Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ricneves-ai/flowgrammers-skillsnpx agentmods add skills/ricneves-ai/flowgrammers-skills/code-reviewerWrote 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/ricneves-ai/flowgrammers-skills/code-reviewer)<a href="https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/code-reviewer"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/code-reviewer/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/ricneves-ai/flowgrammers-skills/code-reviewer"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/code-reviewer.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 | $0.00075 | $0.01327 |
| Opus 5 | $0.00037 | $0.00664 |
| Sonnet 5 | $0.00015 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
code-reviewer 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.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer
Ferramentas automatizadas de revisão de código para analisar pull requests, detectar problemas de qualidade de código e gerar relatórios de revisão.
Sumário
Ferramentas
PR Analyzer
Analisa o git diff entre branches para avaliar a complexidade de revisão e identificar riscos.
# Analisar branch atual contra main
python scripts/pr_analyzer.py /path/to/repo
# Comparar branches específicas
python scripts/pr_analyzer.py . --base main --head feature-branch
# Saída JSON para integração
python scripts/pr_analyzer.py /path/to/repo --json
O que detecta:
- Segredos hardcoded (senhas, chaves de API, tokens)
- Padrões de injeção SQL (concatenação de strings em consultas)
- Statements de depuração (debugger, console.log)
- Desabilitação de regras ESLint
- Tipos
anydo TypeScript - Comentários TODO/FIXME
A saída inclui:
- Pontuação de complexidade (1-10)
- Categorização de risco (crítico, alto, médio, baixo)
- Priorização de arquivos para ordem de revisão
- Validação de mensagem de commit
Code Quality Checker
Analisa código-fonte para problemas estruturais, code smells e violações SOLID.
# Analisar um diretório
python scripts/code_quality_checker.py /path/to/code
# Analisar linguagem específica
python scripts/code_quality_checker.py . --language python
# Saída JSON
python scripts/code_quality_checker.py /path/to/code --json
O que detecta:
- Funções longas (>50 linhas)
- Arquivos grandes (>500 linhas)
- God classes (>20 métodos)
- Aninhamento profundo (>4 níveis)
- Muitos parâmetros (>5)
- Alta complexidade ciclomática
- Tratamento de erro ausente
- Importações não utilizadas
- Números mágicos
Limites:
| Problema | Limite |
|---|---|
| Função longa | >50 linhas |
| Arquivo grande | >500 linhas |
| God class | >20 métodos |
| Muitos parâmetros | >5 |
| Aninhamento profundo | >4 níveis |
| Alta complexidade | >10 branches |
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 · 180 lines · 75 tokens per session scan A 9eeba9234959
code-reviewer is a skill published in the GitHub repository ricneves-ai/flowgrammers-skills (112 stars, last pushed 3mo ago), licensed MIT. It adds 75 tokens to every session and 1,327 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
autoreview
Pre-commit/ship code review: Codex default; optional Claude or Pi.
omh-code-review
This is a Hermes-native code-review workflow skill.
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
code-reviewer
Code review specialist focused on patterns, bugs, security, and performance.
full-repo-review
Comprehensive four-wave review of all repo source files, producing a prioritized issue backlog.
agent-teams-simplify-and-harden
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…