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.
npx agentmods add skills/fradser/dotclaude/request-refactor-plannpx skills add FradSer/dotclaude --skill request-refactor-plangit clone --depth 1 https://github.com/FradSer/dotclaudeWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00053 | $0.00532 |
| Opus 5 | $0.00026 | $0.00266 |
| Sonnet 5 | $0.00011 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
request-refactor-plan 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- request-refactor-plan — 89% identical, 4 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
- request-refactor-plan — 88% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Go through the steps below. Skip steps not considered necessary.
-
Ask the user for a long, detailed description of the problem they want to solve and any potential ideas for solutions.
-
Explore the repo to verify their assertions and understand the current state of the codebase.
-
Ask whether they have considered other options, and present other options to them.
-
Interview the user about the implementation. Be extremely detailed and thorough.
-
Hammer out the exact scope of the implementation. Work out what you plan to change and what you plan not to change.
-
Look in the codebase to check for test coverage of this area of the codebase. If there is insufficient test coverage, ask the user what their plans for testing are.
-
Break the implementation into a plan of tiny commits. Remember Martin Fowler's advice to "make each refactoring step as small as possible, so that you can always see the program working."
-
Create a GitHub issue with the refactor plan. Use the following template for the issue description:
Problem Statement
The problem that the developer is facing, from the developer's perspective.
Solution
The solution to the problem, from the developer's perspective.
Commits
A LONG, detailed implementation plan. Write the plan in plain English, breaking down the implementation into the tiniest commits possible. Each commit should leave the codebase in a working state.
Decision Document
A list of implementation decisions that were made. This can include:
- The modules that will be built/modified
- The interfaces of those modules that will be modified
- Technical clarifications from the developer
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions
Do NOT include specific file paths or code snippets. They may end up being outdated very quickly.
Testing Decisions
A list of testing decisions that were made. Include:
- A description of what makes a good test (only test external behavior, not implementation details)
- Which modules will be tested
- Prior art for the tests (i.e. similar types of tests in the codebase)
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.
- 2d ago First seen · 69 lines · 53 tokens per session scan A 5e09576bf645
request-refactor-plan is a skill published in the GitHub repository FradSer/dotclaude (588 stars, last pushed 21d ago), licensed MIT. It adds 53 tokens to every session and 532 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.