[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:
Understand Code First — HARD-GATE: Do NOT write, plan, or fix until you READ existing code. 1. Search 3+ similar patterns (grep/glob) — citefile:lineevidence 2. Read existing files in target area — understand structure, base classes, conventions 3. Runpython.claude/scripts/code_graph trace <file> --direction both --jsonwhen.code-graph/graph.dbexists 4. Map dependencies viaconnectionsorcallers_of— know what depends on your target 5. Write investigation to.ai/workspace/analysis/for non-trivial tasks (3+ files) 6. Re-read analysis file before implementing — never work from memory alone 7. NEVER invent new patterns when existing ones work — match exactly or document deviation BLOCKED until:- []Read target files- []Grep 3+ patterns- []Graph trace (if graph.db exists)- []Assumptions verified with evidence
Quick Summary
Goal: Analyze and visualize dependencies between features, services, or work items to identify blockers and critical paths.
Workflow:
- Identify Scope — Single feature, module, or full release
- Classify Dependencies — Data, Service, UI, or Infrastructure types
- Build Graph — Create Mermaid dependency diagram
- Find Critical Path — Longest blocking chain; mark ready-to-start items
- Deliver Report — Summary, graph, critical path, risks
Key Rules:
- Respect microservice boundaries (cross-service = message bus only)
- Flag circular dependencies as errors
- Not for package/npm upgrades (use
package-upgradeinstead)
Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).
Dependency Mapping
Purpose
Analyze and visualize dependencies between features, services, modules, or work items to identify blockers, critical paths, and safe execution order.
When to Use
- Planning feature implementation sequence across modules
- Identifying what blocks a specific feature or work item
- Mapping cross-service dependencies (backend-to-backend, frontend-to-backend)
- Understanding critical path for a release or milestone
- Analyzing impact of changing a shared module or entity
When NOT to Use
- Single-service code changes with no cross-boundary impact -- just implement directly
- Performance analysis -- use
arch-performance-optimizationinstead - Security dependency auditing -- use
arch-security-reviewinstead - Package/npm dependency upgrades -- use
package-upgradeinstead
Prerequisites
- Read the feature/PBI/plan files to understand scope
- Access to
docs/project-reference/project-structure-reference.mdfor service boundary reference - Understand the project's microservice boundaries (search
src/Services/for service list)
Workflow
Step 1: Identify Scope
Determine what to map:
- Single feature: Find all files, services, and entities it touches
- Module/service: Map all inbound and outbound dependencies
- Release/milestone: Map all features and their inter-dependencies
Step 2: Classify Dependencies
For each dependency found, classify by type:
| Type | Direction | Description | Example |
|---|---|---|---|
| Data | Entity A requires Entity B | Foreign key, navigation property, shared ID | Employee requires Company |
| Service | Service A calls Service B | Message bus, API call, event consumer | Service A consumes entity events from Service B |
| UI | Component A embeds Component B | Shared component, library dependency | Feature form uses shared component library select |
| Infrastructure | Feature needs infra change | Database migration, config, new queue | New feature needs Redis cache key |
Step 3: Build Dependency Graph
Use Mermaid syntax for visualization:
graph TD
A[Feature A] -->|data| B[Feature B]
A -->|service| C[Feature C]
B -->|blocks| D[Feature D]
C -->|blocks| D
style D fill:#f96,stroke:#333Step 4: Identify Critical Path
- Find the longest chain of blocking dependencies
- Mark items with no blockers as "ready to start"
- Flag circular dependencies as errors
Step 5: Deliver Report
Output structured dependency report (see Output Format).
Output Format
## Dependency Map: [Feature/Module Name]
### Summary
- Total items: N
- Ready to start: N (no blockers)
- Blocked: N
- Critical path length: N steps
### Dependency Graph
[Mermaid diagram]
### Critical Path
1. [Item A] -- no blockers, estimated: Xd
2. [Item B] -- blocked by: A, estimated: Xd
3. [Item C] -- blocked by: B, estimated: Xd
### Dependency Details
| Item | Type | Depends On | Blocks | Status |
| ---- | --------------------- | ---------- | ------ | ------------- |
| ... | data/service/UI/infra | ... | ... | ready/blocked |
### Risks
- [Circular dependency / tight coupling / single point of failure]Examples
Example 1: Backend Cross-Service Feature
Input: "Map dependencies for adding a new Coaching feature in {ServiceA}"
Analysis:
graph TD
E[Employee Entity - ServiceA] -->|data| C[Coaching Entity]
U[User Entity - AuthService] -->|service| C
C -->|service| N[Notification - ServiceB]
C -->|UI| CF[Coaching Form Component]
CF -->|UI| BC[shared-components select]Critical path: Employee Entity -> Coaching Entity -> Coaching API -> Coaching Form Ready to start: Employee Entity already exists, shared component select exists Blocked: Coaching Entity creation, then API, then UI
Example 2: Frontend Module Dependency
Input: "What blocks the new Dashboard widget in {AnalyticsService}?"
Analysis:
graph TD
GA[Source API - ServiceA] -->|service| GE[Event Bus Message]
GE -->|service| GC[Consumer - AnalyticsService]
GC -->|data| GS[Summary Entity]
GS -->|UI| GW[Dashboard Widget]
GW -->|UI| DC[Dashboard Container]Blockers identified:
- Event Bus Message producer must exist in ServiceA (exists: yes)
- Consumer must be created in AnalyticsService (exists: no -- BLOCKER)
- Summary Entity for aggregated data (exists: no -- BLOCKER)
Related Skills
project-manager-- for sprint planning and status trackingfeature-implementation-- for implementing features after dependency analysisarch-cross-service-integration-- for designing cross-service communicationpackage-upgrade-- for npm/NuGet package dependency upgrades
Closing Reminders
- MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using
TaskCreateBEFORE starting - MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
- MANDATORY IMPORTANT MUST ATTENTION cite
file:lineevidence for every claim (confidence >80% to act) - MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality MANDATORY IMPORTANT MUST ATTENTION READ the following files before starting:
- MANDATORY IMPORTANT MUST ATTENTION search 3+ existing patterns and read code BEFORE any modification. Run graph trace when graph.db exists.
- 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.
[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.