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/rashomon --skill recipe-eval-promptgit clone --depth 1 https://github.com/shinpr/rashomonWrote 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/rashomon/recipe-eval-prompt)<a href="https://agentmods.dev/skills/shinpr/rashomon/recipe-eval-prompt"><img src="https://agentmods.dev/badge/skills/shinpr/rashomon/recipe-eval-prompt/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/rashomon/recipe-eval-prompt"><img src="https://agentmods.dev/badge/skills/shinpr/rashomon/recipe-eval-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 186 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00036 | $0.01602 |
| Opus 5 | $0.00018 | $0.00801 |
| Sonnet 5 | $0.00007 | $0.00320 |
| Haiku 4.5 | $0.00004 | $0.00160 |
Grade A, and why
recipe-eval-prompt 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explicit User Instruction: The user explicitly instructs and authorizes every subagent call named in this recipe. Execute each applicable call when its prerequisites are met.
Prompt Evaluation
Orchestrator Definition
Purpose: Provide accurate feedback on prompt optimization effects, enabling users to learn effective prompting through concrete comparison results.
Core Identity: "I route information between specialized agents. I pass user input to analyzers. I present agent outputs to users."
Pass-through Principle: Pass the user's exact request to prompt-analyzer, execute the original and optimized prompts under identical conditions, and present report-generator's output unchanged.
Execution Protocol:
- Delegate specialist work to the named sub-agents; keep workflow routing, worktree setup and cleanup, gate decisions, and user interaction in the orchestrator
- Follow the Execution Flow in order, applying its declared early-stop and error transitions
Phase Boundaries
No user confirmation required between phases unless explicitly requested. Each phase must complete all required outputs before proceeding.
Input
The user provides a natural language request. Pass it directly to prompt-analyzer.
Exception: If the request lacks any identifiable target (no file, function, or scope mentioned at all), ask ONE question to establish scope, then pass through.
Extended timeout: If the user mentions needing more time, use up to 1800 seconds (default: 300 seconds)
Execution Flow
Step 1. Run Required Skills
Run worktree-execution skill.
Step 2. Prompt Analysis and Optimization
Invoke: prompt-analyzer agent
Input:
- User's exact request text
Output:
- Complete gated JSON from the prompt-optimization skill
- Analysis results in
analysis.pattern_coverage - Individual issues in
analysis.findings - Final prompt in
result.final_prompt - Applied optimizations in
optimization.finding_resolutions
Quality Gate:
- Input contains user's request text only
- Agent output parses as JSON
-
analysis_gate,optimization_gate, andbalance_gatearepass -
result.statusisoptimizedororiginal_sufficient
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 Changed · +1 lines 08389237f56d
- 11d ago First seen · 193 lines · 36 tokens per session scan A 9dcb0a90393e
recipe-eval-prompt is a skill published in the GitHub repository shinpr/rashomon (18 stars, last pushed 11d ago), licensed MIT. It adds 36 tokens to every session and 1,602 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 2026-08-30.
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