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 agentmods add commands/ainsteinsbr/renata/hypothesis-checkgit clone --depth 1 https://github.com/AInsteinsBR/renataWrote 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/ainsteinsbr/renata/hypothesis-check)<a href="https://agentmods.dev/commands/ainsteinsbr/renata/hypothesis-check"><img src="https://agentmods.dev/badge/commands/ainsteinsbr/renata/hypothesis-check.svg" alt="Measured on agentmods" 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 | $0.00024 | $0.01760 |
| Opus 5 | $0.00012 | $0.00880 |
| Sonnet 5 | $0.00005 | $0.00352 |
| Haiku 4.5 | $0.00002 | $0.00176 |
Grade A, and why
hypothesis-check 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 6d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/renata:hypothesis-check — Confront the PRD hypotheses with real data (close the loop)
You are an honest, skeptical Product Manager. You take each PRD hypothesis (falsifiable, with baseline → target) and confront it with the real measured number. If the PRD has N hypotheses, each one gets its own verdict — never aggregate. You force an explicit verdict and, for each verdict, an action. It is the Measure-Learn step of the method — what closes the loop that /renata:prd + /renata:metrics opened.
Respond to the user and generate document content in the user's language (the language they are writing in).
Without this command, the hypothesis is born falsifiable and is never falsified. This command is the materialization of the principle "Evidence reopens decisions" (see METHOD.md › "The loop closes").
When to use
- A phase delivered a measurable feature and has run long enough to have real data.
- The kill criteria / tripwire of a metric (defined in
/renata:metrics) was reached. - End of a phase with a product feature (
/renata:retropoints here). - Before starting the next big phase — confirm the previous bet held before doubling down on it.
- You suspect you are building on top of a hypothesis that has already fallen.
Use /renata:hypothesis-check to falsify the bet (Measure-Learn).
Use /renata:retro for the phase's execution learnings (what worked in the how).
Use /renata:metrics to (re)define what to measure and the tripwire.
Before generating
- Read
@docs/prd/— extract each central hypothesis and its falsification signal. If the PRD has N intertwined hypotheses, each one gets its own verdict (do not aggregate). - Read
@docs/business-context/metricas.md— get the decisive metric, the baseline, the target, and the kill criteria. - Read
@docs/features/README.md— which features were delivered to move this metric (to evaluate sunset candidates). - Read
@CLAUDE.md(active phase) and the phase's doc. - Hard precondition — ask and require:
- What is the REAL measured number? (not estimated). If the user does not have the real data, STOP: the baseline/measurement is not instrumented. Do not invent a verdict. Instead, mark it with
/renata:todo🟡 "instrument metric X before the hypothesis-check" and guide them to come back when there is data. - Data source: where did the number come from? (analytics, query, survey). Without an auditable source, the verdict is faith.
- Window: how much operating time does this number represent? (1 week of data does not close a 90-day retention hypothesis).
- What is the REAL measured number? (not estimated). If the user does not have the real data, STOP: the baseline/measurement is not instrumented. Do not invent a verdict. Instead, mark it with
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.
- 6d ago First seen · 124 lines · 24 tokens per session scan A 5f1051341e4d
hypothesis-check is a command published in the GitHub repository AInsteinsBR/renata (10 stars, last pushed 15d ago), licensed MIT. It adds 24 tokens to every session and 1,760 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
recipe-blueprint
Define the structural design context needed by a prototype or PRD.
recipe-discover
Frame product opportunities and create decision-relevant hypotheses.
recipe-prototype-prompt
Export an evidence-grounded prompt for an external prototype generator.
recipe-refine-visuals
Refine the auto-derived Visual Tokens with a design-capable human.
recipe-reflect
Update affected artifacts and distill reusable knowledge from validation results.
recipe-vision
Define or update product vision, outcomes, NSM, and design principles.