refine

A clean-up procedure for code changed in a feature that is already implemented and passing its tests. It proposes refactors while preserving the feature's agreed behaviour.

In plain words
What is it for?
Use it after implementation and passing tests to refactor a feature, check proposed changes against its requirements, and prepare the code for the next review stage.
Why use it?
It helps simplify changed code and improve its structure before final verification, without treating cleanup as permission to change requirements.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/swingerman/engineer/refine
Any agent
npx skills add swingerman/engineer --skill refine
Clone the repo
git clone --depth 1 https://github.com/swingerman/engineer

Made for: Claude Code, Codex.

Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,586 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.01586
Opus 5 $0.00026 $0.00793
Sonnet 5 $0.00010 $0.00317
Haiku 4.5 $0.00005 $0.00159

Measured 2d ago against content hash 790d3e98f1a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

refine 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 2d 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.

engineer/skills/refine/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

refine

Charter-bound clean-up of a feature's changed code — Checkpoint 6. Modeled on Claude Code's stock /simplify (a three-subagent parallel review) with two layers the stock skill lacks: charter validation of every proposal, and graceful breaking changes.

The behavior contract (ACs + specs) is sacred — a refactor that breaks it is a category error. New shapes are introduced alongside old, never instead of.

When to use

Checkpoint 6, after atdd:atdd-team produces passing code, before crap-analyzer (Checkpoint 7).

Verification independence: if manifest.verification.apply_to_checkpoints includes 6, refine must run on a non-implementer agent. The three review subagents (Step 2) are fresh regardless, so independence is satisfied by construction.

Not for: changing behavior (feature-edit); risk analysis (crap-analyzer); code that isn't implemented/green yet.

Workflow

Step 0 — Entry gate. Before starting, verify the prior checkpoint is complete: run ${CLAUDE_PLUGIN_ROOT}/scripts/dae_handoff.py <feature-dir> --through 5. On a non-zero exit, stop and surface the gap to the human — do not proceed.

Verify branch hygiene: run ${CLAUDE_PLUGIN_ROOT}/scripts/dae_branch.py <feature-dir>. On a non-zero exit, stop and surface the message to the human — switch branches and re-invoke. The check honors the git.manual: true manifest opt-out.

After the gate passes, show the pipeline breadcrumb: run ${CLAUDE_PLUGIN_ROOT}/scripts/dae_progress.py <feature-dir> and present its output to the human — it shows where this checkpoint sits in the DAE pipeline. The breadcrumb is advisory: a non-zero exit or a missing progress.md never blocks the skill. Then create one TodoWrite todo per workflow step below. See ${CLAUDE_PLUGIN_ROOT}/references/progress-indicator.md.

  1. Resolve + scope — resolve the methodology root + manifest via ${CLAUDE_PLUGIN_ROOT}/scripts/dae_resolve.py (see references/resolving.md); locate the feature. Scope = the feature branch's changed code (git diff against the branch point). Load feature.md, acs.md, spec.md, CHARTER.md.
  2. Dispatch three parallel review subagents (superpowers:dispatching-parallel-agents), each over the same changed code. Before dispatching, run ${CLAUDE_PLUGIN_ROOT}/scripts/dae_dup.py <methodology-root> to get deterministic project-wide duplicate findings; pass the JSON result into the Reuse subagent's context as a tool input (the subagent treats it as one signal alongside its own LLM judgment, and produces a unified set of findings — not a separate list). If dae_dup.py returns anything other than status: okstatus: unavailable (the backend tool isn't installed), status: skipped (manifest duplication.skip: true), status: unsupported (the configured backend isn't implemented in this version), or status: error (scan failed) — surface that status in the Reuse subagent's prompt and proceed with its existing LLM-only judgment.
    • Reuse — duplication (LLM + dae_dup.py findings), reinvented wheels, dead code, missed existing utilities. LSP-first lookup — at dispatch time, check whether an LSP MCP capability is available; pass that detection result and the LSP-first preference into the Reuse subagent's prompt so it uses find-references / workspace-symbols when available, with graceful fallback to grep + Read. See ${CLAUDE_PLUGIN_ROOT}/references/code-lookup.md.
    • Quality — clarity, structure, naming, incidental complexity, maintainability
    • Efficiency — redundant computation, repeated lookups, visible performance smells
  3. Consolidate — merge findings; dedup overlaps (note multi-lens hits as strong signals); keep genuine conflicts as alternatives for the human.
  4. Charter filter — check every proposal against CHARTER.md (architecture, conventions, methodology, the ACs+specs contract). Charter-violating proposals are rejected internally — never shown. This filter is the DAE-specific layer the stock skill lacks.
  5. Classify breaking changes — consumer-facing (something outside the blast radius depends on the old shape) → graceful path: deprecation-marked forwarding shim + migration note, new shape alongside old. Internal-only → hard change is fine.
  6. Present; human picks — show charter-compliant proposals (change, why, blast radius, churn). The human selects which are worth the churn.
  7. Apply — apply selected; install graceful paths where needed; re-run both test streams. Before running the test streams, ensure required infra is up: read manifest.yml's infra: section, then call ${CLAUDE_PLUGIN_ROOT}/scripts/dae_infra.py ensure <names> for each declared dependency the tests need. On a start-failed structured failure → stop and surface the diagnosis. On a missing manifest declaration for required infra → stop with the "declare in manifest" message. Any failure → revert that proposal and report.
  8. Handoff — emit a summary.

Read the full file on GitHub · 64 lines

Changes

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.

  1. 2d ago First seen · 64 lines · 51 tokens per session scan A 790d3e98f1a7

Subscribe to this mod's changes

refine is a skill published in the GitHub repository swingerman/engineer (144 stars, last pushed 6d ago), licensed MIT. It adds 51 tokens to every session and 1,586 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 2026-08-30.

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