Fix workflow

/fix-workflow

You are the orchestrator for a cleanup cluster. Unlike /find-duplication, /find-dormant, and /find-semantic-duplication — which are detection pipelines — this is the execution skill. It writes code, runs tests, and produces commits.

Invocation is not blanket authorization. For Form A (cluster ID from a triage report), the user has authorized the specific cluster by referencing it. For Forms B and C, you must present an explicit plan and wait for user confirmation before any edits — see Argument Parsing below. Authorization for one cluster is never authorization for follow-on clusters or adjacent fixes.

How success is judged

• A regression/characterization test exists before the fix and is green at commit time (R2; §2d writes the failing test first).
• The behavior-preserving refactor commit is separate from any bug-fix commit (R1).
• The verification matrix ran green for the touched subsystem (Step 3) — plus the jscpd re-scan for dedup shapes (R14).
• The playbook's stop condition has every box checked; no commit otherwise. Work toward these gates from Step 1.

Procedural detail lives in three knowledge files:

• knowledge/fix-shapes.md — Step-2 playbooks. Every shape routes there except workflow registry cleanup, which uses the inline checklist in this file. Read only the section matching the classified cluster.
• knowledge/verification.md — worktree + cleanliness guard commands, test matrix (host-adapter), commit verbs + message template, jscpd re-scan command.
• knowledge/learnings.md — 14 rules from prior clusters (R1–R14). Read on ambiguity; don't front-load.
• .claude/skills/_common/interface-depth.md — read when the cluster extracts a helper/service, promotes a canonical interface, or adds an adapter seam.

Argument parsing

The argument takes three forms. Detect which and route:

Form A — Cluster ID from a triage report

Pattern: cluster:<name>, delete:<name>, fix:<name>, semantic:<name>, layer:<name>, or a short id like P0-1, P1-agent-extract.

• delete:<name> / fix:<name> → load reports/dormant/latest/report.md
• layer:<name> → load reports/layer-violation/latest/report.md (emitted by /find-layer-violation; per-candidate evidence at scout/<candidate_id>.json, machine view in findings.json).
• semantic:<name> → load reports/semantic-duplication/latest/triage.md (emitted by /find-semantic-duplication; finding IDs look like semantic:SC-1, semantic:SC-2 — the prefix is SC- because the collapse stage in that skill re-numbers globally, see .claude/skills/find-semantic-duplication/scripts/collapse_candidates.py). The shape is always semantic-duplication — route to fix-shapes.md §2a (pure / three-way clone) or §2b (shadow helper), using the triage's capability_matrix + confirm notes to decide which canonical survives and which are shadows.
• cluster:<name> or P0/P1/P2 ID → load reports/duplication/latest/triage.md

If the referenced report file does not exist, abort and tell the user which detection skill to run first (/find-dormant, /find-duplication, /find-semantic-duplication, or /find-layer-violation) — do NOT fall back to scanning the codebase.

Find the matching cluster, extract file list + fix shape + helper name. Proceed to Step 1. If no exact ID match exists in the loaded report, list the available IDs and abort — do not fuzzy-match to the closest-looking entry.

Form B — File path

Pattern: matches an existing path in the repo (birth-host example: core/services/parse_json_body_helper.py).

Treat the file as the scope. No triage context — investigate from scratch, answering at minimum: who calls each suspect symbol (grep all call sites); where the duplicate or suspect bodies actually diverge; whether a canonical equivalent already exists; and what tests cover the area. Before any edits, run Step 1 on that scope and present its execution plan (file list, fix shape, helper name) to the user; wait for confirmation.

Form C — Free-form description

Anything else — a sentence describing what to clean up.

The description is your brief. Run Step 1 on it — the execution plan it produces (file list, fix shape, expected changes per file) is what you present. Present this plan and wait for explicit user confirmation before making any edits. Ask for clarification if target file(s) or fix shape can't be inferred.

Approval-token contract (Forms B & C)

When waiting for user confirmation, accept the reply as approval only if the first non-whitespace token is one of: approved, approve, go, ship it, lgtm, proceed, yes. Substring matches on the approval word elsewhere in the sentence do NOT count ("looks ok but defer the helper rename" is a change request, not approval). Any conjunction on the same turn (but, except, however, only if, defer) flips the reply to a change request. When in doubt, ask for a clean approval token — do not guess intent.

Scope

• Worktree: run wherever invoked. Confirm with git rev-parse --show-toplevel before starting.
• Python: .venv/bin/python. Never bare python.
• Cleanliness guard: target files must not carry unrelated uncommitted edits before starting. knowledge/verification.md has the exact commands and abort conditions.

Step 1 — Load context and classify the cluster

Pre: a cluster reference (Form A/B/C resolved). Post: a written plan with target files, shape, helper name, test modules.

Read the target files in full (not just the triage line ranges). Don't trust triage as exhaustive.

Classify into one of these shapes:

Shape	Detection	Playbook
Pure duplication	2+ methods, 90%+ identical	fix-shapes.md §2a
Three-way+ clone	3+ near-identical copies	fix-shapes.md §2a
Policy-flag clone	2 methods, differ on one branch	fix-shapes.md §2a
Template triplication	same pattern N≥3 with minor vars	fix-shapes.md §2a
Shadow helper	function mirrors canonical	fix-shapes.md §2b
Dead code	zero inbound references	fix-shapes.md §2c
Quasi-dead / broken	silently-broken, no tests	fix-shapes.md §2d
Workflow registry cleanup	workflow step, boot payload, or endpoint knowledge repeated across executable layers	checklist below
Extract service (layer violation)	entry point owns business logic — from /find-layer-violation	fix-shapes.md §2a applied at service scope + _common/interface-depth.md; if the extraction spans multiple commits/files, hand off to /refactor-subsystem

Write a one-paragraph execution plan to stdout:

• Target file(s) and line ranges
• Fix shape (from table)
• Helper name (if any) + where it lives (module vs class)
• Interface-depth note for any new helper/service/adapter: deletion test, caller knowledge removed, intended test surface, adapter count
• Test file(s) covering the area, new tests to add
• Expected LOC delta
• Adjacent dead code spotted while reading — do NOT fold in; emit as a follow-on finding

Form A → plan is a self-check; proceed after writing it. Forms B/C → the plan was already produced and approved during argument parsing (Step 1 ran early); don't re-present it or wait for a second confirmation.

Step 2 — Execute the fix shape

Pre: plan written. Post: code edits + test runs complete; no commit yet.

Read the matching section of knowledge/fix-shapes.md (2a / 2b / 2c / 2d) and follow it end to end. Each playbook has an explicit stop condition — do not commit unless you can check every box. (The workflow-registry shape uses the checklist below instead; its stop condition follows the checklist.)

Workflow registry cleanup checklist

Use this checklist when the cleanup centralizes product workflow authority rather than local code clones:

• Write boot-payload characterization tests before production edits. For endpoint registries, assert every key equals reverse(...).
• Keep URL route definitions and view names unchanged unless the user explicitly authorized route migration.
• Migrate only active consumers loaded by current templates; name any deferred dynamic, field-specific, or ai-sidecar endpoints as follow-up scope rather than pulling them in.
• For site-scoped API consumers, add registry-owned static endpoints and, when needed, template endpoints with explicit placeholder names. Frontend JS should call boot helpers instead of string-building /api/sites/<site_id>/....
• Preserve responsive desktop/mobile behavior and existing template ownership. Bump cache-busting query strings for every touched JS include.
• Add or update a diff-scoped guard when the migrated pattern can recur, e.g. JS endpoint-sprawl lint with good/bad fixtures.
• Verification must include the host's site-workflow tests (birth host: the site workflow Django tests and, when a dev server is running, testing/test_site_pages.py).
• Do not touch the host's AI-sidecar surface (birth host: core/services/ai_sidecar/) or unified AI workflow behavior unless the user explicitly scopes that work in.

Stop condition (workflow registry cleanup)

• Boot-payload characterization tests written before production edits, and green.
• Every endpoint-registry key asserted equal to its reverse(...) (or the host router's equivalent).
• Cache-busting bumped on every touched JS include.
• Diff-scoped guard added, or deferred with a named reason in the cluster entry.
• URL routes and view names unchanged, or the user explicitly authorized route migration.

Step 3 — Verification test matrix

Pre: edits complete, playbook's stop condition satisfied. Post: targeted test suites green.

Run the right tests for the cluster, not every test in the repo. The matrix lives in knowledge/verification.md (baseline + per-subsystem rows). If the host table is unfilled, follow its absence fallback — run the narrowest meaningful suite for the touched subsystem and name the choice in your plan. If unsure, run the superset for the file's subsystem.

Post-cluster jscpd re-scan (dedup-shape clusters only)

After the refactor lands, re-run jscpd on the touched subdir and diff the clone count against reports/duplication/latest/jscpd/. Command + rationale in knowledge/verification.md. Fewer clones = the refactor landed (R14).

Step 4 — Commit discipline

One commit per logical unit. Two-commit clusters happen when:

• A refactor surfaces a latent bug (refactor commit + separate fix commit — R1).
• A canonical gap needs filling (Promote commit + Migrate commit — §2b-ii).
• Dead-code deletion reveals a dependent cleanup (Delete commit + dependent cleanup commit).

Verb conventions (Dedup / Delete / Fix / Promote / Migrate) and the commit-message template live in knowledge/verification.md.

Git safety:

• Never --amend a previous commit unless the user asked. New commit instead.
• Never --no-verify. Fix the hook failure.
• Never reset --hard or checkout --. Stage/commit only the files you intended to touch.
• Never push to remote.
• Run git status + git diff --stat before committing to confirm the file list matches your plan.

Step 5 — Write the cluster learnings entry

Write a cluster entry and present it in your closing reply — it is the run's record: Step 6 adds follow-on findings to it, and Step 7's recommendation and the user's next-cluster choice consume it. Entry format:

## Cluster N: <name> (P0/P1/P2)

**Date:** <YYYY-MM-DD>
**Commit:** `<sha>` — "<commit message title>"
**Type:** Duplication / Dead code / Quasi-dead / Shadow helper / ...

### What was flagged
<from /find-duplication or /find-dormant — what the detector said>

### What was actually true
<after investigation — did the detector get it right? what did the
detector miss?>

### What changed
<files, LOC delta, helpers added/removed — table if multi-file>

### Tests
<suites run, new tests added, coverage gaps that remain>

### Skill-worthy patterns
<distilled lessons for future audits. Lead with the observation,
then "Why:" and "How to apply:" where relevant.>

If the cluster taught something not already in the skill's knowledge/learnings.md R1–R14, call it out in the entry — the user decides whether to update the skill.

Append an effectiveness log entry so reports/_meta/dashboard.md can track which shapes are being cleaned up over time. findings_total is always 1 (one cluster per run); buckets keys on the shape with value 1. See .claude/skills/_common/skill-conventions.md for the schema.

python3 scripts/log_effectiveness.py \
  --skill fix-workflow \
  --scan-id "cluster-$(git rev-parse --short HEAD)" \
  --target <primary-target-file> \
  --findings-total 1 \
  --buckets '{"<shape>": 1}' \
  --notes "<cluster-name> → <commit-sha>"

Where <shape> is one of: dedup, delete, fix, promote, migrate, shadow, mapped from the Step-1 shape:

Step-1 shape	bucket
Pure duplication / Three-way+ clone / Policy-flag clone / Template triplication	dedup
Shadow helper	shadow
Dead code	delete
Quasi-dead / broken	fix
Workflow registry cleanup	migrate
Extract service (layer violation)	promote

Step 6 — Surface follow-on findings

While executing, you may have noticed:

• Adjacent dead code — emit as a /find-dormant candidate. Don't delete in this commit.
• Drift between clone copies (inconsistent error messages, inconsistent log strings) — emit as "drift cleanup candidate".
• Silent except Exception near the target — emit as "latent bug risk".
• Tests that would have caught an issue but didn't exist — emit as "coverage gap".

Write these to a ## Follow-on findings section in the learnings entry. They are not TODOs for you — they inform the user's next cluster choice (R11).

Step 7 — Recommend next action

End with a short recommendation that covers both preventing recurrence and what to fix next:

1. Prevent recurrence. If the fix shape generalizes (silent-catch, bare-int-on-request, shadow helper, etc.), suggest: /prevent-regression cluster:<id> — it produces a proposal for a diff-scoped lint rule + fixture + CLAUDE.md Canonical Patterns entry, bundled into one commit. Skip this recommendation when the fix was obviously one-off (a single-site data bug, a typo) — two clusters justify one rule, not a family.
2. Class lift. Name the fixed defect's class in one sentence, define the cheapest detector for it (usually a grep), and RUN the detector across the codebase before closing. Paste the hit count in the recommendation. Siblings found → name them as one batch sweep candidate, not N future clusters; class mechanizable → that is the /prevent-regression candidate from item 1 (the two-clusters-justify-one-rule threshold gates the lint, not the detector run — running the detector is free).
3. Next cluster. Then pick one of:
   • More clusters in the same triage → point at the next.
   • File is now fully dedup'd → suggest /find-duplication on an adjacent file or sub-package.
   • Follow-on findings surfaced → suggest /find-dormant to validate them.
   • Worktree blocking on concurrent edits in main → surface it explicitly.

Do NOT start the next cluster automatically. Each cluster is a separate authorization — and /prevent-regression is its own authorization too; it builds a proposal but does not commit.

Non-goals

• Starting the next cluster automatically.
• Refactoring code adjacent to the cluster target.
• Running the full repo test suite (use knowledge/verification.md subsystem mapping).
• Editing files in the main worktree (concurrency guard).
• Creating documentation unless the user explicitly asks.
• Updating .claude/skills/*/SKILL.md as part of a cluster commit — skill files evolve on their own cadence.

Failure modes and recovery

• Test failure after refactor: do not commit. Read the test output, identify the failing assertion, Read the affected code. <5 min fix → fix it. Otherwise abort and report the exact test+assertion to the user with the current file state.
• Behavior change detected post-commit: create a revert commit, not git reset. Reverts preserve history and signal intentional backout.
• Concurrency collision with main worktree: git status shows conflicting files. Abort. Do not rebase or merge.
• Helper contract is wrong mid-refactor: revise the helper. Do not add a flag to paper over the mismatch — the right shape may be two helpers (R12).
• Silent bug survived the refactor: means the refactor was behavior-preserving (correct) but the code was broken from Day 1. Commit the refactor; file a new cluster for the fix. Don't combine.

Repository layout

.claude/skills/fix-workflow/
├── SKILL.md                    # this file — orchestrator
└── knowledge/                  # loaded on demand, not front-to-back
    ├── fix-shapes.md           # Step-2 playbooks (registry checklist lives above)
    ├── verification.md         # guard commands, test matrix, commit template, jscpd
    └── learnings.md            # R1–R14 from prior clusters