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.
npx agentmods add skills/liatrio-labs/ai-prompts/git-commit-conventionalnpx skills add liatrio-labs/ai-prompts --skill git-commit-conventionalgit clone --depth 1 https://github.com/liatrio-labs/ai-promptsWrote 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.
[](https://agentmods.dev/skills/liatrio-labs/ai-prompts/git-commit-conventional)<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>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.
| Model | Per session | Once 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 |
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.
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
- Inspect repository context:
- Run
git log -n 20 --pretty=format:%sfor subject style context only. - Run
git status.
- Run
- Select the diff to analyze:
- If staged changes exist, use
git diff --stagedand ignore unstaged changes. - If nothing is staged, run
git diffand ask whether to stage all or specific files before committing.
- If staged changes exist, use
- Evaluate commit pressure using balanced thresholds:
- Count changed files and changed lines from the active diff context.
- Classify zone:
green:<=5files and<=150changed lines.yellow:6-12files or151-400changed lines.red:>12files or>400changed 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.
- 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.
- Stage changes intentionally for the selected commit boundary:
- Use
git add -pto stage only relevant hunks when a file mixes logical changes. - Use
git add -eonly when hunk editing is required and apply minimal edits. - After staging, verify scope with
git diff --stagedandgit diffbefore proceeding. - If partial staging would create a broken intermediate commit, keep dependent hunks together.
- Use
- 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.
- If
.pre-commit-config.yamlexists, runpre-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.
- 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.
- Subject must match
- Apply AI attribution policy before each commit:
- Always include a footer-only
Co-Authored-Byattribution 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.
- Always include a footer-only
- Use deterministic output contract for single-commit and multi-commit runs:
- Before committing, present
Commit Plan:commit_countthreshold_zone_encounteredboundaries_rationalescope_per_commit
- Before each commit, present
Per-Commit Preview:index(i/N)staged_filessubjectbody_presentfooterschecks_to_run
- After each commit, present
Per-Commit Result:hashsubjecthook_resultauto_fix_files
- Before committing, present
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.
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.
- 5d ago First seen · 127 lines · 68 tokens per session scan A e8667c2b2009
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.
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tdd-workflow
Test-driven development workflow.
expert
Base expert skill.
alpha
Alpha skill.
pr-fallback
WHAT — When the repo has no GitHub PR template, structure the pull-request body using the default in references/pr-body-default.md. Pair with output-handshake and github-cli-workflow. Does not open the PR; HOW stays in the forge skill.
alpha-evaluate
Factor evaluation. Multi-level evaluation pipeline (IC/ICIR/quintile/robustness). 因子评估。多级评估管线(IC/ICIR/分层/多空/鲁棒性)。 Triggers: "evaluate factor", "test factor", "评估因子", "测试因子".