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 cmaranho/rn-agent-skills --skill rn-code-analysisgit clone --depth 1 https://github.com/cmaranho/rn-agent-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/cmaranho/rn-agent-skills/rn-code-analysis)<a href="https://agentmods.dev/skills/cmaranho/rn-agent-skills/rn-code-analysis"><img src="https://agentmods.dev/badge/skills/cmaranho/rn-agent-skills/rn-code-analysis/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/cmaranho/rn-agent-skills/rn-code-analysis"><img src="https://agentmods.dev/badge/skills/cmaranho/rn-agent-skills/rn-code-analysis.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 | $0.00057 | $0.00457 |
| Opus 5.5 | $0.00023 | $0.00183 |
| Sonnet 5.5 | $0.00011 | $0.00091 |
| Haiku 4.5 | $0.00006 | $0.00046 |
Grade A, and why
rn-code-analysis 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.
What it actually says
React Native — Análise de Código
Objetivo
Mapear problemas de qualidade, risco e aderência a padrões, priorizados por impacto.
Quando utilizar
- Auditar uma base ou módulo.
- Preparar/embasar um code review aprofundado.
- Priorizar dívida técnica e riscos.
Conhecimentos necessários
- Linters/analisadores estáticos (ex.: ESLint,
tsc, ferramentas de complexidade). - Code smells e métricas (complexidade ciclomática, acoplamento, duplicação).
- Padrões arquiteturais do projeto e limites de camada.
- Vetores de risco (segurança, performance, acessibilidade).
Fluxo de execução
- Rode análise estática (lint, typecheck) e colete métricas.
- Mapeie a arquitetura real vs a esperada; identifique violações de camada.
- Detecte smells: duplicação, funções longas,
any, acoplamento, dead code. - Sinalize riscos de performance, segurança e acessibilidade.
- Priorize por impacto × esforço e proponha ações objetivas.
Boas práticas
- Baseie achados em evidência (arquivo/linha/métrica).
- Separe crítico de sugestão; foque no que muda o resultado.
- Prefira automação (regra de lint) a checagem manual recorrente.
- Não altere comportamento durante a análise.
Armadilhas comuns
- Nitpicks estilísticos ofuscando problemas reais.
- Achados sem prioridade nem impacto.
- Ignorar contexto/histórico do código.
- Confundir "diferente do meu gosto" com "defeito".
Critérios de sucesso
- Lista priorizada e acionável de achados, com evidência.
- Riscos críticos identificados.
- Recomendações executáveis e, quando possível, automatizáveis.
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 · 52 lines · 57 tokens per session scan A f2ba17e0248a
rn-code-analysis is a skill published in the GitHub repository cmaranho/rn-agent-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 457 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 2026-08-31.
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