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-personagit 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-persona)<a href="https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-persona"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-persona/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-persona"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-persona.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 | $0.00019 | $0.00872 |
| Opus 5 | $0.00010 | $0.00436 |
| Sonnet 5 | $0.00004 | $0.00174 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
recipe-persona 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: Create or update persona files with demographics, context, JTBD, pains/gains, and behavioral patterns. Integrates existing codebase analysis when available.
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 persona flow defined below
- Approval gate: At
[STOP — BLOCKING], present the persona decision and resume after explicit user confirmation
Workflow
Assess create/update context → gather available evidence → draft and confirm the persona → write the persona and affected references.
Execution Decision Flow
1. Context Assessment
Input: $ARGUMENTS
| Situation | Action |
|---|---|
| No personas exist | Create from scratch — gather user research or assumptions |
| Personas exist, new data available | Update existing personas with new evidence |
| Existing codebase | Invoke codebase-analyzer for repository-observable roles, workflows, and behavior-related structures; user demand or usage claims require direct behavioral evidence |
| Post-interview / post-survey | Update with new primary research |
2. Research Gathering
From Existing Code (if applicable)
Invoke codebase-analyzer using Agent tool (subagent_type: "discover:codebase-analyzer") with analysis_mode: user_behavior and governing_context: $ARGUMENTS to identify:
- User roles and permissions in the system
- User-facing features and workflows
- Data models related to users
- Analytics/tracking events (if present)
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 · 93 lines · 19 tokens per session scan A 006dc0a0f13e
recipe-persona is a skill published in the GitHub repository shinpr/claude-code-discover (10 stars, last pushed 11d ago), licensed MIT. It adds 19 tokens to every session and 872 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.
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recipe-discover
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recipe-validate
Validates a hypothesis with a risk-appropriate method and records decision-relevant evidence. Use when testing Value, Usability, Feasibility, or Viability assumptions.