agentleFS
lgzarturolgzarturo/codeconductor/.agents/skills/cc-fix/SKILL.md

cc-fix

Skill0 starsChanged 27 days agoMITView on GitHub

Bug description: $ARGUMENTS Provide the following information in $ARGUMENTS: Same web scope as cc-feature: when the fix concerns web layout, component states, feedback, motion, or a requested UI audit, apply skill web-design-engineering during test/implementation and review; keep this workflow's gates and TDD order. See cc-feature for the full scope rule. Same Android scope as cc-feature: when the fix concerns concrete Jetpack Compose phone UI or a requested UI audit, apply skill android-ui-design during test/implementation and review; keep this workflow's gates…

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    If reproduction steps are missing, task-coach must ask for them before classifying risk.

    line 48
  2. Distinctive:

    Read the risk field from the Task Card and follow the corresponding route.

    line 58
  3. Distinctive:

    Write a regression test that reproduces the original bug (fails before the fix, passes after)

    line 94
  4. Distinctive:

    Verify that existing tests still pass

    line 96
  5. Distinctive:

    Run the test suite

    line 110
  6. Distinctive:

    If any CRITICAL findings exist, STOP.

    line 131

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Bug Fix Workflow

Bug description: $ARGUMENTS

Provide the following information in $ARGUMENTS:

  • What is the incorrect behavior (actual)
  • What is the expected behavior
  • Steps to reproduce
  • Environment or version where the bug occurs (if known)
  • Any relevant error messages or stack traces

Web interface scope

Same web scope as cc-feature: when the fix concerns web layout, component states, feedback, motion, or a requested UI audit, apply skill web-design-engineering during test/implementation and review; keep this workflow's gates and TDD order. See cc-feature for the full scope rule.

Android phone interface scope

Same Android scope as cc-feature: when the fix concerns concrete Jetpack Compose phone UI or a requested UI audit, apply skill android-ui-design during test/implementation and review; keep this workflow's gates and TDD order. See cc-feature for the full scope rule.

Step 1 — Task Card validation (task-coach)

Invoke task-coach with the bug description above.

task-coach must produce a Task Card that includes:

  • A clear statement of actual vs. expected behavior
  • Reproduction steps (or a note that they are unknown)
  • Risk classification: low, medium, or high
  • Scope: which files or modules are likely affected

If reproduction steps are missing, task-coach must ask for them before classifying risk. A bug without a reproduction path cannot be classified reliably.

STOP here. Show the Task Card and wait for human confirmation.


Step 2 — Route by risk

Read the risk field from the Task Card and follow the corresponding route.

Low-risk route

Applies when: the bug is isolated to a single component, existing tests cover the affected code, and no public API or shared state is involved.

Route: task-coach → tester → implementer

Proceed directly to Step 3.

Medium or high-risk route

Applies when: the bug touches shared state, a public API, auth or payment paths, database writes, or the root cause is not yet understood.

Route: task-coach → architect → tester → implementer → reviewer

Invoke architect before implementation. architect must:

  • Identify the root cause (or document that it is unknown)
  • Define the fix approach and affected files
  • Flag any regression risk to adjacent components
  • Produce a Technical Plan

STOP here if high-risk. Show the Technical Plan and wait for human approval before continuing.


Step 3 — Regression tests (tester)

Invoke tester for all risk levels.

tester must:

  1. Write a regression test that reproduces the original bug (fails before the fix, passes after)
  2. Verify that existing tests still pass
  3. Produce a Coverage Summary: test added, case covered

Step 4a — Implementation, low-risk (implementer)

Invoke implementer with the Task Card and the failing regression test from Step 3. Implementer creates a Git Worktree before touching any file; all edits happen inside it.

implementer must:

  1. Locate the defect using the reproduction steps
  2. Apply the minimal fix to make the regression test pass — no unrelated changes
  3. Run the test suite
  4. Produce an Implementation Summary: root cause, fix applied, files changed

Step 4b — Implementation, medium/high-risk (implementer)

Invoke implementer with the approved Technical Plan, the Task Card, and the failing regression test from Step 3. Implementer creates a Git Worktree before touching any file; all edits happen inside it.

implementer must follow the plan exactly. Any deviation requires a new Technical Plan approval. After implementation, run the full test suite.


Step 5 — Review (reviewer) — medium/high-risk only

Invoke reviewer with the diff and Task Card.

reviewer produces a Review Report with CRITICAL / WARNING / SUGGESTION findings. If any CRITICAL findings exist, STOP. Do not close the fix until they are resolved.


Completion

Report: Task Card, Implementation Summary, regression test added, Review Report (if applicable). The fix is complete only when: the regression test passes, the full suite passes, and no CRITICAL review findings remain.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.