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/ricneves-ai/flowgrammers-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/commands/ricneves-ai/flowgrammers-skills/hypothesis)<a href="https://agentmods.dev/commands/ricneves-ai/flowgrammers-skills/hypothesis"><img src="https://agentmods.dev/badge/commands/ricneves-ai/flowgrammers-skills/hypothesis/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/ricneves-ai/flowgrammers-skills/hypothesis"><img src="https://agentmods.dev/badge/commands/ricneves-ai/flowgrammers-skills/hypothesis.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 | $0.03282 |
| Opus 5 | $0.00000 | $0.01641 |
| Sonnet 5 | $0.00000 | $0.00656 |
| Haiku 4.5 | $0.00000 | $0.00328 |
Grade A, and why
hypothesis 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- hypothesis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/hypothesis
O que essa skill faz
Gera uma hipótese testável e estruturada com métricas de sucesso, avaliação de risco em 4 categorias (Value, Usability, Viability, Feasibility) e plano de validação. Transforma intuições vagas em statements falsificáveis com kill criteria.
Saída: Hypothesis card completa — pronta para alinhar o time e desenhar experimento.
Quando usar
- Tem uma ideia de feature e quer validar antes de construir
- Precisa estruturar o pensamento antes de pedir recursos ao time
- Quer definir critérios claros de sucesso e fracasso antes de começar
- Está usando Opportunity Solution Tree e precisa formalizar a hypothesis de uma solução
- Quer evitar o viés de confirmação — definir o que te faria desistir ANTES de testar
Input esperado
Mínimo:
- Ideia/mudança proposta: O que quer fazer ou mudar
- Usuário-alvo: Quem será impactado
- Problema que resolve: Qual dor ou necessidade endereça
Opcional:
- Dados de discovery (entrevistas, analytics)
- Baseline atual da métrica
- Constraints (timeline, budget, capacidade técnica)
- Experimentos anteriores relacionados
- Opportunity Solution Tree (se já existe)
Processo
-
Formular a crença (Belief) — Escrever a hipótese no formato: "Acreditamos que [ação/mudança] para [usuário-alvo] vai [resultado esperado] medido por [métrica] atingindo [target] em [prazo]." A hipótese DEVE ser falsificável — se qualquer resultado confirma, não é hipótese.
-
Especificar o usuário-alvo — Definir segmento específico, não "usuários". Incluir: quem é, qual o contexto, por que esse grupo especificamente. Quanto mais específico, mais útil o teste.
-
Definir resultado esperado — Descrever a mudança de comportamento do usuário (não métrica interna). Ex: "usuários vão completar onboarding" em vez de "vamos reduzir churn". O comportamento causa a métrica, não o contrário.
-
Estabelecer métricas de sucesso — Definir 3 tipos:
- Primary metric: A métrica principal que a hipótese move
- Secondary metric: Métrica complementar que confirma o mecanismo
- Guardrail metric: Métrica que NÃO pode piorar (efeito colateral)
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.
- 8d ago First seen · 321 lines · 0 tokens per session scan A 3642b66b4cf9
hypothesis is a command published in the GitHub repository ricneves-ai/flowgrammers-skills (112 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,282 tokens. 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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checklist
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clarify
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specify
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converge
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implement
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analyze
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