refactor-module

A refactoring skill that splits one large plain-language module into smaller modules grouped by related concerns. A requires chain connects the resulting modules so their behavior stays intact.

In plain words
What is it for?
Use it when a module covers several distinct areas or when the user explicitly asks to reorganize it. The split is structural and should not change its functional specifications.
Why use it?
It makes a module easier to understand and extend when unrelated features have grown together.

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/plainlang/plain-forge/refactor-module
Any agent
npx skills add plainlang/plain-forge --skill refactor-module
Clone the repo
git clone --depth 1 https://github.com/plainlang/plain-forge

Made for: Claude Code, Codex.

Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,359 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 $0.00060 $0.02359
Opus 5 $0.00030 $0.01179
Sonnet 5 $0.00012 $0.00472
Haiku 4.5 $0.00006 $0.00236

Measured 2d ago against content hash b556ea79bbc0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

refactor-module 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.

forge/skills/refactor-module/SKILL.md · 198 lines

How it starts

The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Refactor Module

Always use the skill load-plain-reference to retrieve the ***plain syntax rules — but only if you haven't done so yet.

When to Use

  • A module has many functional specs spanning multiple distinct domains or feature areas.
  • The module is hard to reason about because unrelated concerns are interleaved.
  • New features are difficult to add because the module's scope is too broad.
  • The user explicitly asks to split or reorganize a module.

Guiding Principle

The refactoring is purely structural. The combined behavior of the resulting modules must be identical to the original module. No functional spec is added, removed, or weakened. The split is semantic — grouping related specs together — not behavioral.

Input

The .plain file to refactor, plus guidance from the user on the desired grouping (or ask the user to confirm your proposed grouping).

Phase 1 — Analyze the Current Module

  1. Read the entire .plain file — frontmatter, definitions, implementation reqs, test reqs, and all functional specs. Also read any import and requires chains to understand the full context.
  2. Inventory the functional specs. Number each spec and note which :Concepts: it references. Build a dependency map: which specs depend on earlier specs (by referencing behavior or state they introduced)?
  3. Inventory the definitions. For each concept, note which functional specs reference it. Identify concepts that are used across multiple logical groups vs. concepts used only within one group.
  4. Identify logical groups. Look for natural seams — clusters of functional specs that share concepts, represent a cohesive domain, or describe a single feature area. Common groupings:
    • Data model setup and CRUD operations
    • Business logic and processing rules
    • UI screens and navigation
    • Integrations and external interfaces
    • Statistics, reporting, and analytics
  5. Verify group independence. Each group's specs should form a contiguous or near-contiguous block in chronological order. If a group's specs are scattered and interleaved with other groups, the split boundary may need adjustment.

Read the full file on GitHub · 198 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 · 198 lines · 60 tokens per session scan A b556ea79bbc0

Subscribe to this mod's changes

refactor-module is a skill published in the GitHub repository plainlang/plain-forge (55 stars, last pushed 4d ago), licensed MIT. It adds 60 tokens to every session and 2,359 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens