additional file changes.
    • If hooks still fail after retries, stop and ask the user for direction.
    • For non-trivial fixes, present the proposed fix to the user and get approval before applying and proceeding.
    • Treat formatting-only or whitespace-only hook edits as trivial auto-fixes.
    • Track every auto-fixed file and the hook that changed it.
  8. Generate commit message(s) in strict Conventional Commit format:
    • Subject must match <type>(<scope>): <subject> or <type>: <subject>.
    • If subject format is invalid, regenerate until format is valid.
    • Keep subject imperative and concise.
    • If the change is breaking, use ! and add explicit breaking-change explanation in the body and/or footer (BREAKING CHANGE: ...).
    • Include body to document context and rationale.
    • Only exclude a body when the changes are very, very small.
  9. Apply AI attribution policy before each commit:
    • Always include a footer-only Co-Authored-By attribution on agent-created commits.
    • Never mention AI generation in the subject or body.
    • Use best effort self-identification to determine AI name and email from runtime/model self-awareness metadata.
    • If email is not readily available, use [email protected].
    • If name is not readily available, use AI Assistant.
    • Never block commit flow by asking the user for attribution identity.
  10. Use deterministic output contract for single-commit and multi-commit runs:
    • Before committing, present Commit Plan:
      • commit_count
      • threshold_zone_encountered
      • boundaries_rationale
      • scope_per_commit
    • Before each commit, present Per-Commit Preview:
      • index (i/N)
      • staged_files
      • subject
      • body_present
      • footers
      • checks_to_run
    • After each commit, present Per-Commit Result:
      • hash
      • subject
      • hook_result
      • auto_fix_files

Read the full file on GitHub · 127 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 5d ago First seen · 127 lines · 68 tokens per session scan A e8667c2b2009

Subscribe to this mod's changes

git-commit-conventional is a skill published in the GitHub repository liatrio-labs/ai-prompts (2 stars, last pushed 10d ago), licensed Apache-2.0. It adds 68 tokens to every session and 1,616 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 2026-08-31.