recipe-define

recipe-define is a skill for Claude Code, Codex from shinpr/claude-code-discover. It costs 27 tokens per session (1,483 once invoked), scanned A, original, MIT.

A guided workflow for turning tested product assumptions into a reviewed PRD. A PRD is a document that explains the problem, proposed product work, requirements, and expected results.

In plain words
What is it for?
Use it to assess readiness, create a PRD, arrange specialist checks, and prepare the document for user approval.
Why use it?
It keeps the evidence behind product decisions connected to the final requirements and makes remaining risks visible before implementation starts.

Skill for Claude CodeCodex

Part of the discover plugin — 15 skills, 5 agents shipped together

Install

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.

agentmods
npx agentmods add skills/shinpr/claude-code-discover/recipe-define
Any agent
npx skills add shinpr/claude-code-discover --skill recipe-define
Clone the repo
git clone --depth 1 https://github.com/shinpr/claude-code-discover

Made for: Claude Code, Codex.

Or install discover, the plugin that ships this one along with the rest of its 15 skills, 5 agents.

Wrote 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.

agentmods badge for recipe-define

README.md
[![agentmods](https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-define.svg)](https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-define)
Your own site
<a href="https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-define"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-define.svg" alt="Measured on agentmods" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,483 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00027 $0.01483
Opus 5 $0.00014 $0.00741
Sonnet 5 $0.00005 $0.00297
Haiku 4.5 $0.00003 $0.00148

Measured 5d ago against content hash e73f11889427, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

recipe-define 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 5d 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.

skills/recipe-define/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context: Transform validated hypotheses into a reviewed PRD that preserves product evidence, exposes remaining risk, and can be consumed by downstream implementation workflows.

Orchestrator Definition

Execution Protocol:

  1. Required specialist execution: Invoking this recipe is the user's explicit instruction and authorization to execute every named specialist whose condition applies. Execute each applicable Agent call with its declared subagent_type when its prerequisites are met and continue from its returned result; equivalent orchestrator work does not complete that step
  2. Exact specialist handoff: The complete Agent prompt consists of all and only the applicable canonical field: value entries declared by the specialist's Input Contract. Copy each value unchanged from its authoritative source; serialize path fields as path strings so the specialist reads referenced artifacts directly
  3. Complete finding coverage: Record one evidence-backed disposition for every reviewer issue before correction or progression
  4. Follow the definition flow below and stop at the final user approval gate

Workflow

Assess readiness → draft the PRD → obtain independent review → resolve each finding → request final user approval.

Execution Decision Flow

1. Readiness Assessment

Input: $ARGUMENTS

Read the relevant Opportunity and hypothesis files. For each hypothesis included in the PRD:

  1. Check confidence evidence using prd-standards references/user-story-guide.md
  2. Assess cost × risk × reversibility
  3. Determine validated enough or needs more validation
  4. Keep proceed, further validation, scope reduction, and explicit residual risk as valid results; choose further validation only when its evidence can change readiness or scope

Use product-principles references/mvp-definition.md to determine the smallest included capability set, explicit exclusions, and observable proof. A ranking aid is optional and applies only when credible candidates remain tied after direct boundary analysis.

Read the full file on GitHub · 112 lines

Changes

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.

  1. 5d ago First seen · 112 lines · 27 tokens per session scan A e73f11889427

Subscribe to this mod's changes

recipe-define is a skill published in the GitHub repository shinpr/claude-code-discover (10 stars, last pushed 7d ago), licensed MIT. It adds 27 tokens to every session and 1,483 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.

Related

Other skills, from other repositories

prfaq

This skill should be used when the user asks to "write a PR/FAQ", "prfaq", "working backwards", "product discovery", "evaluate a product idea", "press release FAQ", "test product value", "revise prfaq", "update prfaq", "add research to prfaq", "add FAQs", "run a meeting", "review meeting", "hive meeting", "autonomous…

punt-labs/prfaq · 140 tokens

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.

shinpr/nautilus · 49 tokens

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.

shinpr/nautilus · 45 tokens

blueprint-standards

Defines structural design artifact formats — information architecture, user flows, content model, brand direction, Visual Tokens, and AI interaction model. Use when creating or reviewing design artifacts that precede prototype generation.

shinpr/nautilus · 45 tokens

recipe-define

Creates a delivery-ready PRD from validated hypotheses with material 4 Risks evidence and necessary traceability. Use when turning validation results into requirements or user stories.

shinpr/nautilus · 35 tokens

recipe-discover

Frames product Opportunities and creates decision-relevant hypotheses from available evidence. Use when exploring a problem, market opportunity, or user need.

shinpr/nautilus · 30 tokens