scan-frontend-patterns

name	scan-frontend-patterns
version	2.0.0
last_reviewed	"2026-04-22T00:00:00.000Z"
description	[Documentation] Use when scanning frontend component, state, form, API, routing, and styling patterns.

Quick Summary

Goal: Scan frontend codebase → populate docs/project-reference/frontend-patterns-reference.md with component base classes, state management patterns, form handling, API service patterns, routing conventions, and directory structure.

Workflow:

1. Classify — Detect framework and scan mode
2. Scan — Parallel sub-agents discover patterns with file:line evidence
3. Report — Write findings incrementally to report file
4. Generate — Build/update reference doc from report
5. Fresh-Eyes — Round 2 verification validates all examples

Key Rules:

• Generic — works with any frontend framework (Angular, React, Vue, Svelte, etc.) MUST ATTENTION detect framework FIRST — agent scope and grep patterns depend on it
• Every code example from actual project files with file:line

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Scan Frontend Patterns

Phase 0: Detect Framework & Mode

[BLOCKING] Before any other step, run in parallel:

1. Read docs/project-reference/frontend-patterns-reference.md

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

2. Detect frontend framework:

Signal	Framework	Key Patterns to Search
angular.json + nx.json	Angular (Nx)	AppBaseComponent, PlatformVmStore, untilDestroyed, BEM classes
angular.json (no Nx)	Angular	@Component, OnDestroy, reactive forms, HttpClient
package.json with react/next	React	hooks, context, useState, useEffect, fetch wrappers
package.json with vue/nuxt	Vue	Composition API, ref, reactive, Pinia stores
package.json with svelte/sveltekit	Svelte	$: reactivity, stores, onMount/onDestroy
Multiple frameworks	Multi-framework	Document each separately — DO NOT merge

3. Detect scan mode:

Mode	Condition	Action
Init	Target doc doesn't exist or placeholder only	Full scan, create all sections
Sync	Target doc has real content	Diff scan — check new base classes, changed patterns

4. Load app paths from docs/project-config.json contextGroups/modules[] if available.

Evidence gate: Confidence <60% on framework → report uncertainty, ask user before proceeding.

Phase 1: Plan

Create TaskCreate 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 pattern category — NEVER batch at end
• Cite file:line for every pattern example
• Confidence: >80% document; 60-80% note as "observed (unverified)"; <60% omit

All findings → plans/reports/scan-frontend-patterns-{YYMMDD}-{HHMM}-report.md

Agent 1: Component & Form Patterns

Think (Base Class dimension): What base classes exist? What does each provide — lifecycle, subscriptions, form helpers, DI? Which base class is used for simple components vs complex state vs forms?

Think (Form dimension): Is form state reactive or template-driven? Where does validation live — in the form, in validators, in the model? What's the error display pattern?

Think (Cleanup dimension): How are subscriptions cleaned up? Is there a shared mechanism (e.g., untilDestroyed()) or is each component responsible?

• Grep for component base classes (extends.*Component, AppBaseComponent, React.Component, defineComponent)
• Find form handling patterns (reactive forms, form builders, validation approach, error display)
• Discover component lifecycle conventions (init, destroy, cleanup patterns)
• Find template/JSX conventions (structural patterns, conditional rendering, BEM class patterns)
• Find component communication patterns (inputs/outputs, props/events, signals, @Input/@Output)

Agent 2: State Management & API Services

Think (State dimension): What is the data flow — unidirectional? How does a component trigger a data load? How does it receive updates? What prevents race conditions?

Think (API dimension): Is there a service base class? What does it provide — base URL, auth headers, error mapping? Who calls the HTTP layer — directly in components or via service abstraction?

Think (Subscription dimension): What patterns prevent memory leaks? Is cleanup enforced by a linter/base class or left to developer discipline?

• Grep for state management (PlatformVmStore, Store, useReducer, createStore, defineStore, signals)
• Find API service base classes (extends.*Service, PlatformApiService, HttpClient, fetch wrappers)
• Discover data fetching patterns (interceptors, error handling, loading states, caching)
• Find subscription/cleanup patterns (untilDestroyed, takeUntil, unsubscribe, dispose callbacks)
• Look for shared/common service patterns and DI registration

Agent 3: Routing, Directives & Directory Structure

Think (Routing dimension): How are routes protected? What's the lazy-loading boundary? How are navigation events handled? Is there a routing hierarchy?

Think (Reuse dimension): What custom directives/pipes exist? Are they in a shared library? What naming conventions distinguish feature-specific from cross-cutting reusables?

Think (Organization dimension): What's the pattern for where things live — feature modules, domain libraries, shared libs? How do apps consume shared code?

• Grep for routing configuration (route definitions, guards, resolvers, lazy loading, canActivate)
• Find custom directives/pipes/hooks and their registration patterns
• Discover module/library organization (shared modules, feature modules, Nx library structure)
• Map directory structure conventions (where components, services, models, and specs live)
• Find build configuration and environment patterns (proxy configs, env-specific settings)

Phase 3: Analyze & Generate

Read full report. Apply fresh-eyes protocol:

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

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

• Does every code example exist at the claimed file:line? (Glob + Grep verify)
• Do base class names in examples match actual class definitions? (Grep verify)
• Are store method names real (not hallucinated)? (Grep verify)
• Are cleanup patterns documented with actual implementation evidence?

Target Sections

Section	Content
Component Base Classes	Hierarchy with what each base provides; when to use which
State Management	Store pattern, reactivity approach, data flow conventions
Forms	Form creation pattern, validation approach, error display
API Services	Service base class, HTTP call pattern, error handling
Routing	Route definition pattern, guards, lazy loading, navigation conventions
Directives & Pipes	Custom reusable behaviors, naming conventions, registration
Directory Structure	Where things live: components, services, models, shared code
Subscription Cleanup	How subscriptions/listeners are managed and cleaned up
Styling Conventions	Component styling approach (scoped, BEM, utility classes)

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 (Glob + Grep): ALL code example file paths exist AND class/method names match
4. Verify base class hierarchy from at least 3 concrete examples
5. Report: sections created vs updated, framework detected, gaps

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[IMPORTANT] Use TaskCreate 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 class names, method signatures, and base class hierarchies. Grep to confirm existence before documenting. Trace full dependency chain after edits. Always trace full chain. Surface ambiguity before coding. NEVER pick silently. NEVER fabricate base class names, lifecycle hooks, or store method names. Grep to confirm before documenting.

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 TaskCreate tasks BEFORE starting IMPORTANT MUST ATTENTION detect framework in Phase 0 — agent patterns depend on framework IMPORTANT MUST ATTENTION cite file:line for every code example — NEVER fabricate class or method names IMPORTANT MUST ATTENTION sub-agents write findings incrementally after each category — NEVER batch at end IMPORTANT MUST ATTENTION if Round 1 finds issues, Round 2 fresh-eyes is non-negotiable after fixing. Clean Round 1 ENDS the scan.

Anti-Rationalization:

Evasion	Rebuttal
"Framework obvious, skip Phase 0 detection"	Phase 0 is BLOCKING — grep patterns and agent scope depend on detected framework
"Base class names look right"	Grep-verify ALL base class names — AI hallucinates class hierarchies
"Store method names are standard"	Every store method name must be grep-verified against actual source
"Skip Round 2 even when Round 1 found issues"	Clean Round 1 ends the scan. When issues exist, fresh-eyes mandatory after fixing — main agent rationalizes own fabricated examples.
"Cleanup pattern documented, 1 example enough"	Cleanup is the most project-specific pattern — verify with 3+ grep hits

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