Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
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Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
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实测评分 未实测
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Creates structured, context-rich prompts for specialist agents that maximize their effectiveness and minimize back-and-forth.
Specialist agents work best with:
Without this structure, agents may:
Every delegation prompt follows this format:
## CONTEXT
### Business Goal
[What business outcome this serves]
### Current State
[Relevant existing systems, constraints, decisions]
### Key Constraints
- [Constraint 1: e.g., "Budget: < $10K/month infrastructure"]
- [Constraint 2: e.g., "Timeline: MVP in 8 weeks"]
- [Constraint 3: e.g., "Team: 3 senior engineers, Python/React expertise"]
### Background Information
[Any relevant context from clarification or previous agents]
---
## TASK
### Primary Deliverable
[Exactly what output is expected]
### Format Requirements
[Structure, sections, level of detail expected]
### Scope Boundaries
- **In scope**: [What to cover]
- **Out of scope**: [What to explicitly exclude]
---
## REQUIREMENTS
### Must-Haves
- [Critical requirement 1]
- [Critical requirement 2]
### Nice-to-Haves
- [Optional enhancement 1]
- [Optional enhancement 2]
### Quality Criteria
- [Criterion 1: e.g., "Architecture must support 10x growth"]
- [Criterion 2: e.g., "Trade-offs explicitly documented"]
### Integration Points
- [What this output feeds into: e.g., "Will be validated by strategic-cto-mentor"]
- [What depends on this: e.g., "Development team will implement from this"]
---
## ADDITIONAL CONTEXT
[Any other relevant information, links to documentation, previous decisions, etc.]
See the prompt-templates folder for pre-built templates:
Business Goal: Be specific about outcomes, not activities
Current State: Include what exists and what's working
Constraints: Be explicit about non-negotiables
Primary Deliverable: One clear output
Format Requirements: Specify structure
Scope Boundaries: Prevent scope creep
Must-Haves vs Nice-to-Haves: Force prioritization
Quality Criteria: Measurable success
Integration Points: Connect the workflow
Bad: Copying entire conversation history into delegation Good: Distill to relevant context only
Bad: "Design a good system" Good: "Design a notification system architecture that supports 100K users, uses our existing PostgreSQL database, and costs < $2K/month"
Bad: Letting agent assume unlimited budget/time Good: Explicitly stating constraints, even if flexible
Bad: No mention of what happens next Good: "This design will be validated by strategic-cto-mentor before implementation begins"
## CONTEXT
### Business Goal
Enable customers to receive real-time notifications for order status changes, reducing support tickets about "where's my order" by 60%.
### Current State
- Monolithic Node.js backend, PostgreSQL database
- Notifications currently sent via email batch (hourly)
- 50K active users, expecting 200K in 12 months
- Mobile app (React Native) and web app (React)
### Key Constraints
- Budget: < $3K/month additional infrastructure
- Timeline: MVP in 6 weeks, full rollout in 10 weeks
- Team: 2 backend engineers, 1 mobile engineer
- Must integrate with existing authentication system
### Background Information
User research shows 73% of support tickets are order status questions. Push notifications tested well in user interviews.
---
## TASK
### Primary Deliverable
System architecture design for real-time notification system
### Format Requirements
Standard 7-section architecture document:
1. Executive Summary
2. System Architecture (with diagrams)
3. Technology Stack Justification
4. Implementation Roadmap
5. Risk Assessment
6. Code Examples (WebSocket integration)
7. Deployment Strategy
### Scope Boundaries
- **In scope**: Backend notification service, mobile push integration, delivery tracking
- **Out of scope**: Email notifications (keep existing), SMS notifications (Phase 2)
---
## REQUIREMENTS
### Must-Haves
- Real-time delivery (< 5 second latency)
- Support for 200K users with 20% daily active
- Push notifications on iOS and Android
- Fallback to email if push fails
### Nice-to-Haves
- Notification preferences per user
- Read receipts / delivery confirmation
- Rich notifications with images
### Quality Criteria
- p95 latency < 5 seconds from event to notification
- 99.9% delivery success rate
- Infrastructure cost < $3K/month at 200K users
### Integration Points
- Will be validated by strategic-cto-mentor before implementation
- Development team will implement from this architecture
- Must integrate with existing user service for preferences
---
## ADDITIONAL CONTEXT
Previous attempt at WebSockets failed due to connection management complexity. Team prefers managed solutions where possible. AWS is our cloud provider.
Before sending delegation prompt, verify:
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Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
中文简介正在准备中。当前显示原文说明。
Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.
中文简介正在准备中。当前显示原文说明。
Identify and challenge implicit assumptions in plans, proposals, and technical decisions. Use when strategic-cto-mentor needs to surface hidden assumptions and wishful thinking before they become costly mistakes.
中文简介正在准备中。当前显示原文说明。
Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.
中文简介正在准备中。当前显示原文说明。
Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.
中文简介正在准备中。当前显示原文说明。
Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
中文简介正在准备中。当前显示原文说明。