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438 downloads. Trust evidence available

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Prompt Optimizer

Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt nee... Skill: Prompt Optimizer Owner: autogame-17 Summary: Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt nee... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-02-17T15:04:16.804Z | user batch sync Archive index: Archive v1.0.0: 7 files, 16817 bytes Files: _meta.json (135b), index.js (3749b), package.json (351b), re

OpenClaw

View on ClawHub

Rank

Safety

Downloads

438

Updated

Apr 15, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 438 downloads reported by the source. Last updated 4/15/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Apr 15, 2026
Protocol compatibility: OpenClawcompatibility · observed Apr 15, 2026
Adoption signal: 438 downloadsadoption · observed Apr 15, 2026
Latest release: 1.0.0release · observed Feb 17, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install kn7apafdj4thknczrgxdzfd2v1808svf:prompt-optimizer

Node.js workspace detected. Install dependencies securely: run `npm ci --ignore-scripts` to prevent post-install lifecycle triggers from running arbitrary code, then selectively audit the dependency tree.
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-autogame-17-prompt-optimizer/snapshot"

Documentation

CLAWHUB

37,987 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: prompt-optimizer
description: Evaluate, optimize, and enhance prompts using 58 proven prompting techniques. Use when user asks to improve, optimize, or analyze a prompt; when a prompt needs better clarity, specificity, or structure; or when generating prompt variations for different use cases. Covers quality assessment, targeted improvements, and automatic optimization across techniques like CoT, few-shot learning, role-play, and 50+ more.
---

# Prompt Optimizer

A Node.js implementation of 58 proven prompting techniques cataloged in `references/prompt-techniques.md`.

## Usage

### 1. List Available Techniques
See all 58 techniques with their IDs and descriptions.
```bash
node skills/prompt-optimizer/index.js list
```

### 2. Get Technique Details
View the template and purpose of a specific technique.
```bash
node skills/prompt-optimizer/index.js get <technique_name>
```
Example: `node skills/prompt-optimizer/index.js get "Chain of Thought"`

### 3. Optimize a Prompt
Apply a specific technique's template to your prompt.
```bash
node skills/prompt-optimizer/index.js optimize "<your_prompt>" --technique "<technique_name>"
```
Example:
```bash
node skills/prompt-optimizer/index.js optimize "Write a python script to reverse a string" --technique "Chain of Thought"
```

## References
- `references/prompt-techniques.md`: Full catalog of techniques.
- `references/quality-framework.md`: Framework for evaluating prompt quality manually.

_meta.json

{
  "ownerId": "kn7apafdj4thknczrgxdzfd2v1808svf",
  "slug": "prompt-optimizer",
  "version": "1.0.0",
  "publishedAt": 1771340656804
}

references/prompt-techniques.md

# Prompt Techniques Catalog

Complete catalog of 58 proven prompting techniques organized by category.

## Table of Contents

- [Reasoning Techniques](#reasoning-techniques)
- [Context Techniques](#context-techniques)
- [Creative Techniques](#creative-techniques)
- [Structural Techniques](#structural-techniques)
- [Control Techniques](#control-techniques)
- [Meta Techniques](#meta-techniques)

---

## Reasoning Techniques

### 1. Chain of Thought (CoT)
**Purpose:** Encourage step-by-step reasoning before final answer
**When to use:** Math problems, logic puzzles, complex analysis
**Template:**
```
[Task]

Let's think step by step:
1. [First step reasoning]
2. [Second step reasoning]
3. [Continue reasoning...]

Final answer: [Conclusion]
```
**Example:** "Solve: 2x + 5 = 13. Let's think step by step: 1. Subtract 5 from both sides: 2x = 8. 2. Divide by 2: x = 4."

---

### 2. Tree of Thoughts
**Purpose:** Explore multiple reasoning branches before conclusion
**When to use:** Complex decisions, strategic planning, creative problem solving
**Template:**
```
[Task]

Explore multiple possible approaches:

Approach 1: [Description]
- Reasoning: [Why this approach]
- Outcome: [Expected result]

Approach 2: [Description]
- Reasoning: [Why this approach]
- Outcome: [Expected result]

Compare approaches and select the best one.
```

---

### 3. Least-to-Most Prompting
**Purpose:** Break complex tasks into sub-problems solved sequentially
**When to use:** Multi-step reasoning, complex analysis
**Template:**
```
[Complex Task]

First, identify the sub-problems that need to be solved:
1. [Sub-problem 1]
2. [Sub-problem 2]
3. [Sub-problem 3]

Now solve them in order:
1. [Solve sub-problem 1]
2. [Solve sub-problem 2 using results from 1]
3. [Solve sub-problem 3 using results from 1-2]

Final solution: [Combine results]
```

---

### 4. Self-Consistency
**Purpose:** Generate multiple reasoning paths and select most consistent answer
**When to use:** Ambiguous problems, tasks requiring high confidence
**Template:**
```
[Task]

Generate 3 different approaches to solve this:

Approach 1:
[Reasoning 1]
Answer: [Answer 1]

Approach 2:
[Reasoning 2]
Answer: [Answer 2]

Approach 3:
[Reasoning 3]
Answer: [Answer 3]

Most common answer: [Select consistent answer]
```

---

### 5. Reasoning via Planning
**Purpose:** Explicitly plan execution before acting
**When to use:** Multi-stage tasks, projects, workflows
**Template:**
```
[Task]

Planning Phase:
1. What are the key milestones?
2. What resources are needed?
3. What are potential obstacles?

Execution Plan:
Step 1: [Description]
Step 2: [Description]
Step 3: [Description]

Now execute the plan:
[Detailed execution]
```

---

### 6. Decomposition
**Purpose:** Break down complex problems into manageable components
**When to use:** Large-scale analysis, system design
**Template:**
```
[Complex Problem]

Decompose into components:

Component 1: [Name]
- Definition: [What it includes]
- Considerations: [Key factors]

Compon

references/quality-framework.md

# Prompt Quality Evaluation Framework

Systematic framework for evaluating prompt quality across multiple dimensions.

## Table of Contents

- [Evaluation Dimensions](#evaluation-dimensions)
- [Scoring Rubric](#scoring-rubric)
- [Quality Assessment Process](#quality-assessment-process)
- [Common Anti-Patterns](#common-anti-patterns)
- [Quality Benchmarks](#quality-benchmarks)

---

## Evaluation Dimensions

### 1. Clarity (清晰度)

**Definition:** How unambiguous and easy to understand the prompt is.

**Key Questions:**
- Can the task be understood on first read?
- Are there multiple possible interpretations?
- Is the language precise and specific?
- Are technical terms defined if needed?

**Indicators of Good Clarity:**
✓ Clear, unambiguous language
✓ Specific task description
✓ Defined technical terms
✓ Single interpretation

**Indicators of Poor Clarity:**
✗ Vague or ambiguous phrasing
✗ Multiple possible interpretations
✗ Undefined jargon
✗ Unclear what's being asked

**Examples:**
- Poor: "Write something about AI"
- Good: "Write a 500-word article about the impact of AI on healthcare"
- Excellent: "Write a 500-word article for healthcare professionals about three specific ways AI is transforming patient care, including one real-world example for each"

---

### 2. Specificity (具体性)

**Definition:** How well the prompt defines requirements, constraints, and expectations.

**Key Questions:**
- Are the deliverables clearly defined?
- Are constraints (length, format, style) specified?
- Is the scope clearly bounded?
- Are success criteria explicit?

**Indicators of Good Specificity:**
✓ Clear deliverable definition
✓ Explicit constraints
✓ Bounded scope
✓ Defined success criteria

**Indicators of Poor Specificity:**
✗ Open-ended without boundaries
✗ No format or length guidance
✗ Unclear what "good" looks like
✗ Missing context about audience/purpose

**Examples:**
- Poor: "Write an essay about climate change"
- Good: "Write a 1,200-word persuasive essay about climate change for high school students, arguing for renewable energy investment"
- Excellent: "Write a 1,200-word persuasive essay for high school students arguing that governments should increase renewable energy investment by 50% over the next 5 years. Include: 1) three specific benefits, 2) address two counterarguments, 3) end with a call to action for students"

---

### 3. Structure (结构)

**Definition:** How well-organized and logical the prompt is.

**Key Questions:**
- Is information organized logically?
- Are related concepts grouped together?
- Is there a clear flow from context to task?
- Are complex tasks broken down?

**Indicators of Good Structure:**
✓ Logical organization
✓ Clear sections or components
✓ Appropriate ordering of information
✓ Complex tasks broken into steps

**Indicators of Poor Structure:**
✗ Disorganized information
✓ Jumping between topics
✓ Important details buried
✓ No clear flow or progression

**Examples:**
- Poor: "Here are some things: the deadline is

package.json

{
  "name": "prompt-optimizer",
  "version": "1.0.0",
  "description": "Evaluate, optimize, and enhance prompts using 58 proven prompting techniques.",
  "main": "index.js",
  "scripts": {
    "test": "node index.js list"
  },
  "keywords": [
    "prompt",
    "optimizer",
    "ai",
    "techniques"
  ],
  "author": "OpenClaw",
  "license": "MIT"
}

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

About

Avoid when

Public facts

Install and run

Documentation

Extracted files

SKILL.md

_meta.json

references/prompt-techniques.md

references/quality-framework.md

package.json

Related agents

activepieces

cherry-studio

AionUi

CopilotKit

Machine-readable data