scan-design-system

name	scan-design-system
description	[Documentation] Use when scanning design tokens, component inventory, and app-to-doc design system mappings.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Prefer the plan-hard skill for planning guidance in this Codex mirror.
• 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: Scan project for design system artifacts → populate docs/project-reference/design-system/README.md with overview, app-to-documentation mapping, design token inventory, and component catalog. (read directly when relevant; do not rely on hook-injected conversation text)

Workflow:

1. Classify — Detect design system type and approach before scanning
2. Scan — Parallel sub-agents discover structure, components, tokens
3. Report — Write findings incrementally to report file
4. Generate — Build/update reference doc from report
5. Fresh-Eyes — Round 2 verification validates paths and token values

Key Rules:

• Generic — works with any design system approach
• Discover organization dynamically from file system MUST ATTENTION detect design system TYPE first — agent emphasis depends on type
• Every reference must point to real files — NEVER fabricate component names or token values

---

Scan Design System

Phase 0: Classify Design System Type

Before any other step, run in parallel:

1. Read docs/project-reference/design-system/README.md

   • Detect mode: Init (placeholder) or Sync (populated)
   • In Sync mode: extract section list → skip re-scanning well-documented sections

2. Detect design system type:

Signal	Type	Agent Emphasis
Token files (design-tokens.json, tokens.scss, Style Dictionary config)	Token-first	Prioritize Agent 3 (token discovery)
Storybook config (.storybook/, *.stories.ts)	Component-library	Prioritize Agent 2 (component inventory)
Figma token exports or figma-tokens.json	Figma-driven	Prioritize Agent 3 (token import chain)
Only component directories, no token files	Ad-hoc/CSS-only	Prioritize Agent 1 (structure)
Mix of above	Hybrid	Run all 3 agents with equal weight

3. Resolve config-driven paths from docs/project-config.json:

   • designSystem.canonicalDoc — single source of truth for new code
   • designSystem.tokenFiles — drop-in token files
   • Never hardcode these names — content varies per project, names come from config.

4. Check for app-specific design docs in the same directory

Evidence gate: Confidence <60% on design system type → report uncertainty, proceed with Agent 1 (structure) only.

Phase 1: Plan

Create task tracking entries for each sub-agent and each verification step. Do not start Phase 2 without tasks created.

Phase 2: Execute Scan (Parallel Sub-Agents)

Launch 3 general-purpose sub-agents in parallel. Each MUST:

• Write findings incrementally after each category — NEVER batch at end
• Cite file:line for every finding
• Confidence: >80% document; 60-80% note as "observed (unverified)"; <60% omit

All findings → plans/reports/scan-design-system-{YYMMDD}-{HHMM}-report.md

Agent 1: Design System Structure

Think: How is the design system organized? What's the canonical doc? What's the token chain? Which apps have design docs and which don't?

• Glob for docs/project-reference/design-system/** to map all design docs
• Find design token files (CSS custom properties, SCSS variables, JSON tokens)
• Discover Storybook stories (*.stories.ts, *.stories.tsx, *.stories.mdx)
• Find component library entry points (index files, barrel exports)
• Map app-to-design-doc relationships
• Verify canonical doc at {docsPath}/{canonicalDoc} has expected sections. Flag missing sections.
• Verify token files at {docsPath}/{tokenFiles[i]} exist and contain variable declarations. Flag empty/missing.

Agent 2: Component Inventory

Think: What dimensions define a complete component inventory? Consider: Discoverability (can I find it?), Categorization (what type?), Variant coverage (size/color/state?), Accessibility (ARIA/keyboard?), Documentation completeness (JSDoc/README/Storybook?), Icon/asset library coverage.

For each dimension, derive the specific grep/glob patterns from what the project actually uses — do NOT hardcode Angular/React/Vue-specific patterns unless confirmed.

• Find reusable UI components (shared component directories, exported components)
• Find component categories (layout, forms, feedback, navigation, data display)
• Discover component variants (size, color, state variations)
• Find icon sets or asset libraries
• Look for accessibility patterns (ARIA roles, keyboard support)
• Find documentation for individual components

Agent 3: Token & Component Source Discovery

Think: What design tokens actually exist in source code (not just what's documented)? Which are declarations (authoritative) vs usages (derived)?

Source scope (whitelist, not full repo):

• src/**/styles/**/*.{scss,css}, src/**/themes/**/*.{scss,css}, src/**/tokens/**/*.{scss,css}
• src/**/*.scss ONLY when path contains theme, token, palette, design, style-guide, or variables
• Exclude node_modules, dist, .nx, coverage, component-local styles

Discovery rules (declarations only, NOT usages):

• CSS custom properties (declarations): --[a-zA-Z][a-zA-Z0-9_-]*\s*: — capture LHS only, dedupe
• SCSS variable declarations: ^\s*\$[a-zA-Z][a-zA-Z0-9_-]*\s*: — anchor to start-of-line
• Color values used ≥3 times across whitelist (hex, rgb, hsl)
• Spacing scale (declarations): (padding|margin|gap)\s*:\s*[\d.]+(px|rem|em) — extract values, dedupe
• Typography (declarations): (font-family|font-size|font-weight)\s*: — extract RHS, dedupe
• Breakpoints: @media[^{]*\((min|max)-width:\s*[\d.]+(px|em|rem)\) — extract widths, dedupe

Categorise: Colors / Typography / Spacing / Breakpoints / Z-Index / Elevation / Component-prefixes / Other. Persist incrementally — append to report after each category. Quality gate: If a category has <3 unique entries OR >200 entries, log "scope too narrow/broad — manual refinement required".

Phase 3: Analyze & Generate

Read report. Build target sections.

Round 1 (main agent): Build section drafts from report findings.

Round 2 (fresh sub-agent, zero memory): Independently verifies:

• All doc paths exist on filesystem (Glob check — not just "looks right")
• All token values are from actual declarations, not usages or fabricated
• Component names in inventory match actual files (Grep check)
• Gap Analysis section present (what's missing, not just what exists)

Target Sections

Section	Content
Design System Overview	High-level description — type, tools, organization
App Documentation Map	Table: App name, Design doc path, Token source, Component library
Design Tokens	Token categories, file locations, naming convention — values from declarations
Component Inventory	Table: Component name, Category, Variants, Path, Has docs?
Gap Analysis	Missing docs, zero-adoption tokens, undocumented components
Icon & Asset Library	Icon set source, asset directory paths, usage patterns
Storybook	Setup (if exists), story organization, how to add new stories
Usage Guidelines	How to consume tokens and components in application code

Authoring (init mode only)

When init mode detected (canonical doc missing or placeholder):

1. Author {docsPath}/{canonicalDoc} from Agent 3 findings:
   • Prepend regen marker: <!-- Generated by $scan-design-system on YYYY-MM-DD; refine sections manually -->
   • Sections: Foundations, Tokens, Components, Patterns, Accessibility, Adoption Strategy
2. Author each {docsPath}/{tokenFiles[i]} from grouped declarations:
   • First: REMOVE PLACEHOLDER_MARKER_SCSS sentinel before writing real tokens
   • .scss: SCSS variable block per category + CSS custom property mirrors in :root {}
   • Categories: Colors, Typography, Spacing, Breakpoints, Z-Index, Elevation/Shadow
3. Preserve manual content in sync mode — DO NOT overwrite populated doc/token file

Phase 4: Write & Verify

1. Write updated doc with <!-- Last scanned: YYYY-MM-DD --> at top
2. Surgical update only — preserve unchanged sections
3. Verify config-driven paths: {docsPath}/{canonicalDoc} and every {docsPath}/{tokenFiles[i]}
4. Verify (Glob): ALL component paths in inventory exist — not just 3
5. Verify (Grep): Token names in doc match actual declarations in source
6. Verify: Gap Analysis section present
7. Report: sections updated / unchanged / gaps documented / canonical + token presence

---

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks per file read. Prevents context loss from long files. Simple tasks: ask user whether to skip.

Prerequisites: MUST ATTENTION READ before executing:

Critical Thinking Mindset — Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources, admit uncertainty, self-check output, cross-reference independently. Certainty without evidence = root of all hallucination.

Scan & Update Reference Doc — Surgical updates only, NEVER full rewrite.

1. Read existing doc first — understand structure and manual annotations
2. Detect mode: Placeholder (headings only) → Init. Has content → Sync.
3. Scan codebase (grep/glob) for current patterns
4. Diff findings vs doc — identify stale sections only
5. Update ONLY diverged sections. Preserve manual annotations.
6. Update metadata (date, version) in frontmatter/header
7. NEVER rewrite entire doc. NEVER remove sections without evidence obsolete.

Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — stale instantly
2. No directory trees — use 1-line path conventions
3. No TOCs — AI reads linearly
4. One example per pattern — only if non-obvious
5. Lead with answer, not reasoning
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end

AI Mistake Prevention — Failure modes to avoid:

Verify AI-generated content against actual code. AI hallucinates component names/token values. Grep to confirm existence before documenting. NEVER invent variable values, hex colors, or mixin signatures. Grep to confirm before documenting. Trace full dependency chain after edits. Always trace full chain. Surface ambiguity before coding. NEVER pick silently.

IMPORTANT MUST ATTENTION read existing doc first, scan codebase, diff, surgical update only. Never rewrite entire doc.

IMPORTANT MUST ATTENTION output quality: no counts/trees/TOCs, 1 example per pattern, lead with answer.

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

IMPORTANT MUST ATTENTION break work into small task tracking tasks BEFORE starting IMPORTANT MUST ATTENTION detect design system TYPE in Phase 0 — agent emphasis depends on it IMPORTANT MUST ATTENTION NEVER invent variable values, hex colors, or mixin signatures — Grep to confirm IMPORTANT MUST ATTENTION sub-agents write findings incrementally after each category — NEVER batch at end IMPORTANT MUST ATTENTION Gap Analysis section is mandatory — document what's missing, not just what exists IMPORTANT MUST ATTENTION when Round 1 finds issues, Round 2 fresh-eyes after fixing validates paths and token values. Clean Round 1 ENDS the scan.

Anti-Rationalization:

Evasion	Rebuttal
"Design system type obvious, skip Phase 0 detection"	Phase 0 is BLOCKING — agent emphasis depends on detected type
"Only 2 agents needed, skip token discovery agent"	Token discovery is separate from component inventory — NEVER merge
"Token values look correct"	Grep-verify ALL token values against declarations — "looks correct" ≠ verified
"Gap Analysis not needed"	Gap Analysis is a required section — documents what's missing for future work
"Skip Round 2 even when Round 1 found issues"	Clean Round 1 (zero issues) does end the scan. But when issues exist, fresh-eyes is mandatory after fixing — main agent rationalizes own mistakes.
"Verified 3 paths, that's enough"	Glob-verify ALL paths in inventory — spot-check is insufficient

[TASK-PLANNING] Before acting, analyze task scope and break 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.

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:
   • 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.

Learned Lessons

Lessons Learned

[CRITICAL] Hard-won project debugging/architecture rules. MUST ATTENTION apply BEFORE forming hypothesis or writing code.

Quick Summary

Goal: Prevent recurrence of known failure patterns — debugging, architecture, naming, AI orchestration, environment.

Top Rules (apply always):

• MUST ATTENTION verify ALL preconditions (config, env, DB names, DI regs) BEFORE code-layer hypothesis
• MUST ATTENTION fix responsible layer — NEVER patch symptom sites with caller-specific defensive code
• MUST ATTENTION use ExecuteInjectScopedAsync for parallel async + repo/UoW — NEVER ExecuteUowTask
• MUST ATTENTION name by PURPOSE not CONTENT — adding member forces rename = abstraction broken
• MUST ATTENTION persist sub-agent findings incrementally after each file — NEVER batch at end
• MUST ATTENTION Windows bash: verify Python alias (where python/where py) — NEVER assume python/python3 resolves

---

Debugging & Root Cause Reasoning

• [2026-04-11] Holistic-first: verify environment before code. Failure → list ALL preconditions (config, env vars, DB names, endpoints, DI regs, credentials, permissions, data prerequisites) → verify each via evidence (grep/cat/query) BEFORE code-layer hypothesis. Worst rabbit holes: diving nearest layer while bug sits elsewhere — e.g., hours debugging "sync timeout", real cause: test appsettings pointing wrong DB. Cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug in caller (wrong data) or callee (wrong handling)? Fix responsible layer — NEVER patch symptom site masking real issue.
• [2026-04-01] Trace data lifecycle, not error site. Follow data: creation → transformation → consumption. Bug usually where data created wrong, not consumed.
• [2026-04-01] Code is caller-agnostic. Functions/handlers/consumers don't know who invokes them. Comments/guards/messages describe business intent — NEVER reference specific callers (tests, seeders, scripts).

Architecture Invariants

• [2026-03-31] ParallelAsync + repo/UoW MUST use ExecuteInjectScopedAsync, NEVER ExecuteUowTask. ExecuteUowTask creates new UoW but reuses outer DI scope (same DbContext) — parallel iterations sharing non-thread-safe DbContext silently corrupt data. ExecuteInjectScopedAsync creates new UoW + new DI scope (fresh repo per iteration).
• [2026-03-31] Bus message naming MUST include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) are leaders. Feature services (Growth, Talents) sending to core MUST use {CoreServiceName}...RequestBusMessage — never define own event for core to consume.

Naming & Abstraction

• [2026-04-12] Name PURPOSE not CONTENT — "OrXxx" anti-pattern. HrManagerOrHrOrPayrollHrOperationsPolicy names set members, not what it guards. Add role → rename = broken abstraction. Rule: names express DOES/GUARDS, not CONTAINS. Test: adding/removing member forces rename? YES = content-driven = bad → rename to purpose (e.g., HrOperationsAccessPolicy). Nuance: "Or" fine in behavioral idioms (FirstOrDefault, SuccessOrThrow) — expresses HAPPENS, not membership.

Environment & Tooling

• [2026-04-20] Windows bash: NEVER assume python/python3 resolves — verify alias first. Python may not be in bash PATH under those names. Check: where python / where py. Prefer py (Windows Python Launcher) for one-liners, node if JS alternative exists.

Test-specific lessons → docs/project-reference/integration-test-reference.md Lessons Learned section. Production-code anti-patterns → docs/project-reference/backend-patterns-reference.md Anti-Patterns section. Generic debugging/refactoring reminders → System Lessons in .claude/hooks/lib/prompt-injections.cjs.

---

Closing Reminders

• IMPORTANT MUST ATTENTION holistic-first: verify ALL preconditions (config, env, DB names, endpoints, DI regs) BEFORE code-layer hypothesis — cheapest check first
• IMPORTANT MUST ATTENTION fix responsible layer — NEVER patch symptom site; trace caller (wrong data) vs callee (wrong handling), fix root owner
• IMPORTANT MUST ATTENTION parallel async + repo/UoW → ALWAYS ExecuteInjectScopedAsync, NEVER ExecuteUowTask (shared DbContext = silent data corruption)
• IMPORTANT MUST ATTENTION bus message prefix = schema ownership; feature services NEVER define events for core services — use {CoreServiceName}...RequestBusMessage
• IMPORTANT MUST ATTENTION name by PURPOSE — adding/removing member forces rename = broken abstraction
• IMPORTANT MUST ATTENTION sub-agents MUST write findings after each file/section — NEVER batch all findings into one final write
• IMPORTANT MUST ATTENTION Windows bash: NEVER assume python/python3 resolves — run where python/where py first, use py launcher or node

[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.