Recipe front adjust
Skill shinpr/codex-workflows/.agents/skills/recipe-front-adjust
Controlled, reviewable agentic coding workflows for OpenAI Codex CLI with task-specific subagents, explicit planning, TDD, and quality gates.
npx -y skills add shinpr/codex-workflows --skill recipe-front-adjustAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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Adjust an implemented UI with external resource context, focused write-set confirmation, verification, and quality checks.
SKILL.md
5.3 KB, as published. Nobody here has run it
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]
documentation-criteria-- scale and planning criteria - [LOAD IF NOT ACTIVE]
external-resource-context-- external resource hearing and lookup - [LOAD IF NOT ACTIVE]
subagents-orchestration-guide-- agent coordination rules - [LOAD IF NOT ACTIVE]
llm-friendly-context-- clear prompts, handoffs, and generated artifacts
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 Protocol:
- Delegate bounded one-shot work to
ui-analyzer,work-planner, andquality-fixer-frontend. - Run user dialogue, write-set confirmation, edits, and verification in the parent session.
- Respect every
[STOP]marker before moving to the next phase.
Adjustment request: $ARGUMENTS
Execution Flow
Step 1: External Resource Hearing
Run the frontend domain hearing protocol from external-resource-context.
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]. Read docs/project-context/external-resources.md, resolve relevant UI sources through declared access methods, analyze existing UI code, and populate candidateWriteSet[].
Step 3: Confirm Write Set and Scale
- Present
candidateWriteSet[]to the user. - Ask the user to confirm high-confidence entries, confirm all entries, or provide an edited file list.
- Apply documentation-criteria Creation Decision Matrix to the confirmed write set:
0 files: ask the user for the component or path that owns the change, then pause this recipe.1-2 files: proceed with direct adjustment.3-5 files: create a focused work plan.6+ filesor ADR conditions: route to the frontend design flow.
Step 4: Plan Creation When Needed
For 3-5 files, spawn work-planner:
Create a focused UI adjustment plan. Adjustment request: [verbatim]. ui_analysis: [ui-analyzer JSON]. External resources: docs/project-context/external-resources.md. Confirmed write set: [files]. Each phase should be implementable as 1-3 commits. Include visual verification, accessibility, i18n parity, and generated artifact checks when relevant. Output path: docs/plans/[YYYYMMDD]-adjust-[short-description].md.
[STOP] Present the plan and wait for approval.
For 1-2 files, present a concise adjustment context:
- request
- confirmed write set
- relevant
focusAreas[] - relevant external resource summaries and access methods
[STOP] Wait for user confirmation that the context covers the work.
Step 5: Adjustment and Verification
For each adjustment unit:
- Plan the edit from
focusAreas[], confirmed write set, and relevant external resource summaries. - Apply the edit in the parent session.
- Verify against declared access methods:
- design origin: compare implementation target to the recorded design source
- visual verification: use the recorded browser, test runner, Storybook, dev server, or manual confirmation path
- design system: confirm tokens, variants, and usage rules through the recorded source
- Refine until the implemented UI matches the design source or the user-confirmed adjustment target.
Step 6: Quality Verification
Spawn quality-fixer-frontend for each unit:
- Direct adjustment: pass
filesModified: [edited files] - Planned adjustment: pass
task_file: [work plan path]andfilesModified: [edited files]
Route quality-fixer-frontend results:
approved: proceed to commitstub_detected: complete the implementation gap and rerun quality verificationblocked: surface missing prerequisites or unclear specification points to the user
Step 7: Commit
Commit each approved adjustment unit with affected files and relevant generated artifacts.
Completion Criteria
- External resource hearing completed or update explicitly skipped
-
ui-analyzerreturned JSON with external resource status andcandidateWriteSet - User confirmed write set before scale judgment
- Scale judgment completed with matching branch
- Direct context or work plan approved
- Adjustment units edited and verified through declared resource paths
- Each unit passed
quality-fixer-frontend - Each approved unit committed
Output Example
Frontend adjustment completed.
- External resources: docs/project-context/external-resources.md (updated|unchanged)
- UI analysis: [N] components, [M] focus areas
- Scale: 1-2 files | 3-5 files
- Work plan: path | N/A
- Adjustment units committed: [count]
- Quality status: approved