[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 — When updating reference docs: (1) Read existing doc first. (2) Scan codebase for current state (grep/glob). (3) Diff findings vs doc content. (4) Update ONLY sections where code diverged from doc. (5) Preserve manual annotations. (6) Update metadata (date, counts). NEVER rewrite entire doc — surgical updates only.

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Output Quality — 10 rules for reference docs: (1) No inventories/counts, (2) No directory trees, (3) No TOCs, (4) Rules over descriptions, (5) 1 example per pattern, (6) Tables over prose, (7) Primacy-recency anchoring (critical rules in first+last 5 lines), (8) No checkbox checklists — use "MUST ATTENTION verify X", (9) Min density: 8 MUST ATTENTION/NEVER/ALWAYS per 100 lines, (10) Verify base class names and code examples preserved.

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Quick Summary

Goal: Scan existing business feature documentation and populate

docs/project-reference/feature-docs-reference.md

with app-to-service mapping, documentation structure conventions, template usage, and section standards.

Workflow:

1. Read — Load current target doc, detect init vs sync mode
2. Scan — Discover documentation patterns via parallel sub-agents
3. Report — Write findings to external report file
4. Generate — Build/update reference doc from report
5. Verify — Validate discovered paths and templates exist

Key Rules:

• Generic — works with any documentation structure (docs/, wiki/, etc.)
• Discover documentation organization dynamically from file system
• Map relationships between apps/services and their documentation
• Every reference must point to 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 Feature Docs

Phase 0: Read & Assess

1. Read

   docs/project-reference/feature-docs-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

Discover documentation locations:

• docs/

  directory structure (business features, architecture, guides)

• docs/business-features/

  or similar feature doc directories

• docs/templates/

  or similar template directories
• README.md files across service directories
• Wiki or external doc references in config files

Use

docs/project-config.json

if available for module lists and app mappings.

Phase 2: Execute Scan (Parallel Sub-Agents)

Launch 2 Explore agents in parallel:

Agent 1: Documentation Structure

• Glob for

  docs/**/*.md

  to map full documentation tree
• Find documentation templates (template files, skeleton docs)
• Discover documentation section patterns (recurring H2/H3 headings across docs)
• Identify INDEX.md / README.md hub files and their link structures
• Count docs per app/module to assess coverage distribution
• Identify documentation naming patterns across feature docs

Agent 2: App-to-Service Mapping

• Map frontend apps to backend services (from config, imports, or API calls)
• Find API reference docs and their relationship to services
• Discover troubleshooting docs and their coverage
• Find cross-references between docs (links, mentions)
• Identify documentation conventions (naming, numbering, tagging patterns)
• Look for doc generation tools or scripts

Write all findings to:

plans/reports/scan-feature-docs-{YYMMDD}-{HHMM}-report.md

Phase 3: Analyze & Generate

Read the report. Build these sections:

Target Sections

Section	Content
App-to-Service Mapping	Table: App name, Backend services, Doc directory, Doc count
Directory Structure	Tree showing docs/ organization with purpose annotations
Template Paths	Table: Template name, Path, Purpose, Used by N docs
Section Structure	Standard sections found across feature docs (with frequency)
Documentation Conventions	Naming conventions, numbering schemes, required fields
Evidence Rules	How docs reference code (file:line patterns, test case IDs)
Coverage Gaps	Apps/services without documentation, incomplete docs

Content Rules

• Use tables for all structured data (mappings, templates, conventions)
• Include actual directory tree output (top 3 levels)
• Show section heading patterns with frequency counts
• Highlight well-documented vs under-documented areas

Phase 4: Write & Verify

1. Write updated doc with

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

   at top
2. Verify: 3 template paths exist on filesystem
3. Verify: app-to-service mappings match actual directory structure
4. Report: sections updated, coverage statistics, gaps identified

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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: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 -->