Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/code-quality/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/code-quality)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/code-quality"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/code-quality/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/code-quality"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/code-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00036 | $0.00610 |
| Opus 5.5 | $0.00014 | $0.00244 |
| Sonnet 5.5 | $0.00007 | $0.00122 |
| Haiku 4.5 | $0.00004 | $0.00061 |
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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Quality
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="code-quality", mode="preamble") and
display the returned preamble text as a blockquote. Then
tell the user they can say "tell me more" for a step-by-step
guide, or answer the scoping questions below to proceed.
If the MCP call fails, fall back to:
Code Quality — Reviews your code for style issues, likely bugs, and structural problems in one pass.
Scoping
Before running, ask:
- Scope: "Which files or directory should I review?"
- Depth: "Quick scan, thorough, or deep review?"
- Quick: code_review only
- Thorough: code_review + bug_predict combined
- Deep: deep_review (security + quality + test gaps)
Execution
Quick scan:
code_review(path="<user-specified path>")
Thorough analysis:
code_review(path="<user-specified path>")
bug_predict(path="<user-specified path>")
Merge and deduplicate results from both tools.
Deep review (multi-pass: security, quality, test gaps):
deep_review(path="<user-specified path>")
Output Format
Prefer the rich panel. If the tool response includes panel_html,
pass it to mcp__visualize__show_widget — the universal report panel
(title, score, findings/category sections; from
attune.workflows.report_panel). It shows an explicit "did not
complete" state on failure, never a false "clean". Fall back to the
markdown below when the widget surface is unavailable.
## Code Quality Report
**Health:** X/100 | **Files:** Y | **Issues:** Z
### Issues by Category
| Category | Count | Severity |
|----------|-------|----------|
| Style | X | Low |
| Correctness | Y | High |
| Security | Z | Critical |
| Predicted Bugs | W | Medium |
### Details
| File | Line | Issue | Source |
|------|------|-------|--------|
### Predicted Bug Risks
| File | Pattern | Confidence |
|------|---------|------------|
Help
After presenting results, call:
help_lookup(topic="code-quality", mode="workflow_help")
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.
- 1mo ago First seen · 100 lines · 36 tokens per session scan A 1a37ab6872e8
code-quality is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 610 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
attest
Verifying spec compliance: extracts ACs from specs, adversarially checks conformance, generates BDD scenarios and traceability matrices. Use when impl must be proven to match a PRD/SRS/AC.
guardian
Gatekeeping Git/PR by classifying change essence and recommending granularity, naming, and strategy; the ship recipe also executes PR delivery (create, CI watch, merge, cleanup) with Ask First on merge. Use when PR preparation, commit strategy, or gated PR delivery is needed.
atlas
Analyzing dependencies, circular references, and God Classes; authoring ADRs/RFCs. Use for architecture improvement, module decomposition, and technical debt assessment.
judge
Reviewing code via multi-engine orchestration (Claude + Codex) on three axes — secure, correct, and lean — shipping only findings worth fixing. Use for PR review or pre-commit. Complements Zen.
quill
Adding JSDoc/TSDoc, updating READMEs, replacing any types with proper definitions, and adding high-value comments to complex logic. Use for documentation gaps or type safety.
zen
Refactoring code: variable naming, function extraction, magic number constants, dead code removal. Does not change behavior. Not for bugs (Judge), security (Sentinel), tests (Radar), or features (Builder).