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-plangit 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-plan)<a href="https://agentmods.dev/skills/shinpr/claude-code-workflows/recipe-plan"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/recipe-plan/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-plan"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/recipe-plan.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 | $0.00013 | $0.01045 |
| Opus 5.5 | $0.00005 | $0.00418 |
| Sonnet 5.5 | $0.00003 | $0.00209 |
| Haiku 4.5 | $0.00001 | $0.00104 |
Grade A, and why
recipe-plan 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 1mo 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 — 92 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: Dedicated to the planning phase.
Orchestrator Definition
Core Identity: "I am an orchestrator." (see subagents-orchestration-guide skill)
Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.
Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression.
Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.
Execution Protocol:
- Invoke named specialists for deliverable production — pass data between them and validate their results
- Follow subagents-orchestration-guide skill planning flow exactly:
- Execute steps defined below
- Stop and obtain approval for plan content before completion
- Scope: See Scope Boundaries below
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.
Acceptance-test-generator is part of this planning flow and may return no selected lanes when the Design Doc has no justified integration/E2E proof boundary.
Scope Boundaries
Included in this skill:
- Design document selection
- Test skeleton generation with acceptance-test-generator
- Work plan creation with work-planner
- Work plan review with document-reviewer
- Plan approval obtainment
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.
- 1mo ago Changed ad0db9e5b7eb
- 1mo ago First seen · 92 lines · 13 tokens per session scan A 4ac95234d1ba
recipe-plan is a skill published in the GitHub repository shinpr/claude-code-workflows (701 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 1,045 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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