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-discovergit 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-discover)<a href="https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-discover"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-discover.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 | $0.00017 | $0.01177 |
| Opus 5 | $0.00009 | $0.00589 |
| Sonnet 5 | $0.00003 | $0.00235 |
| Haiku 4.5 | $0.00002 | $0.00118 |
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 7d 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 — 107 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.
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 discovery flow defined below
- Approval gates: At each
[STOP — BLOCKING], present the named decision and resume after explicit user confirmation
Workflow
Assess context → gather decision-relevant business and user evidence → confirm Opportunities → draft and approve hypotheses → write discovery artifacts.
Execution Decision Flow
1. Context Assessment
Input: $ARGUMENTS
Assess the starting point:
| Situation | Action |
|---|---|
| Greenfield (no existing product) | Gather the business and user evidence needed to frame the current Opportunity |
| Existing codebase | Invoke codebase-analyzer first using Agent tool (subagent_type: "discover:codebase-analyzer") with analysis_mode: feature_discovery and governing_context: $ARGUMENTS |
| 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
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.
- 7d ago First seen · 107 lines · 17 tokens per session scan A dd44d9965038
recipe-discover is a skill published in the GitHub repository shinpr/claude-code-discover (10 stars, last pushed 9d ago), licensed MIT. It adds 17 tokens to every session and 1,177 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 skills, from other repositories
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.
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.
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.
recipe-validate
Validates a hypothesis with a risk-appropriate method and records decision-relevant evidence. Use when testing Value, Usability, Feasibility, or Viability assumptions.
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.
recipe-discover
Frames product Opportunities and creates decision-relevant hypotheses from available evidence. Use when exploring a problem, market opportunity, or user need.