Agent 生成器
- 作者仓库星标 3,406
- 作者更新于 实时读取
- 作者仓库 claude-octopus
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @nyldn · 未声明 license
- Token 消耗评级
- 较高消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- Python
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: skill-meta-prompt
description: Craft better prompts using proven optimization techniques — use when your prompt needs refinemen…
category: AI 智能
runtime: Python
---
# skill-meta-prompt 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Overview / The Five Techniques / Technique 1: Task Decomposition”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Overview / The Five Techniques / Technique 1: Task Decomposition”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文出现了 `/octo` 这类斜杠命令;如果你的 Agent 支持命令触发,优先用命令开场,再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Overview / The Five Techniques / Technique 1: Task Decomposition”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: skill-meta-prompt
description: Craft better prompts using proven optimization techniques — use when your prompt needs refinemen…
category: AI 智能
source: nyldn/claude-octopus
---
# skill-meta-prompt
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Overview / The Five Techniques / Technique 1: Task Decomposition」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "skill-meta-prompt" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Overview / The Five Techniques / Technique 1: Task Decomposition
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than
/octo:*slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, seeskills/blocks/codex-host-adapter.md.
Meta-Prompt Generator Skill
Overview
Generate well-structured, verifiable prompts for any use case. Applies proven meta-prompting techniques to minimize hallucination and maximize effectiveness.
┌─────────────────────────────────────────────────────────────────────────────┐
│ META-PROMPT GENERATION │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ Phase 1: Requirement Gathering │
│ → Understand the primary goal/role │
│ → Clarify expected outputs │
│ → Identify accuracy requirements │
│ ↓ │
│ Phase 2: Task Analysis │
│ → Apply Technique 1: Task Decomposition │
│ → Identify if complex enough for subtasks │
│ → Map dependencies between subtasks │
│ ↓ │
│ Phase 3: Expert Assignment │
│ → Apply Technique 5: Specialized Experts │
│ → Assign personas to subtasks │
│ → Apply Technique 2: Fresh Eyes Review │
│ ↓ │
│ Phase 4: Verification Design │
│ → Apply Technique 3: Iterative Verification │
│ → Build in checking steps │
│ → Apply Technique 4: No Guessing │
│ ↓ │
│ Phase 5: Prompt Assembly │
│ → Structure: Role, Context, Instructions, Constraints, Format │
│ → Add verification hooks │
│ → Include uncertainty disclaimers │
│ ↓ │
│ Phase 6: Output & Iteration │
│ → Present generated prompt │
│ → Offer refinement │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
The Five Techniques
Technique 1: Task Decomposition
What: Break complex tasks into smaller, manageable subtasks.
When to use:
- Task has multiple distinct steps
- Different expertise needed for different parts
- Risk of getting lost in complexity
How to apply:
- List all components of the task
- Identify dependencies (what must happen first)
- Group related components
- Order by logical sequence
Example:
Task: "Create a technical blog post about OAuth 2.0"
Decomposition:
1. Research Phase
- Gather OAuth 2.0 specifications
- Find common implementation examples
- Identify security best practices
2. Structure Phase
- Outline main sections
- Plan code examples
- Design diagrams/visuals
3. Writing Phase
- Write introduction
- Write technical sections
- Write conclusion/CTA
4. Review Phase
- Technical accuracy check
- Code example testing
- Readability review
Technique 2: Fresh Eyes Review
What: Use different "experts" for creation vs. validation. Never use the same expert to both create and verify.
When to use:
- Output needs to be accurate
- Risk of blind spots from creator
- Quality assurance is critical
How to apply:
- Assign Creator Expert for initial work
- Assign different Reviewer Expert for validation
- Reviewer should not have seen creation process
- Loop back to Creator if issues found
Example:
Creator: "Expert Technical Writer" produces article
Reviewer: "Expert Security Engineer" verifies OAuth claims
Reviewer: "Expert Developer" tests code examples
NOT: Same expert writes AND reviews their own work
Technique 3: Iterative Verification
What: Build explicit verification steps into the task, especially for error-prone outputs.
When to use:
- Mathematical calculations
- Code generation
- Factual claims
- Multi-step reasoning
How to apply:
- After each significant output, add verification step
- For calculations: "Now verify this by [alternative method]"
- For code: "Test this code against [test cases]"
- For claims: "Confirm this by [citing source]"
Example:
Step 1: Calculate discount price
Step 2: VERIFY - recalculate from opposite direction
Step 3: If mismatch, identify error and recalculate
Step 4: Only proceed when both methods match
Technique 4: No Guessing
What: Never assume unverified facts. Disclaim uncertainty explicitly.
When to use:
- ALWAYS (this is a default behavior)
- Especially for: dates, statistics, quotes, technical specifications
How to apply:
- If uncertain, say "I'm not certain about..."
- If no data, say "I don't have information on..."
- Ask for sources rather than inventing
- Distinguish between "likely" and "confirmed"
Disclaimer templates:
"Note: This figure is approximate and should be verified."
"I don't have access to [specific data]. Please provide or verify."
"This is based on general patterns; your specific case may differ."
Technique 5: Specialized Experts
What: Spawn domain-specific personas for complex subtasks.
When to use:
- Task requires specialized knowledge
- Different perspectives would improve quality
- Cross-functional work needed
Available expert archetypes:
| Expert | Use For |
|---|---|
| Expert Writer | Content, copy, documentation |
| Expert Mathematician | Calculations, proofs, statistics |
| Expert Python | Python code, data analysis |
| Expert Security | Security review, threat modeling |
| Expert Architect | System design, trade-offs |
| Expert Reviewer | Quality assurance, error-finding |
| Expert Strategist | Planning, prioritization |
How to apply:
"For this subtask, adopt the persona of Expert [X].
Your expertise includes [specific areas].
Focus exclusively on [your assigned task].
You have no memory of previous context—all needed information is below."
Phase 1: Requirement Gathering
Initial Prompt
**Meta-Prompt Generator**
I'll help you create an effective, verifiable prompt.
**Questions:**
1. **What is the main goal?**
What should this prompt help someone accomplish?
2. **What's the expected output?**
(e.g., document, code, analysis, decision)
3. **How important is accuracy?**
- Critical (factual, technical, or high-stakes)
- Moderate (useful but not mission-critical)
- Flexible (creative, exploratory)
4. **Any specific constraints?**
(length, format, tone, tools available)
Minimum Information Needed
- Primary goal (REQUIRED)
- Output type (REQUIRED)
- Accuracy requirements (can assume moderate)
- Constraints (optional, will use sensible defaults)
If information is missing, ask ONE clarifying question at a time.
Phase 2-4: Analysis & Design
After gathering requirements, analyze internally:
Task Complexity Assessment
| Complexity | Indicators | Approach |
|---|---|---|
| Simple | Single step, one output | Direct prompt, no decomposition |
| Moderate | 2-3 steps, clear sequence | Light decomposition, one expert |
| Complex | 4+ steps, dependencies | Full decomposition, multiple experts |
Expert Assignment Matrix
| Task Type | Creator Expert | Reviewer Expert |
|---|---|---|
| Technical writing | Expert Writer | Expert Engineer |
| Code generation | Expert Developer | Expert Reviewer |
| Analysis | Expert Analyst | Expert Strategist |
| Creative | Expert Creative | Expert Editor |
Verification Points
For the task, identify where verification is needed:
| Step | Risk | Verification Method |
|---|---|---|
| [step] | [what could go wrong] | [how to verify] |
Phase 5: Prompt Assembly
Output Format
You MUST return the generated prompt in this exact format:
# [Prompt Title]
## Role
[Short, direct role definition]
[Emphasize verification and uncertainty disclaimers]
## Context
[User's task and goals]
[Background information provided]
[Clarifications gathered]
## Instructions
### Phase 1: [First Phase Name]
1. [Step 1]
2. [Step 2]
3. **Verification:** [How to verify this phase]
### Phase 2: [Second Phase Name]
1. [Step 1]
2. [Step 2]
3. **Verification:** [How to verify this phase]
[Continue phases as needed...]
### Expert Assignments (if applicable)
- **[Expert Type]:** Handles [specific subtask]
- **[Reviewer Type]:** Validates [what they check]
## Constraints
- [Constraint 1]
- [Constraint 2]
- [Accuracy requirement: how to handle uncertainty]
## Output Format
[Specify exactly how the output should be structured]
[Include all required sections]
## Verification Checklist
Before considering complete:
- [ ] [Verification item 1]
- [ ] [Verification item 2]
- [ ] [Accuracy disclaimers added where needed]
## Examples (if provided)
[Context or examples from user]
Phase 6: Output & Iteration
Present the Prompt
**Generated Meta-Prompt**
[The prompt in the format above]
**Techniques Applied:**
- ✓ Task Decomposition: [Yes/No, how]
- ✓ Fresh Eyes Review: [Yes/No, which experts]
- ✓ Iterative Verification: [Yes/No, where]
- ✓ No Guessing: [Where disclaimers needed]
- ✓ Specialized Experts: [Which ones assigned]
**Would you like me to:**
1. Refine any section
2. Add more verification steps
3. Adjust the complexity level
4. Execute this prompt now
Example: Complete Meta-Prompt Generation
User Request
"Create a prompt to help me write API documentation"
Generated Prompt
# API Documentation Generator
## Role
You are an Expert Technical Writer specializing in API documentation.
Your documentation will be reviewed by an Expert Developer for accuracy.
When uncertain about behavior, note "Verify with implementation" rather than guessing.
## Context
Creating comprehensive API documentation for developers.
Documentation should be usable without reading source code.
## Instructions
### Phase 1: API Discovery
1. List all endpoints with methods (GET, POST, etc.)
2. Document request/response schemas
3. Identify authentication requirements
4. **Verification:** Cross-reference with OpenAPI spec if available
### Phase 2: Documentation Writing
1. Write endpoint descriptions (what it does, not how)
2. Create request examples with all parameters
3. Create response examples for success and error cases
4. Document rate limits and constraints
5. **Verification:** Each example should be valid JSON/code
### Phase 3: Review Cycle
Expert Developer reviews for:
- Technical accuracy of examples
- Missing edge cases
- Unclear descriptions
### Expert Assignments
- **Expert Technical Writer:** Creates documentation prose
- **Expert Developer:** Validates examples and accuracy
## Constraints
- Use consistent terminology throughout
- Examples must be syntactically valid
- Note any undocumented or unclear behaviors
- Accuracy: Mark assumptions with "Assumed behavior - verify"
## Output Format
```markdown
# [Endpoint Name]
**Method:** [HTTP method]
**Path:** [/api/path]
**Auth:** [Required/Optional/None]
## Description
[What this endpoint does]
## Request
[Parameters, body schema, headers]
## Response
[Success and error responses with examples]
## Notes
[Rate limits, deprecation, related endpoints]
Verification Checklist
- All endpoints documented
- All examples are valid
- Authentication clearly specified
- Error responses included
- Assumptions marked for verification
## Error Handling
### Unclear Requirements
```markdown
I need a bit more clarity to create an effective prompt.
**Specifically:**
[Question about the unclear part]
[Offer 2-3 options if applicable]
Over-Complex Request
This task has [N] distinct components. I recommend:
1. **Split into multiple prompts** - One per major component
2. **Simplify scope** - Focus on [core element] first
3. **Proceed as-is** - Full complexity, longer prompt
Which approach works best for you?
Can't Apply Techniques
If techniques don't fit the task:
ℹ️ **Note on Techniques**
This task is straightforward enough that some techniques
don't apply:
- Task Decomposition: Not needed (single step)
- Fresh Eyes: [Explain why/why not]
- Specialized Experts: Not needed (single domain)
The generated prompt focuses on clarity and verification instead.
Integration
With skill-content-pipeline
Generate prompts for content creation based on anatomy guides.
With skill-thought-partner
Transform brainstorming insights into actionable prompts.
With skill-prd
Enhance PRD generation with meta-prompting techniques.
With flow-develop
Generate implementation prompts with built-in verification.
The Bottom Line
Meta-prompt → Decompose → Assign experts → Build verification → Generate
Otherwise → Vague prompts → Hallucination → Unreliable output
Structure breeds reliability. Verification breeds accuracy. Experts breed quality.
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核