AI Prompt Optimization

Use when users need to optimize prompts for AI conversations, generate structured templates, create few-shot examples, design chain-of-thought guidance, or d...

Security Scan

This is an instruction-only prompt-optimization skill whose declared purpose, required resources, and runtime instructions are coherent and proportional; it does not request credentials, install software, or perform unexpected I/O.

Purpose & Capability

The name/description (prompt optimization, templates, few-shot, CoT) matches the SKILL.md content and the provided templates. No unrelated credentials, binaries, or config paths are declared or required.

Instruction Scope

SKILL.md contains guidance and templates for analyzing and constructing prompts; it does not instruct the agent to read local files, system configuration, environment secrets, or to call external endpoints beyond normal model invocation. Referencing the included references/templates.md is consistent with its stated purpose.

Install Mechanism

There is no install spec and no code files; the skill is instruction-only, which minimizes disk writes and execution risk.

Credentials

The skill declares no required environment variables, credentials, or config paths. The templates and workflows do not rely on external secrets or unrelated services.

Persistence & Privilege

always is false and the skill does not request elevated or persistent system presence. It does not modify other skills or system-wide settings according to the provided files.

Assessment

This skill is low-risk and internally consistent, but exercise standard caution: do not paste sensitive secrets, credentials, or private data into prompts you ask the skill to optimize; review optimized prompts for instructions that might cause a model to reveal or infer private information; test with non-sensitive examples before using in production; if you prefer to prevent autonomous use, you can disable the skill or restrict agent autonomy in your platform settings.

Core Capabilities

When users seek prompt optimization assistance, provide the following services:

1. Diagnosis & Optimization - Analyze existing prompt issues and provide specific improvement plans
2. Template Generation - Generate structured prompt templates for different scenarios
3. Few-Shot Generation - Create example-driven few-shot prompts
4. Chain-of-Thought Guidance - Design CoT (Chain of Thought) prompts

Usage

1. Diagnosis & Optimization Workflow

When a user provides a prompt for optimization:

Analyze Structure → Identify Issues → Provide Improved Version → Explain Changes

Diagnosis Checklist:

• Is the role/identity clearly defined?
• Is the task objective specific and clear?
• Are output format/style constrained?
• Is the necessary context/background information provided?
• Are boundary conditions and exceptions specified?
• Are there clear success criteria?

2. Template Generation

Generate structured templates based on user scenarios. Core template format:

# Role Definition
You are a [role] in [professional domain], skilled at [core competency].

# Task Description
Please help me [specific task], with the goal of [expected outcome].

# Context Information
- Background: [relevant background]
- Audience: [target users]
- Constraints: [boundary conditions]

# Output Requirements
- Format: [desired format]
- Style: [language style]
- Length: [length requirement]

# Quality Standards
[Key metrics for evaluating output]

3. Few-Shot Example Generation

Generate few-shot examples for complex tasks:

1. Select Representative Samples - 3-5 examples covering different variants
2. Format Examples - Input → Output structure
3. Add Explanations - Explain the rationale for selecting each example

4. Chain-of-Thought Design

Design CoT prompts for tasks requiring reasoning:

Before giving your final answer, please think through the following steps:
1. [Understand the Problem] - ...
2. [Decompose the Problem] - ...
3. [Step-by-Step Reasoning] - ...
4. [Verify the Conclusion] - ...

Scenario Reference

For complete scenario templates and examples, see references/templates.md:

• Writing assistance prompts
• Code generation prompts
• Image generation prompts
• Data analysis prompts
• Q&A and consultation prompts

Optimization Principles

1. Specific > Vague - Clearly specify what is wanted and what is not
2. Structured > Scattered - Use clear segmentation and markers
3. Constrained > Free - Appropriate constraints improve output quality
4. Iterative > One-Shot - Encourage users to continuously optimize based on output