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.
git clone --depth 1 https://github.com/lucasgaravelli/pm-skills-claude-codeWrote 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/commands/lucasgaravelli/pm-skills-claude-code/acceptance-criteria)<a href="https://agentmods.dev/commands/lucasgaravelli/pm-skills-claude-code/acceptance-criteria"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/acceptance-criteria/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/commands/lucasgaravelli/pm-skills-claude-code/acceptance-criteria"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/acceptance-criteria.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 | $0.00000 | $0.02404 |
| Opus 5 | $0.00000 | $0.01202 |
| Sonnet 5 | $0.00000 | $0.00481 |
| Haiku 4.5 | $0.00000 | $0.00240 |
Grade A, and why
acceptance-criteria 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 12d 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.
This is a copy
100% identical to acceptance-criteria — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/acceptance-criteria
O que essa skill faz
Gera critérios de aceite estruturados em formato Given/When/Then (Gherkin) cobrindo happy path, edge cases, e casos de erro.
Saída: 5-10 cenários prontos para QA e eng usarem como test cases.
Quando usar
- User story aprovada, precisa detalhar validações
- Vai passar para QA — precisa ter explícito o que testar
- Feature tem regras de negócio complexas
Input esperado
Mínimo:
- Feature ou user story: O que está sendo testado
- Regras de negócio: Constraints, validações, edge cases
- Contexto: Que dados já existem
Opcional:
- Design mockup (para validações visuais)
- Casos de erro conhecidos
- Performance requirements
Processo
- Happy path — Fluxo ideal, tudo certo
- Edge cases — Variações que são válidas mas diferentes
- Error cases — Entradas inválidas, o que fazer?
- Performance/Scale — Se relevante, como se comporta em volume?
- Accessibility — Pode ser usado por screenreader?
Output
# Acceptance Criteria: [Feature Name]
## Cenário 1: Happy Path — [Descrição]
```gherkin
Given que [pré-condição/contexto inicial]
When [ação do usuário]
Then [resultado esperado]
And [validação adicional]
And [outra validação]
Notas para QA
- [Se há screenshot esperada, descreva]
- [Se há performance expectativa, mencione]
- [Qualquer detalhe visual importante]
Cenário 2: Happy Path Alternativo — [Variação]
Given que [contexto diferente do cenário 1]
When [ação similar]
Then [resultado diferente]
Notas para QA
[...]
Cenário 3: Edge Case — [Situação limite]
Given que [contexto especial]
When [ação que toca limite]
Then [comportamento esperado para limite]
Cenário 4: Error — [Erro esperado]
Given que [contexto que leva a erro]
When [ação que causa erro]
Then [mensagem de erro clara é mostrada]
And [campo fica highlighted]
And [user pode corrigir facilmente]
Cenário 5...N
[Mesmo formato]
Casos de teste por categoria
Validação de entrada
- Campo vazio
- String muito longa
- Caracteres especiais
- Números onde esperado texto
Fluxo de negócio
- Happy path completo
- Cancelamento/Back
- Reentrar dados
- Dados pré-populados
Estado do sistema
- Primeira vez usando feature
- Segunda vez (dados existem)
- Com dados muito antigos
- Em volume alto (performance)
Integração
- Com outros módulos
- Com dados de terceiros (API)
- Efeitos colaterais (emails, webhooks)
Acessibilidade
- Navegável via tab/keyboard
- Legível com screenreader
- Contraste adequate
- Mobile responsivo
Definição de Pronto
Feature está aceita quando:
- ✅ Todos os cenários passaram (manual ou automatizado)
- ✅ Sem erros em console/logs
- ✅ Performance dentro da spec
- ✅ Mobile funcionando
- ✅ PM fez sign-off em staging
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.
- 12d ago First seen · 370 lines · 0 tokens per session scan A 03f582cf8776
acceptance-criteria is a command published in the GitHub repository lucasgaravelli/pm-skills-claude-code (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,404 tokens. A static security scan graded it A with 0 findings. It is 100% identical to acceptance-criteria, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.