Turn a rough request into a structured AI prompt with a clear goal, output format, and constraints. For complex work, add smaller tasks, separate creator and reviewer roles, and explicit checks for accuracy and uncertainty.
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Craft better prompts using proven optimization techniques — use when your prompt needs refinement
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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 │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
What: Break complex tasks into smaller, manageable subtasks.
When to use:
How to apply:
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
What: Use different "experts" for creation vs. validation. Never use the same expert to both create and verify.
When to use:
How to apply:
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
What: Build explicit verification steps into the task, especially for error-prone outputs.
When to use:
How to apply:
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
What: Never assume unverified facts. Disclaim uncertainty explicitly.
When to use:
How to apply:
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."
What: Spawn domain-specific personas for complex subtasks.
When to use:
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."
**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)
If information is missing, ask ONE clarifying question at a time.
After gathering requirements, analyze internally:
| 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 |
| 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 |
For the task, identify where verification is needed:
| Step | Risk | Verification Method |
|---|---|---|
| [step] | [what could go wrong] | [how to verify] |
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]
Tune the assembled prompt to the model that will execute it:
skills/blocks/frontier-model-routing.md when choosing between Opus 5, GPT-5.6, Sonnet 5, Fable 5.1, Astra, or cheaper seats.skills/blocks/fable5-prompting.md to either model ID. In short: never instruct the model to reveal or transcribe its reasoning; replace step-by-step micromanagement with a boundary plus checkable acceptance criteria; drop "CRITICAL"/"MUST" emphasis unless strict compliance is required; add grounded-progress and act-when-ready language for long runs.docs/GPT-5.6-PROMPTING.md; give it a concrete outcome, scoped repository constraints, checkable acceptance criteria, non-goals, and the required verification.**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
"Create a prompt to help me write API documentation"
# 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]
## 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]
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?
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.
Generate prompts for content creation based on anatomy guides.
Transform brainstorming insights into actionable prompts.
Enhance PRD generation with meta-prompting techniques.
Generate implementation prompts with built-in verification.
Meta-prompt → Decompose → Assign experts → Build verification → Generate
Otherwise → Vague prompts → Hallucination → Unreliable output
Structure breeds reliability. Verification breeds accuracy. Experts breed quality.
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Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.
中文简介正在准备中。当前显示英语简介。
Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.
中文简介正在准备中。当前显示英语简介。
Clarify a feature’s requirements with multiple AI providers, combining technical constraints with user and business needs. Produce a scoped definition covering priorities, edge cases, exclusions, and a requirements checklist before implementation begins.
中文简介正在准备中。当前显示英语简介。
Review code or documents with available AI providers before delivery. Combine findings into a scored validation report that checks prior requirements and decisions, separates critical issues from warnings, and recommends next steps.
中文简介正在准备中。当前显示英语简介。
Develop complex features or business documents by gathering approaches from available AI providers. Combine their perspectives with prior project decisions and quality checks to produce a proposed plan, then proceed with implementation after user confirmation.
中文简介正在准备中。当前显示英语简介。
Research technical or business questions through multiple available AI providers, each assigned a different perspective. Combine usable results into a report covering findings, disagreements, evidence gaps, priorities, and recommended next steps.
中文简介正在准备中。当前显示英语简介。