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/gc-victor/supersimple/code-qualitynpx skills add gc-victor/supersimple --skill code-qualitygit clone --depth 1 https://github.com/gc-victor/supersimpleWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gc-victor/supersimple/code-quality)<a href="https://agentmods.dev/skills/gc-victor/supersimple/code-quality"><img src="https://agentmods.dev/badge/skills/gc-victor/supersimple/code-quality.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.00417 |
| Opus 5 | $0.00012 | $0.00209 |
| Sonnet 5 | $0.00005 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
code-quality 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 5d 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.
What it actually says
Code Quality Skill
Purpose
This skill MUST assess code across correctness, readability, architecture, security, and performance, and it SHOULD approve changes only when they clearly improve the codebase. Judge the real maintenance cost of the change, not theoretical perfection.
ReAct Framework
REASON — Is the change justified and well scoped?
- Is the core problem clear?
- Does the change solve one problem?
- Is this the simplest solution that could work?
- Is added complexity justified?
- Was reuse considered?
ACT — Is the implementation high quality?
Review these five axes:
- Correctness
- Readability
- Architecture
- Security
- Performance
REFLECT — Is the system better after the change?
- Did complexity stay flat or decrease?
- Should any cleanup happen now instead of later?
- Is knowledge preserved in understandable code?
- Is the change easy to roll back or iterate on?
Severity Model
- Critical: blocks approval
- High: serious issue, must fix
- Medium: should fix, but may not block
- Low / Nit: optional improvement
- FYI: context only
Only Critical and High issues block approval.
Change Size Guidance
- ~100 lines: good
- ~300 lines: acceptable if focused
- ~1000 lines: too large; split it
Separate refactoring from feature work when possible.
Verification
Run the relevant checks before reporting:
- lint,
- type check,
- tests,
- dependency audit,
- build.
Mark unavailable checks as N/A.
Red Flags
Treat AI-generated code as untrusted until verified; check edge cases, tests, and hidden assumptions carefully.
- speculative abstractions,
- opaque cleverness,
- missing validation,
- secrets in code,
- N+1 or unbounded work,
- oversized changes with weak justification.
Follow the ReAct framework above.
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.
- 5d ago First seen · 81 lines · 23 tokens per session scan A 592c0f6ef95d
code-quality is a skill published in the GitHub repository gc-victor/supersimple (33 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 417 once invoked, about $0.0001 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.
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…