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Skillsdesign-describe

design-describe

Design & creation

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Updated 10-15 21:35

About this skill

Codex compatibility note:

  • Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
  • Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
  • User-question prompts mean to ask the user directly in Codex.
  • Ignore Claude-specific mode-switch instructions when they appear.
  • Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
  • Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
  • Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
  • For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
  • If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

  • docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
  • docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
  • docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Situation-based docs:

  • Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
  • Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
  • Spec/test-case planning or TC mapping: feature-docs-reference.md
  • Integration test implementation/review: integration-test-reference.md
  • E2E test implementation/review: e2e-test-reference.md
  • Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Analyze a screenshot or video and produce a detailed written description of the UI design.

Workflow:

  1. Analyze — Process the visual input (screenshot/video) using vision capabilities
  2. Describe — Write detailed description of layout, colors, typography, interactions

Key Rules:

  • Use ai-multimodal skill for image/video analysis
  • Focus on design elements: layout, spacing, colors, typography, interactions

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Think hard to describe the design based on this screenshot/video: $ARGUMENTS

Required Skills (Priority Order)

  1. ui-ux-pro-max - Design intelligence database (ALWAYS ACTIVATE FIRST)
  2. frontend-design - Visual analysis

Ensure token efficiency while maintaining high quality.

Workflow:

  1. Use ai-multimodal skills to describe super details of the screenshot/video so the developer can implement it easily.
    • Be specific about design style, every element, elements' positions, every interaction, every animation, every transition, every color, every border, every icon, every font style, font size, font weight, every spacing, every padding, every margin, every size, every shape, every texture, every material, every light, every shadow, every reflection, every refraction, every blur, every glow, every image, background transparency, etc.
    • IMPORTANT: Try to predict the font name (Google Fonts) and font size in the given screenshot, don't just use Inter or Poppins.
  2. Use ui-ux-designer subagent to create a design implementation plan following the progressive disclosure structure so the result matches the screenshot/video:
    • Create a directory using naming pattern from ## Naming section.
    • Save the overview access point at plan.md, keep it generic, under 80 lines, and list each phase with status/progress and links.
    • For each phase, add phase-XX-phase-name.md files containing sections (Context links, Overview with date/priority/statuses, Key Insights, Requirements, Architecture, Related code files, Implementation Steps, Todo list, Success Criteria, Risk Assessment, Security Considerations, Next steps).
  3. Report back to user with a summary of the plan.

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Skill Variant: Variant of design skills — describe UI from screenshot or video.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

  • MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting
  • MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
  • MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
  • MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. Any supported AI tool may execute when this shared context and local docs are available.

  1. DETECT: Match prompt against workflow catalog
  2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
  3. ASK (REQUIRED FORMAT): Use a direct user question with this structure unless the user explicitly invoked a workflow/skill and the local protocol treats explicit invocation as confirmation:
    • Question: "Which workflow do you want to activate?"
    • Option 1: "Activate [BestMatch Workflow] (Recommended)"
    • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
  4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
  5. CREATE TASKS: task tracking for ALL workflow steps
  6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

  1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
  2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
  3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
  4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
  5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
  6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
  7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.