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/claude-code-discover --skill recipe-blueprintgit clone --depth 1 https://github.com/shinpr/claude-code-discoverWrote 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/claude-code-discover/recipe-blueprint)<a href="https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-blueprint"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-blueprint/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/claude-code-discover/recipe-blueprint"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-blueprint.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 | $0.00031 | $0.02073 |
| Opus 5 | $0.00015 | $0.01037 |
| Sonnet 5 | $0.00006 | $0.00415 |
| Haiku 4.5 | $0.00003 | $0.00207 |
Grade A, and why
recipe-blueprint 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: Create or update structural design artifacts in docs/product/design/ that provide shared context for prototype generation and downstream UI specification. Blueprint bridges the gap between "what problems to solve" (discovery) and "what the product looks and works like" (prototypes).
Orchestrator Definition
Execution Protocol:
- 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_typewhen its prerequisites are met and continue from its returned result; equivalent orchestrator work does not complete that step - Exact specialist handoff: The complete Agent prompt consists of all and only the applicable canonical
field: valueentries 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 - Follow the blueprint flow defined below
- Approval gates: At each
[STOP — BLOCKING], present the named decision and resume after explicit user confirmation
Workflow
Assess context and MVP scope → confirm IA → confirm the content model and necessary flows → confirm applicable brand and AI decisions → write the selected artifacts.
Execution Decision Flow
1. Context Assessment
Input: $ARGUMENTS
Inspect only sources whose decisions can change the current blueprint:
| File | Extract |
|---|---|
docs/product/vision.md |
Product vision, design vision, outcomes, NSM, strategic priorities |
docs/product/design-principles.md |
Trade-off resolutions that constrain all design decisions |
docs/product/personas/ |
Relevant roles, JTBD, pains, behavioral patterns |
docs/discovery/INDEX.md |
Opportunity and hypothesis status overview |
docs/discovery/opportunities/ |
In-scope validated opportunities and impact evidence |
docs/discovery/hypotheses/ |
Hypotheses that can change scope or a design decision |
docs/discovery/journeys/ |
Journey maps (if available) |
docs/product/learnings.md |
Tier 1 learnings from reflection cycles |
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 · 161 lines · 31 tokens per session scan A 76c6433901ab
recipe-blueprint is a skill published in the GitHub repository shinpr/claude-code-discover (10 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 2,073 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
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.
design-perspective
Integrates design principles, WCAG 2.2 AA accessibility, persona context, and state design into product decisions. Use when reviewing UX decisions, checking accessibility, applying design principles, or ensuring state coverage in acceptance criteria.
recipe-blueprint
Selects and defines the structural design context needed by a prototype or PRD. Use when shared information architecture, flows, content, brand, Visual Tokens, or AI interaction decisions are missing.
recipe-persona
Creates or updates evidence-backed personas with the context and JTBD needed for product decisions. Use when user segments or behavior affect discovery, design, or requirements.
prototype-guide
Defines evidence-grounded prototype quality and external-generator prompt construction. Use when generating or evaluating a prototype, or exporting a prompt for Lovable, v0, or a similar tool.
recipe-refine-visuals
Use when a design-capable human wants to refine the auto-derived Visual Tokens in docs/product/design/brand-direction.md.