task-commit-trigger
Skill4 starsChanged 3 months agoMITView on GitHub
WHEN/WHERE/WHO: [Scheduling: Developers or agents who have just finished a task and want a structured commit message.] HOW: [Structural: Use this SKILL to auto-trigger a commit prompt and generate a structured WHAT/HOW/WHY git commit.] WHY: [Scheduling: Capturing the rationale immediately after task completion ensures accurate context and saves time.]
In this repository61 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 8 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter …
- Very specific: line 46
Failure Signals: Commit prompts trigger too early, unrelated files are mixed in, commit messages lose the WHAT/HOW/WHY structure, or the …
- Distinctive: line 51
Transfer Check: Verify the workflow still works for small tasks and larger multi-file completions.
- Distinctive: line 52
Stop Rule: If the completed task or changed-files scope is unclear, stop and ask before drafting a commit.
- Distinctive: line 55
Do not commit changes unrelated to the completed task.
- Distinctive: line 56
Keep the commit message concise but informative.
- Distinctive: line 57
Ensure the WHY section focuses on design decisions, not just repeating the HOW.
- Distinctive: line 60
If anything is unclear, missing, or conflicting, stop and ask the user before proceeding.
- Distinctive: line 64
Analyze the changes: If the user agrees, review the git diff and the completed task context.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Task Commit Trigger
Common Contract (Load First)
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter rules, anti-pattern guidance, and the Review In Mind loop in review-in-mind. This skill adds only domain-specific rules below.
Who
Developers or agents who have just finished a task and want a structured, high-quality git commit message.
What
Auto triggers or notices developer when a task is just completed by asking: "Shall we commit just completed work?". If the user agrees, it generates a structured git commit message explaining the rationale (WHAT, HOW, WHY) and performs the commit.
When
- A user request or task discussed in the conversation context is fulfilled/completed.
- The user says something like "we're done with this task", "looks good", "save the progress", or explicitly asks to commit.
- The agent finishes a logical unit of work during the interaction.
Where
Applies to the local git repository in the workspace.
Why
- Good commit messages are essential for project maintainability.
- Capturing the WHAT, HOW, and WHY immediately after task completion ensures accurate context.
- It saves developers time and cognitive effort.
Inputs
- Changes made: The diff or description of the files modified during the task.
- Task context: The goal of the task that was just completed.
Output (Logical Evidence)
- A structured commit message containing:
- WHAT: A short summary of what was solved or added.
- HOW: A brief explanation of the technical changes made.
- WHY: The rationale behind the chosen approach or design decisions.
- Execution of the git commit (if approved).
Optimization Readiness
- Failure Signals: Commit prompts trigger too early, unrelated files are mixed in, commit messages lose the WHAT/HOW/WHY structure, or the rationale becomes a restatement of the diff.
- Evidence To Collect: Diff summaries, drafted commit messages, approval responses, and examples where the commit prompt aligned with a completed logical unit of work.
- Safe Mutation Boundaries: Refine trigger wording, diff-review guidance, message formatting, and approval steps without changing the core post-completion commit flow.
- Acceptance Criteria: Accept revisions only if the skill still asks at the right time, keeps changes scoped, and produces a concise WHAT/HOW/WHY commit rationale.
- Rejected Revision Handling: Record premature prompts, unrelated diff bundles, and vague commit rationales so they are not repeated.
- Transfer Check: Verify the workflow still works for small tasks and larger multi-file completions.
- Stop Rule: If the completed task or changed-files scope is unclear, stop and ask before drafting a commit.
Constraints (Logical Boundaries)
- Do not commit changes unrelated to the completed task.
- Keep the commit message concise but informative.
- Ensure the WHY section focuses on design decisions, not just repeating the HOW.
One More Thing
If anything is unclear, missing, or conflicting, stop and ask the user before proceeding.
How (Structural Workflow)
- Proactive Notification: Immediately upon completing a task in the conversation, auto trigger or notice developer: "Shall we commit just completed work?".
- Analyze the changes: If the user agrees, review the git diff and the completed task context.
- Draft the message: Structure the commit message with clear headers or bullet points for WHAT, HOW, and WHY.
- Present to user: Show the drafted commit message to the user for final review.
- Commit: Upon approval, execute the git commit command.
Review In Mind (ReviewInMindGenie)
Execute the common review loop in review-in-mind before delivering.
Review lens for this skill:
- Is the commit message structured as WHAT/HOW/WHY and grounded in the actual diff?
- Is the commit only executed after explicit user approval?
- Does the message avoid exposing secrets or unrelated changes?
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

