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 recipe-discovergit 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/recipe-discover)<a href="https://agentmods.dev/skills/shinpr/nautilus/recipe-discover"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/recipe-discover/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/recipe-discover"><img src="https://agentmods.dev/badge/skills/shinpr/nautilus/recipe-discover.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 | $0.01060 |
| Opus 5 | $0.00015 | $0.00530 |
| Sonnet 5 | $0.00006 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
recipe-discover 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 4d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: Discover Opportunities and generate hypotheses by combining business analysis (BMC/VPC/market) with user analysis (JTBD/pains/gains). Outputs Opportunity files and hypothesis files.
Required Skills [LOAD BEFORE EXECUTION]
- [LOAD IF NOT ACTIVE]
hypothesis-discipline— hypothesis lifecycle, evidence, and stopping conditions - [LOAD IF NOT ACTIVE]
product-principles— Opportunity hierarchy, 4 Risks, and framing rules
Conditional Skills [LOAD WHEN TRIGGERED]
- WHEN the request or available evidence raises a business-model, market, or viability decision: [LOAD IF NOT ACTIVE]
business-context
Delegate code analysis when a separate repository reading can change the discovery decision.
Execution Decision Flow
1. Context Assessment
Input: Use the path or text supplied with the explicit skill invocation. If no input was supplied and the target cannot be inferred unambiguously, ask for it.
Assess the starting point:
| Situation | Action |
|---|---|
| Greenfield (no existing product) | Gather the business and user evidence needed to frame the requested outcome |
| Existing codebase | Invoke codebase-analyzer when current behavior can change the Opportunity framing |
| Specific market opportunity | Focus on market analysis + VPC |
| User feedback / support tickets | Focus on user analysis + journey mapping |
Vision exists (docs/product/vision.md) |
Align discovery with Product Outcomes |
2. Business Context Analysis
When business context can change the Opportunity decision, select the relevant business-context framework:
- BMC: Understand the business model — especially Customer Segments, Value Propositions, Revenue Streams
- VPC: Map Customer Profile (jobs/pains/gains) to Value Map (products/pain relievers/gain creators)
- Market Analysis: TAM/SAM/SOM, competitive landscape, market gaps
Read the selected business-context reference for its decision boundary and evidence requirements.
Web search: Use web search for market research — industry reports, competitor analysis, trend data. Market research benefits from hypothesis context (unlike code analysis).
What ships with it
2 files 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.
- 4d ago Changed · +2 lines b5e3933cccac
- 10d ago First seen · 104 lines · 30 tokens per session scan A 052cccee8dc4
recipe-discover is a skill published in the GitHub repository shinpr/nautilus (4 stars, last pushed 8d ago), licensed MIT. It adds 30 tokens to every session and 1,060 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.