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-front-adjustnpx skills add shinpr/codex-workflows --skill recipe-front-adjustgit 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-front-adjust)<a href="https://agentmods.dev/skills/shinpr/codex-workflows/recipe-front-adjust"><img src="https://agentmods.dev/badge/skills/shinpr/codex-workflows/recipe-front-adjust.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 | $0.00018 | $0.01002 |
| Opus 5 | $0.00009 | $0.00501 |
| Sonnet 5 | $0.00004 | $0.00200 |
| Haiku 4.5 | $0.00002 | $0.00100 |
Grade A, and why
recipe-front-adjust 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 5d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: UI adjustment for implemented frontend features. The parent session owns the edit and verification loop; subagents handle bounded fact gathering, planning, and quality checks.
Required Skills [LOAD BEFORE EXECUTION]
- [LOAD IF NOT ACTIVE]
subagents-orchestration-guide-- agent coordination rules - [LOAD IF NOT ACTIVE]
llm-friendly-context-- adjustment handoff and verification context
Load external-resource-context in Step 1 only when a named external source is required for the requested adjustment.
Spawn rule: every spawn_agent call uses fork_turns="none" so the subagent receives only the task message and explicitly provided context.
Execution Pattern
Core Identity: "I am a guided executor. I run the UI adjustment and verification loop in the parent session."
Execution Plan: Reuse the active execution plan. When the workflow has multiple dependent actions and no plan exists, create one that tracks them through final verification.
Execution Protocol:
- Delegate bounded one-shot work to
ui-analyzerandquality-fixer-frontend. - Run evidence resolution, edits, and verification in the parent session.
Adjustment request: $ARGUMENTS
Execution Flow
Step 1: External Resource Hearing
Identify whether the requested adjustment depends on an external design or verification source unavailable from the repository or supplied input. Reuse a matching recorded resource when available. Otherwise run the focused external-resource-context hearing for that exact source. When repository or user-supplied evidence defines the target, continue with no external resource.
Step 2: UI Fact Gathering
Spawn ui-analyzer:
requirement_analysis: { affectedFiles: [files inferred from request], purpose: "UI adjustment", technicalConsiderations: [] }. requirements: [adjustment request]. target_paths: [paths named or inferred from request]. target_components: [components named in request]. ui_spec_path: [path if available]. externalResourceRefs: [{label, featureIdentifier} selected in Step 1, or []]. Analyze existing UI code and populate candidateWriteSet[].
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.
- 5d ago First seen · 86 lines · 18 tokens per session scan A 45b92f2e1a95
recipe-front-adjust is a skill published in the GitHub repository shinpr/codex-workflows (37 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 1,002 once invoked, about $0.0001 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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