Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add kitchen-engineer42/joharnessburg/plugin install johnWrote 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/kitchen-engineer42/joharnessburg/code-quality-guardrails)<a href="https://agentmods.dev/skills/kitchen-engineer42/joharnessburg/code-quality-guardrails"><img src="https://agentmods.dev/badge/skills/kitchen-engineer42/joharnessburg/code-quality-guardrails/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/kitchen-engineer42/joharnessburg/code-quality-guardrails"><img src="https://agentmods.dev/badge/skills/kitchen-engineer42/joharnessburg/code-quality-guardrails.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00134 | $0.02418 |
| Opus 5 | $0.00067 | $0.01209 |
| Sonnet 5 | $0.00027 | $0.00484 |
| Haiku 4.5 | $0.00013 | $0.00242 |
Grade A, and why
code-quality-guardrails 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
code-quality-guardrails
The produced app is the deliverable. The user trusts it not to leak credentials, not to ship debug noise, not to crash on the first run. This skill is the discipline that makes that trust possible — adapted from a production app-builder's quality patterns: inherit the methods, but skill-ify them rather than hardcoding a pipeline.
The principle: deterministic checks first, LLM repair second.
The pattern
When you're about to ship produced-app code (end of a build/polish phase, before deploy, or any time the user signals "is this ready?"):
- Run the deterministic checks. Grep for leaked secrets, check the build, verify imports resolve, lint, smoke-test the entrypoint. These are cheap, fast, predictable. They catch the bulk of real issues. See
references/common-guardrails.mdfor categories. - Apply automated fixes where possible. Dependency missing → install. Import path wrong → fix the path. Leaked secret in a string → flag for user (do NOT auto-redact without confirmation; you might break a config). Many guardrails have obvious fixes; apply them.
- For residual issues, dispatch the cross-validation subagent. A separate reviewer reads the produced code + the design intent (from PLAN.md), flags issues a grep can't catch (subtle UX bugs, missing error states, security-via-obscurity, etc.). See
references/cross-validation-pattern.md. - Surface to the user anything still unresolved after steps 1-3.
The reason for the order: deterministic checks are cheap and reliable; LLM checks are expensive and probabilistic. Spend the cheap ones first; reserve the expensive ones for what they're uniquely good at.
When a guardrail fires but the fix isn't obvious
Deterministic checks are good at pattern matching, not at semantic judgment. When a guardrail fires, decide:
- Check context. Is
api_keyin a comment? In a.env.exampleplaceholder? In a test config? In production? Same pattern, different decisions. - If context is ambiguous, flag to user with the match + line number. Don't auto-fix.
- If context is clear, fix and log. "Leaked sk-* in committed file" is unambiguous; "missing dep in package.json that imports require" is unambiguous; fix.
What ships with it
3 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 First seen · 143 lines · 134 tokens per session scan A 7200899f8e55
code-quality-guardrails is a skill published in the GitHub repository kitchen-engineer42/joharnessburg (9 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 2,418 once invoked, about $0.0007 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 2026-08-31.
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