[IMPORTANT] Use

TaskCreate

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.

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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.

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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.

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Prerequisites: MUST ATTENTION READ before executing:

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

1. Read existing doc first — understand current structure and manual annotations
2. Detect mode: Placeholder (only headings, no content) → Init mode. Has content → Sync mode.
3. Scan codebase for current state (grep/glob for patterns, counts, file paths)
4. Diff findings vs doc content — identify stale sections only
5. Update ONLY sections where code diverged from doc. Preserve manual annotations.
6. Update metadata (date, counts, version) in frontmatter or header
7. NEVER rewrite entire doc. NEVER remove sections without evidence they're obsolete.

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Output Quality — Token efficiency without sacrificing quality.

1. No inventories/counts — AI can

   grep | wc -l

   . Counts go stale instantly
2. No directory trees — AI can

   glob

   /

   ls

   . Use 1-line path conventions
3. No TOCs — AI reads linearly. TOC wastes tokens
4. No examples that repeat what rules say — one example only if non-obvious
5. Lead with answer, not reasoning. Skip filler words and preamble
6. Sacrifice grammar for concision in reports
7. Unresolved questions at end, if any

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Quick Summary

Goal: Scan backend codebase and populate

docs/project-reference/backend-patterns-reference.md

with actual repository patterns, CQRS command/query structures, validation patterns, entity conventions, event handlers, and migration approaches. (content auto-injected by hook — check for [Injected: ...] header before reading)

Workflow:

1. Read — Load current target doc, detect init vs sync mode
2. Scan — Discover backend patterns via parallel sub-agents
3. Report — Write findings to external report file
4. Generate — Build/update reference doc from report
5. Verify — Validate code examples reference real files

Key Rules:

• Generic — works with any backend framework (.NET, Node.js, Java, etc.)
• Detect framework first, then scan for framework-specific patterns
• Every code example must come from actual project files with file:line references

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

Scan Backend Patterns

Phase 0: Read & Assess

1. Read

   docs/project-reference/backend-patterns-reference.md

2. Detect mode: init (placeholder) or sync (populated)
3. If sync: extract existing sections and note what's already well-documented

Phase 1: Plan Scan Strategy

Detect backend framework:

• .csproj

  files → .NET (check for MediatR, CQRS patterns)

• package.json

  with express/fastify/nestjs → Node.js

• pom.xml

  /

  build.gradle

  → Java/Kotlin

• requirements.txt

  /

  pyproject.toml

  → Python

Use

docs/project-config.json

contextGroups/modules if available for service paths.

Phase 2: Execute Scan (Parallel Sub-Agents)

Launch 3 Explore agents in parallel:

Agent 1: Repository & Entity Patterns

• Grep for repository interfaces (

  interface I*Repository

  ,

  extends Repository

  )
• Find entity/model classes (base class inheritance, attributes/annotations)
• Find DTO classes and mapping patterns (AutoMapper, manual mapping,

  MapTo*

  methods)
• Discover data access patterns (Unit of Work, DbContext, MongoDB collections)
• Look for extension methods on repositories

Agent 2: CQRS & Command/Query Patterns

• Grep for command handlers (

  IRequestHandler

  ,

  CommandHandler

  ,

  @CommandHandler

  )
• Grep for query handlers, query objects
• Find validation patterns (FluentValidation, class-level validators, middleware)
• Discover request/response wrapper patterns (Result<T>, ApiResponse)
• Find authorization attributes/decorators on handlers

Agent 3: Events, Migrations & Infrastructure

• Grep for event handlers, domain events, integration events
• Find message bus consumers/publishers (MassTransit, RabbitMQ, Kafka patterns)
• Discover migration patterns (EF migrations, Flyway, custom migrators)
• Find background job patterns (Hangfire, Quartz, hosted services)
• Grep for middleware, filters, interceptors

Write all findings to:

plans/reports/scan-backend-patterns-{YYMMDD}-{HHMM}-report.md

Phase 3: Analyze & Generate

Read the report. Build these sections:

Target Sections

Section	Content
Repository Pattern	Interface naming, base classes, service-specific repos, extension methods with examples
CQRS Patterns	Command structure, query structure, handler patterns, file organization conventions
Validation Patterns	Validation approach (fluent API, attributes, etc.), common rules, error response format
Entity Patterns	Base classes, property conventions, factory methods, domain logic placement
DTO Mapping	Mapping approach, who owns mapping (DTO, handler, or service), examples
Event Handlers	Domain events vs integration events, handler discovery, side-effect placement
Message Bus	Cross-service communication patterns, consumer conventions, message contracts
Migrations	Migration strategy, naming conventions, data migration patterns
Background Jobs	Job scheduling, recurring jobs, one-time jobs, conventions
Authorization	Auth patterns, permission checks, role-based access

Content Rules

• Show actual code snippets (5-15 lines) from the project with

  file:line

  references
• Include "DO" and "DON'T" examples where anti-patterns are clear
• Use tables for convention summaries (naming, file locations, base classes)
• Group patterns by concern, not by framework feature

Phase 4: Write & Verify

1. Write updated doc with

   <!-- Last scanned: YYYY-MM-DD -->

   at top
2. Verify: 5 code example file paths exist (Glob check)
3. Verify: class names in examples match actual class definitions
4. Report: sections updated, patterns discovered, coverage gaps

---

Closing Reminders

• IMPORTANT MUST ATTENTION break work into small todo tasks using

  TaskCreate

  BEFORE starting
• IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• IMPORTANT MUST ATTENTION cite

  file:line

  evidence for every claim (confidence >80% to act)
• IMPORTANT MUST ATTENTION add a final review todo task to verify work quality <!-- SYNC:scan-and-update-reference-doc:reminder -->
• IMPORTANT MUST ATTENTION read existing doc first, scan codebase, diff, surgical update only. Never rewrite entire doc. <!-- /SYNC:scan-and-update-reference-doc:reminder --> <!-- SYNC:output-quality-principles:reminder -->
• IMPORTANT MUST ATTENTION follow output quality rules: no counts/trees/TOCs, rules > descriptions, 1 example per pattern, primacy-recency anchoring. <!-- /SYNC:output-quality-principles:reminder --> <!-- SYNC:critical-thinking-mindset:reminder -->
• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact. <!-- /SYNC:critical-thinking-mindset:reminder --> <!-- SYNC:ai-mistake-prevention:reminder -->
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction. <!-- /SYNC:ai-mistake-prevention:reminder -->