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/codex-workflows --skill recipe-implementgit clone --depth 1 https://github.com/shinpr/codex-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/codex-workflows/recipe-implement)<a href="https://agentmods.dev/skills/shinpr/codex-workflows/recipe-implement"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-implement/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/codex-workflows/recipe-implement"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-implement.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 | $0.00018 | $0.00963 |
| Opus 5.5 | $0.00007 | $0.00385 |
| Sonnet 5.5 | $0.00004 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
recipe-implement 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 16d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Required Skills [LOAD BEFORE EXECUTION]
- [LOAD IF NOT ACTIVE]
subagents-orchestration-guide— agent coordination and workflow routing - [LOAD IF NOT ACTIVE]
documentation-criteria— scale-selected document path - [LOAD IF NOT ACTIVE]
requirement-convergence— outcome, exclusion, and rough-cost convergence before design - [LOAD IF NOT ACTIVE]
llm-friendly-context— cross-agent handoffs and task carrier
Invocation rule: Start each named agent with spawn_agent and fork_turns="none". When invoking the same named agent again within this flow, use followup_task on its existing instance with the inputs specified for the current invocation.
Full-Cycle Implementation
$ARGUMENTS
Orchestrator Definition
Core Identity: Coordinate the lifecycle, complete lightweight workflow operations directly, and invoke named specialists for their domain work.
Follow the scale-selected flow and its user approval points from subagents-orchestration-guide.
Step 1: Requirement Analysis
Apply the subagents-orchestration-guide Requirement Evidence Handoff, then invoke requirement-analyzer for compact scope evidence, cost evidence, affected-layer evidence, and decision-changing questions.
At the requirements stop, the orchestrator compares the evidence with its retained user record and applies subagents-orchestration-guide Requirement Convergence. User selections establish requirements and exclusions; the orchestrator judges readiness, determines Structural Scale and affected layers, and selects the canonical route.
Immediately before the stop, read requirement-convergence's references/scope-confirmation.md and render that output from the retained user record and analyzer evidence. Render each unanswered product, UX, or operational decision under User decisions as an unresolved question followed by its answer-dependent effects. Only an explicit user selection moves it into Confirmed scope. Record Structural Scale and workflow routing under Workflow.
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.
- 16d ago Changed 04e987e43b14
- 1mo ago Changed · +2 lines 283653f8e8d5
- 1mo ago Changed 8db668a432d4
- 1mo ago Changed ac88dc8f290a
- 1mo ago First seen · 72 lines · 18 tokens per session scan A 80e095e66739
recipe-implement is a skill published in the GitHub repository shinpr/codex-workflows (39 stars, last pushed 2d ago), licensed MIT. It adds 18 tokens to every session and 963 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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