baphled/dotopencode/ai-commit
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Create properly attributed commits for AI-generated code
Install
npx vskill@latest install baphled/dotopencode/ai-commitSKILL.md
Skill: ai-commit
What I do
I provide expertise in creating properly attributed commits for AI-generated code using the project's standard workflow. I ensure every commit is atomic, follows conventional commit formats, and includes mandatory co-authoring attribution.
When to use me
- When creating new commits for code generated or modified by AI
- When you need to split changes into atomic, logical units
- When attributing work to both the human developer and the AI agent
Core principles
- Atomic commits: Each commit must represent a single, logical change. Do not bundle unrelated fixes or features together.
- Standard workflow: Always write your commit message to a temporary file first, then use the project's make target for execution.
- Proper attribution: Include the Co-authored-by trailer for the AI model used to maintain a clear audit trail.
- Conventional format: Use clear types like feat, fix, docs, or refactor to categorise changes.
Patterns & examples
Workflow for a new commit:
- Stage your changes with
git add. - Write the message to a file, for example
/tmp/commit.txt. - Run
make ai-commit FILE=/tmp/commit.txt.
Example commit message in /tmp/commit.txt:
Using fixup commits: For small corrections to a previous, unpushed commit, use git commit --fixup=<hash> to keep history clean before a final squash.
Anti-patterns to avoid
- ❌ Direct git commit: Skipping the
make ai-committarget loses consistent formatting and attribution. - ❌ Bloated commits: Bundling multiple logical changes makes code reviews difficult and rollbacks risky.
- ❌ Missing trailers: Failing to include co-authoring information breaks the project's attribution rules.
KB Reference
~/vaults/baphled/3. Resources/Knowledge Base/AI Development System/Skills/Git/AI Commit.md
Related skills
git-master: For advanced history search and planninggit-advanced: For rebase and history managementclean-code: To ensure the committed code meets quality standards
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/ai-commit/SKILL.md
Popularity
- Repository stars
- 1
- Repository forks
- 1
- 7-day trend score
- 8
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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