sadd%3Alaunch-sub-agent by neolabhq/context-engineering-kit
npx skills add https://github.com/neolabhq/context-engineering-kit --skill sadd:launch-sub-agent在分派之前,系统地分析任务。逐步思考:
Let me analyze this task step by step to determine the optimal configuration:
1. **Task Type Identification**
"What type of work is being requested?"
- Code implementation / feature development
- Research / investigation / comparison
- Documentation / technical writing
- Code review / quality analysis
- Architecture / system design
- Testing / validation
- Simple transformation / lookup
2. **Complexity Assessment**
"How complex is the reasoning required?"
- High: Architecture decisions, novel problem-solving, multi-faceted analysis
- Medium: Standard implementation following patterns, moderate research
- Low: Simple transformations, lookups, well-defined single-step tasks
3. **Output Size Estimation**
"How extensive is the expected output?"
- Large: Multiple files, comprehensive documentation, extensive analysis
- Medium: Single feature, focused deliverable
- Small: Quick answer, minor change, brief output
4. **Domain Expertise Check**
"Does this task match a specialized agent profile?"
- Development: code, implement, feature, endpoint, TDD, tests
- Research: investigate, compare, evaluate, options, library
- Documentation: document, README, guide, explain, tutorial
- Architecture: design, system, structure, scalability
- Exploration: understand, navigate, find, codebase patterns
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根据任务分析选择最优模型:
| 任务概况 | 推荐模型 | 理由 |
|---|---|---|
| 复杂推理(架构、设计、关键决策) | opus | 最大推理能力 |
| 专业领域(匹配智能体配置文件) | Opus + 专业智能体 | 领域专业知识 + 推理能力 |
| 非复杂但冗长(大量文档、冗长输出) | sonnet[1m] | 良好能力,对长文本具有成本效益 |
| 简单且简短(琐碎任务、快速查找) | haiku | 快速,对简单任务具有成本效益 |
| 默认(不确定时) | opus | 质量优先于成本 |
决策树:
Is task COMPLEX (architecture, design, novel problem, critical decision)?
|
+-- YES --> Use Opus (highest capability)
| |
| +-- Does it match a specialized domain?
| +-- YES --> Include specialized agent prompt
| +-- NO --> Use Opus alone
|
+-- NO --> Is task SIMPLE and SHORT?
|
+-- YES --> Use Haiku (fast, cheap)
|
+-- NO --> Is output LONG but task not complex?
|
+-- YES --> Use Sonnet (balanced)
|
+-- NO --> Use Opus (default)
如果任务匹配专业领域,则整合相关的智能体提示。专业智能体提供特定领域的最佳实践、质量标准和结构化方法,从而提高输出质量。
决策: 当任务明显受益于领域专业知识时,使用专业智能体。对于琐碎任务,跳过以避免不必要的开销。
智能体: 可用的专业智能体取决于项目和已安装的插件。来自 sdd 插件的常见智能体包括:sdd:developer、sdd:researcher、sdd:software-architect、sdd:tech-lead、sdd:team-lead、sdd:qa-engineer、sdd:code-explorer、sdd:business-analyst。如果合适的专业智能体不可用,则回退到没有专业化的通用智能体。
与模型选择的集成:
用法:
使用这些强制组件构建子智能体提示:
## Reasoning Approach
Before taking any action, you MUST think through the problem systematically.
Let's approach this step by step:
1. "Let me first understand what is being asked..."
- What is the core objective?
- What are the explicit requirements?
- What constraints must I respect?
2. "Let me break this down into concrete steps..."
- What are the major components of this task?
- What order should I tackle them?
- What dependencies exist between steps?
3. "Let me consider what could go wrong..."
- What assumptions am I making?
- What edge cases might exist?
- What could cause this to fail?
4. "Let me verify my approach before proceeding..."
- Does my plan address all requirements?
- Is there a simpler approach?
- Am I following existing patterns?
Work through each step explicitly before implementing.
<task>
{Task description from $ARGUMENTS}
</task>
<constraints>
{Any constraints inferred from the task or conversation context}
</constraints>
<context>
{Relevant context: files, patterns, requirements, codebase information}
</context>
<output>
{Expected deliverable: format, location, structure}
</output>
## Self-Critique Loop (MANDATORY)
Before completing, you MUST verify your work. Submitting unverified work is UNACCEPTABLE.
### 1. Generate 5 Verification Questions
Create 5 questions specific to this task that test correctness and completeness. There example questions:
| # | Verification Question | Why This Matters |
|---|----------------------|------------------|
| 1 | Does my solution fully address ALL stated requirements? | Partial solutions = failed task |
| 2 | Have I verified every assumption against available evidence? | Unverified assumptions = potential failures |
| 3 | Are there edge cases or error scenarios I haven't handled? | Edge cases cause production issues |
| 4 | Does my solution follow existing patterns in the codebase? | Pattern violations create maintenance debt |
| 5 | Is my solution clear enough for someone else to understand and use? | Unclear output reduces value |
### 2. Answer Each Question with Evidence
For each question, examine your solution and provide specific evidence:
[Q1] Requirements Coverage:
- Requirement 1: [COVERED/MISSING] - [specific evidence from solution]
- Requirement 2: [COVERED/MISSING] - [specific evidence from solution]
- Gap analysis: [any gaps identified]
[Q2] Assumption Verification:
- Assumption 1: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
- Assumption 2: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
[Q3] Edge Case Analysis:
- Edge case 1: [scenario] - [HANDLED/UNHANDLED] - [how]
- Edge case 2: [scenario] - [HANDLED/UNHANDLED] - [how]
[Q4] Pattern Adherence:
- Pattern 1: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
- Pattern 2: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
[Q5] Clarity Assessment:
- Is the solution well-organized? [YES/NO]
- Are complex parts explained? [YES/NO]
- Could someone else use this immediately? [YES/NO]
### 3. Revise If Needed
If ANY verification question reveals a gap:
1. **STOP** - Do not submit incomplete work
2. **FIX** - Address the specific gap identified
3. **RE-VERIFY** - Confirm the fix resolves the issue
4. **DOCUMENT** - Note what was changed and why
CRITICAL: Do not submit until ALL verification questions have satisfactory answers with evidence.
使用任务工具以选定的配置进行分派:
Use Task tool:
- description: "Sub-agent: {brief task summary}"
- prompt: {constructed prompt with CoT prefix + task + critique suffix}
- model: {selected model - opus/sonnet/haiku}
上下文隔离提醒: 仅传递与此特定任务相关的上下文。不要传递整个对话历史。
输入: /launch-sub-agent Design a caching strategy for our API that handles 10k requests/second
分析:
选择: Opus + sdd:software-architect 智能体
分派: 使用 Opus 模型、sdd:software-architect 提示、思维链前缀、批判后缀的任务工具
输入: /launch-sub-agent Update the README to add --verbose flag to CLI options
分析:
选择: Haiku(快速、便宜、足以完成任务)
分派: 使用 Haiku 模型、基本思维链前缀、基本批判后缀的任务工具
输入: /launch-sub-agent Implement pagination for /users endpoint following patterns in /products
分析:
选择: Sonnet + sdd:developer 智能体(非复杂但需要领域专业知识)
分派: 使用 Sonnet 模型、sdd:developer 提示、思维链前缀、批判后缀的任务工具
输入: /launch-sub-agent Research authentication options for mobile app - evaluate OAuth2, SAML, passwordless
分析:
选择: Opus + sdd:researcher 智能体
分派: 使用 Opus 模型、sdd:researcher 提示、思维链前缀、批判后缀的任务工具
每周安装数
212
仓库
GitHub 星标数
699
首次出现
2026年2月19日
安装于
opencode207
codex205
github-copilot205
gemini-cli204
kimi-cli202
amp202
Before dispatching, analyze the task systematically. Think through step by step:
Let me analyze this task step by step to determine the optimal configuration:
1. **Task Type Identification**
"What type of work is being requested?"
- Code implementation / feature development
- Research / investigation / comparison
- Documentation / technical writing
- Code review / quality analysis
- Architecture / system design
- Testing / validation
- Simple transformation / lookup
2. **Complexity Assessment**
"How complex is the reasoning required?"
- High: Architecture decisions, novel problem-solving, multi-faceted analysis
- Medium: Standard implementation following patterns, moderate research
- Low: Simple transformations, lookups, well-defined single-step tasks
3. **Output Size Estimation**
"How extensive is the expected output?"
- Large: Multiple files, comprehensive documentation, extensive analysis
- Medium: Single feature, focused deliverable
- Small: Quick answer, minor change, brief output
4. **Domain Expertise Check**
"Does this task match a specialized agent profile?"
- Development: code, implement, feature, endpoint, TDD, tests
- Research: investigate, compare, evaluate, options, library
- Documentation: document, README, guide, explain, tutorial
- Architecture: design, system, structure, scalability
- Exploration: understand, navigate, find, codebase patterns
Select the optimal model based on task analysis:
| Task Profile | Recommended Model | Rationale |
|---|---|---|
| Complex reasoning (architecture, design, critical decisions) | opus | Maximum reasoning capability |
| Specialized domain (matches agent profile) | Opus + Specialized Agent | Domain expertise + reasoning power |
| Non-complex but long (extensive docs, verbose output) | sonnet[1m] | Good capability, cost-efficient for length |
| Simple and short (trivial tasks, quick lookups) | haiku | Fast, cost-effective for easy tasks |
| Default (when uncertain) |
Decision Tree:
Is task COMPLEX (architecture, design, novel problem, critical decision)?
|
+-- YES --> Use Opus (highest capability)
| |
| +-- Does it match a specialized domain?
| +-- YES --> Include specialized agent prompt
| +-- NO --> Use Opus alone
|
+-- NO --> Is task SIMPLE and SHORT?
|
+-- YES --> Use Haiku (fast, cheap)
|
+-- NO --> Is output LONG but task not complex?
|
+-- YES --> Use Sonnet (balanced)
|
+-- NO --> Use Opus (default)
If the task matches a specialized domain, incorporate the relevant agent prompt. Specialized agents provide domain-specific best practices, quality standards, and structured approaches that improve output quality.
Decision: Use specialized agent when task clearly benefits from domain expertise. Skip for trivial tasks where specialization adds unnecessary overhead.
Agents: Available specialized agents depends on project and plugins installed. Common agents from the sdd plugin include: sdd:developer, sdd:researcher, sdd:software-architect, sdd:tech-lead, sdd:team-lead, sdd:qa-engineer, sdd:code-explorer, sdd:business-analyst. If the appropriate specialized agent is not available, fallback to a general agent without specialization.
Integration with Model Selection:
Usage:
Build the sub-agent prompt with these mandatory components:
## Reasoning Approach
Before taking any action, you MUST think through the problem systematically.
Let's approach this step by step:
1. "Let me first understand what is being asked..."
- What is the core objective?
- What are the explicit requirements?
- What constraints must I respect?
2. "Let me break this down into concrete steps..."
- What are the major components of this task?
- What order should I tackle them?
- What dependencies exist between steps?
3. "Let me consider what could go wrong..."
- What assumptions am I making?
- What edge cases might exist?
- What could cause this to fail?
4. "Let me verify my approach before proceeding..."
- Does my plan address all requirements?
- Is there a simpler approach?
- Am I following existing patterns?
Work through each step explicitly before implementing.
<task>
{Task description from $ARGUMENTS}
</task>
<constraints>
{Any constraints inferred from the task or conversation context}
</constraints>
<context>
{Relevant context: files, patterns, requirements, codebase information}
</context>
<output>
{Expected deliverable: format, location, structure}
</output>
## Self-Critique Loop (MANDATORY)
Before completing, you MUST verify your work. Submitting unverified work is UNACCEPTABLE.
### 1. Generate 5 Verification Questions
Create 5 questions specific to this task that test correctness and completeness. There example questions:
| # | Verification Question | Why This Matters |
|---|----------------------|------------------|
| 1 | Does my solution fully address ALL stated requirements? | Partial solutions = failed task |
| 2 | Have I verified every assumption against available evidence? | Unverified assumptions = potential failures |
| 3 | Are there edge cases or error scenarios I haven't handled? | Edge cases cause production issues |
| 4 | Does my solution follow existing patterns in the codebase? | Pattern violations create maintenance debt |
| 5 | Is my solution clear enough for someone else to understand and use? | Unclear output reduces value |
### 2. Answer Each Question with Evidence
For each question, examine your solution and provide specific evidence:
[Q1] Requirements Coverage:
- Requirement 1: [COVERED/MISSING] - [specific evidence from solution]
- Requirement 2: [COVERED/MISSING] - [specific evidence from solution]
- Gap analysis: [any gaps identified]
[Q2] Assumption Verification:
- Assumption 1: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
- Assumption 2: [assumption made] - [VERIFIED/UNVERIFIED] - [evidence]
[Q3] Edge Case Analysis:
- Edge case 1: [scenario] - [HANDLED/UNHANDLED] - [how]
- Edge case 2: [scenario] - [HANDLED/UNHANDLED] - [how]
[Q4] Pattern Adherence:
- Pattern 1: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
- Pattern 2: [pattern name] - [FOLLOWED/DEVIATED] - [evidence]
[Q5] Clarity Assessment:
- Is the solution well-organized? [YES/NO]
- Are complex parts explained? [YES/NO]
- Could someone else use this immediately? [YES/NO]
### 3. Revise If Needed
If ANY verification question reveals a gap:
1. **STOP** - Do not submit incomplete work
2. **FIX** - Address the specific gap identified
3. **RE-VERIFY** - Confirm the fix resolves the issue
4. **DOCUMENT** - Note what was changed and why
CRITICAL: Do not submit until ALL verification questions have satisfactory answers with evidence.
Use the Task tool to dispatch with the selected configuration:
Use Task tool:
- description: "Sub-agent: {brief task summary}"
- prompt: {constructed prompt with CoT prefix + task + critique suffix}
- model: {selected model - opus/sonnet/haiku}
Context isolation reminder: Pass only context relevant to this specific task. Do not pass entire conversation history.
Input: /launch-sub-agent Design a caching strategy for our API that handles 10k requests/second
Analysis:
Selection: Opus + sdd:software-architect agent
Dispatch: Task tool with Opus model, sdd:software-architect prompt, CoT prefix, critique suffix
Input: /launch-sub-agent Update the README to add --verbose flag to CLI options
Analysis:
Selection: Haiku (fast, cheap, sufficient for task)
Dispatch: Task tool with Haiku model, basic CoT prefix, basic critique suffix
Input: /launch-sub-agent Implement pagination for /users endpoint following patterns in /products
Analysis:
Selection: Sonnet + sdd:developer agent (non-complex but needs domain expertise)
Dispatch: Task tool with Sonnet model, sdd:developer prompt, CoT prefix, critique suffix
Input: /launch-sub-agent Research authentication options for mobile app - evaluate OAuth2, SAML, passwordless
Analysis:
Selection: Opus + sdd:researcher agent
Dispatch: Task tool with Opus model, sdd:researcher prompt, CoT prefix, critique suffix
Weekly Installs
212
Repository
GitHub Stars
699
First Seen
Feb 19, 2026
Installed on
opencode207
codex205
github-copilot205
gemini-cli204
kimi-cli202
amp202
agent-browser 浏览器自动化工具 - Vercel Labs 命令行网页操作与测试
147,400 周安装
opus| Optimize for quality over cost |