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/extractWrote 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/extract)<a href="https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/extract"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/extract/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/extract"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/extract.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 | $0.00030 | $0.01425 |
| Opus 5.5 | $0.00012 | $0.00570 |
| Sonnet 5.5 | $0.00006 | $0.00285 |
| Haiku 4.5 | $0.00003 | $0.00143 |
Grade A, and why
extract 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.
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.
/si:extract — Criar Skills a partir de Padrões
Transforma um padrão recorrente ou solução de debugging em uma skill standalone e portável que pode ser instalada em qualquer projeto.
Uso
/si:extract <descrição do padrão> # Extração interativa
/si:extract <padrão> --name docker-m1-fixes # Especificar nome da skill
/si:extract <padrão> --output ./skills/ # Diretório de saída personalizado
/si:extract <padrão> --dry-run # Prévia sem criar arquivos
Quando Extrair
Um aprendizado se qualifica para extração de skill quando QUALQUER uma das condições for verdadeira:
| Critério | Sinal |
|---|---|
| Recorrente | Mesmo problema em 2+ projetos |
| Não óbvio | Exigiu debugging real para descobrir |
| Amplamente aplicável | Não está vinculado a um código específico |
| Solução complexa | Correção multi-etapa fácil de esquecer |
| Sinalizado pelo usuário | "Salve isso como skill", "quero reutilizar isso" |
Fluxo de Trabalho
Passo 1: Identificar o padrão
Ler a descrição do usuário. Pesquisar a auto-memória por entradas relacionadas:
MEMORY_DIR="$HOME/.claude/projects/$(pwd | sed 's|/|%2F|g; s|%2F|/|; s|^/||')/memory"
grep -rni "<palavras-chave>" "$MEMORY_DIR/"
Se encontrado na auto-memória, use essas entradas como material de origem. Se não, use a descrição do usuário diretamente.
Passo 2: Determinar o escopo da skill
Perguntar (máximo 2 perguntas):
- "Que problema isso resolve?" (se não estiver claro)
- "Isso deve incluir exemplos de código?" (se aplicável)
Passo 3: Gerar nome da skill
Regras para nomenclatura:
- Minúsculas, hífens entre palavras
- Descritivo mas conciso (2-4 palavras)
- Exemplos:
docker-m1-fixes,api-timeout-patterns,pnpm-workspace-setup
Passo 4: Criar os arquivos da skill
Iniciar o agente skill-extractor para a geração real dos arquivos.
O agente cria:
<skill-name>/
├── SKILL.md # Arquivo principal da skill com frontmatter
├── README.md # Visão geral legível por humanos
└── reference/ # (opcional) Documentação de suporte
└── examples.md # Exemplos concretos e casos extremos
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 · 180 lines · 30 tokens per session scan A 6d4109b21620
extract is a skill published in the GitHub repository ricneves-ai/flowgrammers-skills (117 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,425 once invoked, about $0.0001 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 2026-09-03.
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