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/refo/claude-dev-helpers/qanpx skills add refo/claude-dev-helpers --skill qagit clone --depth 1 https://github.com/refo/claude-dev-helpersWhat 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 | $0.00058 | $0.01063 |
| Opus 5 | $0.00029 | $0.00531 |
| Sonnet 5 | $0.00012 | $0.00213 |
| Haiku 4.5 | $0.00006 | $0.00106 |
Grade A, and why
qa 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.
This is a copy
100% identical to qa — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Session
Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language.
For each issue the user raises
1. Listen and lightly clarify
Let the user describe the problem in their own words. Ask at most 2-3 short clarifying questions focused on:
- What they expected vs what actually happened
- Steps to reproduce (if not obvious)
- Whether it's consistent or intermittent
Do NOT over-interview. If the description is clear enough to file, move on.
2. Explore the codebase in the background
While talking to the user, kick off an Agent (subagent_type=Explore) in the background to understand the relevant area. The goal is NOT to find a fix — it's to:
- Learn the domain language used in that area (check UBIQUITOUS_LANGUAGE.md)
- Understand what the feature is supposed to do
- Identify the user-facing behavior boundary
This context helps you write a better issue — but the issue itself should NOT reference specific files, line numbers, or internal implementation details.
3. Assess scope: single issue or breakdown?
Before filing, decide whether this is a single issue or needs to be broken down into multiple issues.
Break down when:
- The fix spans multiple independent areas (e.g. "the form validation is wrong AND the success message is missing AND the redirect is broken")
- There are clearly separable concerns that different people could work on in parallel
- The user describes something that has multiple distinct failure modes or symptoms
Keep as a single issue when:
- It's one behavior that's wrong in one place
- The symptoms are all caused by the same root behavior
4. File the GitHub issue(s)
Create issues with gh issue create. Do NOT ask the user to review first — just file and share URLs.
Issues must be durable — they should still make sense after major refactors. Write from the user's perspective.
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 · 131 lines · 58 tokens per session scan A d9727aac6503
qa is a skill published in the GitHub repository refo/claude-dev-helpers (2 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 1,063 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to qa, differing in 0 lines, and is treated as a copy.
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.
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.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…