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 skills add shinpr/nautilus --skill hypothesis-disciplinegit clone --depth 1 https://github.com/shinpr/nautilusWrote 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/shinpr/nautilus/hypothesis-discipline)<a href="https://agentmods.dev/skills/shinpr/nautilus/hypothesis-discipline"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/hypothesis-discipline/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/shinpr/nautilus/hypothesis-discipline"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/hypothesis-discipline.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 | $0.00045 | $0.00756 |
| Opus 5 | $0.00023 | $0.00378 |
| Sonnet 5 | $0.00009 | $0.00151 |
| Haiku 4.5 | $0.00005 | $0.00076 |
Grade A, and why
hypothesis-discipline 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypothesis Discipline
Core Principle
A hypothesis is not a guess. A hypothesis selected for validation needs a clear decision, evidence criteria, and a stopping condition.
Hypothesis Characteristics
- Hypotheses exist at every level of the OST hierarchy (see product-principles skill)
- Each hypothesis has a target level attribute indicating which OST level it addresses
- Hypotheses follow an ADR-style lifecycle — a single file tracks the full journey from draft to conclusion
- Preserve rejected and invalidated hypotheses as learning assets
Hypothesis Lifecycle
draft → testing → validated → adopted
→ rejected (validated but not adopted)
→ invalidated (disproven by evidence)
→ inconclusive (evidence gathered but insufficient to confirm or deny)
→ timeout (deadline passed, decision needed: continue or stop)
Hypothesis File Schema
The authoritative schema is defined in references/hypothesis-template.md. Key fields:
id: HYPO-NNNlevel: outcome / opportunity / solution / assumptionstatus: draft / testing / validated / invalidated / inconclusive / adopted / rejected / timeoutconfidence: scores for the risk dimensions that can change the hypothesis decision, on the product-principles 0-10 scaletime-budgetanddeadline: include when time or calendar limits affect the validation decision
Validation Criteria Requirements
A hypothesis entering validation defines:
- We believe that — the hypothesis statement
- We'll know we're right when — measurable success criteria
- We'll know we're wrong when — measurable failure criteria
- Validation method — how we will test (prototype, data analysis, interview, code spike, market research)
- Stopping condition — the evidence, time limit, or cost limit that ends this validation
Time Budget and Cutoff
- Give a validation a time budget when exploration or evidence collection can expand
- Add a deadline when a calendar cutoff changes the decision
- When deadline passes without conclusion → status becomes
timeout - Timeout forces a decision: extend (with justification), pivot, or abandon
- End validation at its evidence, time, or cost boundary
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 76 lines · 45 tokens per session scan A 5093b5382c64
hypothesis-discipline is a skill published in the GitHub repository shinpr/nautilus (4 stars, last pushed 8d ago), licensed MIT. It adds 45 tokens to every session and 756 once invoked, about $0.0002 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 skills, from other repositories
productspec
Use when implementing, reviewing, planning, or changing work governed by a Product Spec. Treat .product-spec.md files as the product contract for the work.
productspec-authoring
Writes, validates, and converts ProductSpec files (.product-spec.md), the Markdown format for recording product intent before implementation. Use when authoring a new Product Spec, converting an existing PRD or feature doc into one, validating spec files locally or in CI, or recording how a spec's intent changed over…
product-principles
Defines 4 Risks confidence thresholds, OST hierarchy levels, Knowledge Pyramid tiers, and state design requirements. Use when evaluating user stories, setting confidence scores, referencing OST levels, scoping MVP, or determining validation sufficiency.
recipe-define
Orchestrate PRD creation from validated hypotheses — standard PRD output with 4 Risks confidence and hypothesis traceability.
recipe-validate
Orchestrate hypothesis validation through type-appropriate methods — prototypes, code analysis, market research, and expert review.
hypothesis-discipline
Manages hypothesis lifecycle, enforces validation criteria, time budgets, and confidence scoring rules. Use when creating hypotheses, updating confidence scores, setting validation criteria, handling timeouts, or recording validation results.