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 skills add fusengine/agents --skill code-qualitygit clone --depth 1 https://github.com/fusengine/agentsWrote 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/fusengine/agents/code-quality)<a href="https://agentmods.dev/skills/fusengine/agents/code-quality"><img src="https://agentmods.dev/badge/skills/fusengine/agents/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/fusengine/agents/code-quality"><img src="https://agentmods.dev/badge/skills/fusengine/agents/code-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 85 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00041 | $0.01553 |
| Opus 5.5 | $0.00016 | $0.00621 |
| Sonnet 5.5 | $0.00008 | $0.00311 |
| Haiku 4.5 | $0.00004 | $0.00155 |
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 8d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
It validates quality AFTER the fact -- it does not confirm the original problem was actually solved. Run the verification skill first to confirm functional resolution, then code-quality to confirm the fix is clean.
Code Quality Skill
Canonical workflow definition: agents/sniper.md (this skill and the sniper agent share the same 7-phase workflow — update both together).
🚨 MANDATORY 7-PHASE WORKFLOW
PHASE 1: Exploration (explore-codebase) → BLOCKER
PHASE 2: Documentation (research-expert) → BLOCKER
PHASE 3: Impact Analysis (Grep usages) → BLOCKER
PHASE 3.5: DRY Detection (jscpd duplication) → NON-BLOCKING
PHASE 4: Error Detection (linters)
PHASE 5: Precision Correction (with docs + impact + DRY)
PHASE 6: Verification (re-run linters, tests, duplication)
CRITICAL: Phases 1-3 are BLOCKERS. Never skip them. DRY: Phase 3.5 is non-blocking but findings inform Phase 5 corrections.
PHASE 1: Architecture Exploration
Launch explore-codebase agent FIRST:
> Agent(subagent_type="fuse-ai-pilot:explore-codebase", prompt="...")
Gather:
- Programming language(s) detected
- Existing linter configs (.eslintrc, .prettierrc, pyproject.toml)
- Package managers and installed linters
- Project structure and conventions
- Framework versions (package.json, go.mod, Cargo.toml)
- Architecture patterns (Clean, Hexagonal, MVC)
- State management (Zustand, Redux, Context)
- Interface/types directories location
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago Changed 218ab7139dcf
- 1mo ago First seen · 171 lines · 41 tokens per session scan A de033a16e0a3
code-quality is a skill published in the GitHub repository fusengine/agents (33 stars, last pushed 8d ago), licensed MIT. It adds 41 tokens to every session and 1,553 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
radin-review
Review a commit, PR, directory or range for the simplest native solution, maintainability and documented repo rubrics, triage findings with the user. Use for /radin-review, "review and log to backlog".
reviewing-code
Reviews code. Use when reviewing code implementations, assessing code quality, running a security review, checking spec or design system compliance, or conducting an end-of-build whole-project review against PRD success criteria.
assessing-quality
Conducts quality reviews of non-code artefacts. Use when reviewing or critiquing Skills, agents, workflows, documentation, or written materials, assessing quality against standards, or providing structured feedback.
code
Review code through the lens of a senior engineer.
gemini
Cross-model second opinion from Google Gemini — a different AI reviewing the same changes, with deep Google ecosystem knowledge. Three modes: review (pass/fail gate for Google Ads campaigns, SEO metadata, or code), challenge (adversarial stress-test that tries to break your changes), and consult (open Q&A with Gemini…
boundaries
Analyze Phoenix context boundaries and module coupling via mix xref. Use when checking cross-context calls, validating dependencies, before splitting modules, or reviewing architecture.