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 agentmods add skills/shinpr/codex-workflows/recipe-quality-profilenpx skills add shinpr/codex-workflows --skill recipe-quality-profilegit 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-quality-profile)<a href="https://agentmods.dev/skills/shinpr/codex-workflows/recipe-quality-profile"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-quality-profile.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01059 |
| Opus 5 | $0.00019 | $0.00530 |
| Sonnet 5 | $0.00008 | $0.00212 |
| Haiku 4.5 | $0.00004 | $0.00106 |
Grade A, and why
recipe-quality-profile 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 6d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Required Skills [LOAD BEFORE EXECUTION]
llm-friendly-context— make each generated condition unambiguous, executable, and observablecoding-rules— distinguish repository-specific policy from general code quality knowledge
Purpose
Establish repository-specific code-quality acceptance conditions with the user, then create or update docs/project-context/quality.yaml. Create docs/project-context/ when the directory is absent.
Target repository and, when a profile already exists, requested policy change: $ARGUMENTS
Profile Contract
Use this shape:
version: 1
review_dimensions:
- id: stable-kebab-case-id
applies_when: Observable condition that makes this repository rule relevant to a change.
pass: Observable accepted state to verify.
evidence:
- "repository/path: section, identifier, or contract"
Each dimension represents one repository-specific decision. id identifies it across reviews, applies_when limits its review surface, pass defines acceptance, and evidence records why the repository owns the rule.
Authoring Flow
- Resolve the target repository and read its existing
docs/project-context/quality.yamlwhen present. When a profile exists without a requested policy change, ask for the intended change and keep the profile unchanged. - Establish candidate dimensions before deciding profile content:
- For initial creation, derive candidates from acceptance conditions that repository instructions, contributor documentation, CI, manifests and scripts, schemas and public contracts, tests, or established implementation patterns express or enforce.
- For an update, translate the requested policy change into candidate additions, changes, or removals. Match it to the current dimension that owns the same policy when one exists and preserve other dimensions as the proposed unchanged set.
- State a candidate
applies_whenandpass, then identify the repository claims that must hold for that criterion to mean what it says. Inspect supporting and contradicting evidence, following declarations, workflows, contracts, tests, and usages wherever their difference could change the candidate's applicability, accepted state, or ownership. - Compare the candidate with the current profile and repository evidence. Record duplicated policy ownership, contradictory accepted states under overlapping
applies_whenconditions, conflicting evidence, and current accepted behavior that the candidate would newly reject. Separate repository facts from policy choices that only the user can make. - Retain a candidate for the proposal only when its failure would change implementation acceptance and every repository fact it depends on is supported by cited evidence. Present remaining policy choices for user confirmation. When a required repository fact cannot be established, report the exact evidence needed and omit that candidate from the proposal. A general language or framework concern qualifies when repository evidence adds a distinct local rule. Express each retained dimension as one positive observable
passcondition with the narrowest usefulapplies_whenboundary, and consolidate dimensions that would produce the same finding and correction. - Before writing, present the proposed additions, changes, and removals; confirm that other dimensions remain unchanged; show the supporting and contradicting evidence for each proposed modification; and state every unresolved policy choice with its effect on review acceptance. [STOP — BLOCKING] Keep the repository unchanged until the user explicitly confirms a proposal with no unresolved choices.
- When the user's response changes a proposed criterion or resolves a policy choice, repeat its repository comparison and present the revised proposal for confirmation. After confirmation, write only the confirmed profile content.
- Read the resulting YAML and verify version
1, unique IDs, all four dimension fields, positive observable pass conditions, readable evidence references, and consistency with the confirmed proposal.
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.
- 6d ago First seen · 73 lines · 39 tokens per session scan A dc4b3a7786be
recipe-quality-profile is a skill published in the GitHub repository shinpr/codex-workflows (37 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 1,059 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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