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-workflows --skill recipe-diagnosegit clone --depth 1 https://github.com/shinpr/claude-code-workflowsWrote 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-workflows/recipe-diagnose)<a href="https://agentmods.dev/skills/shinpr/claude-code-workflows/recipe-diagnose"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/recipe-diagnose/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-workflows/recipe-diagnose"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/recipe-diagnose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.02267 |
| Opus 5.5 | $0.00006 | $0.00907 |
| Sonnet 5.5 | $0.00003 | $0.00453 |
| Haiku 4.5 | $0.00002 | $0.00227 |
Grade A, and why
recipe-diagnose 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 yesterday.
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 — 225 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.
Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.
Context: Diagnosis flow to identify root cause and present solutions
Target problem: $ARGUMENTS
Orchestrator Definition
Core Identity: "I am an orchestrator."
Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.
Execution Method:
- Investigation → performed by investigator
- Verification → performed by verifier
- Solution derivation → performed by solver
Orchestrator invokes sub-agents and passes structured JSON between them.
At each Agent invocation below, build the prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.
Execution Gate: Each step below establishes evidence required by the next decision. Complete Steps 0-7 in order, including every required investigation and verification retry. Advance only through the current step's stated quality or coverage condition; invoke solver only after coverage is closed.
Step 0: Diagnosis Scope Envelope (Before investigator invocation)
Define a semantic scope envelope from the reported problem and repository evidence by recording:
- the user's symptom report and corrections verbatim, with linked evidence; distinguish these from investigation interpretations
- observed and expected behavior, observed location, trigger, occurrence conditions, and onset when known; mark unresolved facts rather than selecting a location or condition by plausibility
- symptom-reachable execution paths and adjacent cases that share the same path, contract, persisted state, or external boundary
- applicable evidence axes: code, history, dependencies, configuration, governing documents, and external specifications
- explicit exclusions from the user or governing artifacts
- newly discovered areas are inside the envelope only when they have one of the relationships above and evidence shows they can change the supported cause set, coverage judgment, or counter-evidence
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.
- yesterday Changed 7dad2a92f8fb
- 3d ago Changed · +7 lines 737819233ed5
- 1mo ago Changed · -44 lines d5f1af29dc0f
- 1mo ago First seen · 262 lines · 15 tokens per session scan A 0403595da2f9
recipe-diagnose is a skill published in the GitHub repository shinpr/claude-code-workflows (703 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 2,267 once invoked, about $0.0001 per session on Opus 5.5. 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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