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 lubochka/xiigen-general-skills --skill generated-code-reviewgit clone --depth 1 https://github.com/lubochka/xiigen-general-skillsWrote 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/lubochka/xiigen-general-skills/generated-code-review)<a href="https://agentmods.dev/skills/lubochka/xiigen-general-skills/generated-code-review"><img src="https://agentmods.dev/badge/skills/lubochka/xiigen-general-skills/generated-code-review/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/lubochka/xiigen-general-skills/generated-code-review"><img src="https://agentmods.dev/badge/skills/lubochka/xiigen-general-skills/generated-code-review.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 | $0.00027 | $0.00188 |
| Opus 5 | $0.00014 | $0.00094 |
| Sonnet 5 | $0.00005 | $0.00038 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
generated-code-review 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 9d 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
Generated Code Review
Purpose
Review generated code before trusting it.
When to Use
Use after AI-generated code, scaffolding, migration output, or copied snippets.
Inputs
Generated files, expected behavior, APIs, tests, security constraints.
Steps
- Inspect logic and API compatibility.
- Check error handling and security-sensitive paths.
- Run relevant verification.
- Remove unused or hallucinated code.
Gates
Generated output is reviewed as code, not accepted as source of truth.
Evidence
Review findings, tests, build checks, or manual inspection notes.
Anti-Patterns
Assuming generated code is correct because it is syntactically plausible.
Related
review-fix-loop, code-examination
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.
- 9d ago First seen · 34 lines · 27 tokens per session scan A 172956637981
generated-code-review is a skill published in the GitHub repository lubochka/xiigen-general-skills (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 188 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 2026-08-31.
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