[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.

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.

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.

Prerequisites: MUST ATTENTION READ before executing:

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.

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

Quick Summary

Goal: Scan the project codebase and populate

docs/project-reference/project-structure-reference.md

Workflow:

1. Read — Load current target doc, detect init vs sync mode
2. Scan — Discover services, apps, ports, tech stack via parallel sub-agents
3. Report — Write findings to external report file
4. Generate — Build/update the reference doc from report
5. Verify — Spot-check paths and ports

Key Rules:

• Generic — discover everything dynamically, never hardcode project-specific values
• Use

  docs/project-config.json

  for hints if available, fall back to filesystem scanning
• All examples must reference real files found in this project

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

Scan Project Structure

Phase 0: Read & Assess

1. Read

   docs/project-reference/project-structure-reference.md

2. Detect mode: init (placeholder only) or sync (has real content)
3. If sync: note which sections exist and their line counts

Phase 1: Plan Scan Strategy

Check if

docs/project-config.json

• Backend services — Find

  .csproj

  ,

  Dockerfile

  ,

  Program.cs

  ,

  launchSettings.json

  for ports
• Frontend apps — Find

  angular.json

  ,

  nx.json

  ,

  package.json

  ,

  vite.config

  ,

  next.config

• Infrastructure — Find

  docker-compose.yml

  ,

  Dockerfile

  , K8s manifests, CI/CD config
• Tech stack — Parse

  package.json

  dependencies,

  .csproj

  PackageReferences, build tool configs

Phase 2: Execute Scan (Parallel Sub-Agents)

Launch 3 Explore agents in parallel:

Agent 1: Backend Services

• Glob for

  **/*.csproj

  and

  **/Dockerfile

  to find services
• Grep

  launchSettings.json

  or

  appsettings*.json

  for port numbers
• Grep for

  [ApiController]

  or

  MapControllers

  to find API services
• List service directories with their ports

Agent 2: Frontend Apps

• Glob for

  **/angular.json

  ,

  **/nx.json

  ,

  **/package.json

  (not in node_modules)
• Find app entry points (

  main.ts

  ,

  index.tsx

  ,

  App.vue

  )
• Extract dev server ports from configs (

  serve

  commands, proxy configs)
• Identify framework versions from package.json

Agent 3: Infrastructure & Tech Stack

• Find

  docker-compose*.yml

  — extract service definitions and port mappings
• Find CI/CD configs (

  .github/workflows/*.yml

  ,

  azure-pipelines.yml

  ,

  Jenkinsfile

  )
• Parse primary package managers (

  package.json

  ,

  *.csproj

  ) for key dependencies
• Identify databases, message brokers, caching from connection strings or Docker services

Write all findings to:

plans/reports/scan-project-structure-{YYMMDD}-{HHMM}-report.md

Phase 3: Analyze & Generate

Read the report file. Build these sections:

Target Sections

src/

Content Rules

• Use tables for structured data (not prose)
• Include actual port numbers found in configs
• Directory tree: show only meaningful structure (skip node_modules, bin, obj)
• Tech stack: include version numbers from package.json/csproj

Phase 4: Write & Verify

1. Write updated doc with

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

   at top
2. Verify: spot-check 3 service paths exist on filesystem
3. Verify: port numbers match actual config files
4. Report: sections updated vs unchanged

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 MANDATORY IMPORTANT MUST ATTENTION READ the following before starting: <!-- 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 -->