Co-Pilot / 辅助式
更新于 24 days ago

skill-creator-skill

Oobserverw
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observerw/skill-creator-skill
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Agent 评分

💡 摘要

该技能指导用户创建有效的、模块化的AI代理技能,具有专业知识和工作流程。

🎯 适合人群

希望增强代理能力的AI开发者需要创建特定领域技能的产品经理记录技能创建过程的技术写作者将专业工具集成到AI工作流程中的数据科学家设计定制AI解决方案的业务分析师

🤖 AI 吐槽:看起来很能打,但别让配置把人劝退。

安全分析中风险

风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发);API Key/Token 的获取、存储与泄露风险;文件读写范围与路径穿越风险。以最小权限运行,并在生产环境启用前审计代码与依赖。


name: skill-creator description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends agent' capabilities with specialized knowledge, workflows, or tool integrations. license: LICENSE

Skill Creator

This skill provides guidance for creating effective skills.

About Skills

Skills are modular, self-contained packages that extend agent' capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform agent from general-purpose assistants into specialized assistants equipped with procedural knowledge that no model can fully possess.

What Skills Provide

  1. Specialized workflows - Multi-step procedures for specific domains
  2. Tool integrations - Instructions for working with specific file formats or APIs
  3. Domain expertise - Company-specific knowledge, schemas, business logic
  4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks

Core Principles

Concise is Key

The context window is a public good. Skills share the context window with everything else agent need: system prompt, conversation history, other Skills' metadata, and the actual user request.

Default assumption: agent are already very smart. Only add context agent don't already have. Challenge each piece of information: "Does an agent really need this explanation?" and "Does this paragraph justify its token cost?"

Prefer concise examples over verbose explanations.

Set Appropriate Degrees of Freedom

Match the level of specificity to the task's fragility and variability:

  • High freedom (text-based instructions): Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
  • Medium freedom (pseudocode or scripts with parameters): Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
  • Low freedom (specific scripts, few parameters): Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.

Think of agent as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).

Anatomy of a Skill

Every skill consists of a required SKILL.md file and optional bundled resources:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter metadata (required)
│   │   ├── name: (required)
│   │   └── description: (required)
│   └── Markdown instructions (required)
└── Bundled Resources (optional)
    ├── scripts/          - Executable code (Python/Bash/etc.)
    ├── references/       - Documentation intended to be loaded into context as needed
    └── assets/           - Files used in output (templates, icons, fonts, etc.)

SKILL.md (required)

Every SKILL.md consists of:

  • Frontmatter (YAML): Contains name and description fields. These are the only fields that agent read to determine when the skill gets used, thus it is very important to be clear and comprehensive in describing what the skill is, and when it should be used.
  • Body (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers (if at all).

Bundled Resources (optional)

Scripts (scripts/)

Executable code files for tasks that require deterministic reliability. For detailed guidance on creating and using scripts in skills, see references/scripts.md.

References (references/)

Documentation and reference material intended to be loaded as needed into context to inform agent' process and thinking.

  • When to include: For documentation that agent should reference while working
  • Examples: references/finance.md for financial schemas, references/mnda.md for company NDA template, references/policies.md for company policies, references/api_docs.md for API specifications
  • Use cases: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
  • Benefits: Keeps SKILL.md lean, loaded only when agent determine it's needed
  • Best practice: If files are large (>10k words), include grep search patterns in SKILL.md
  • Avoid duplication: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.

Assets (assets/)

Files not intended to be loaded into context, but rather used within the output agent produce.

  • When to include: When the skill needs files that will be used in the final output
  • Examples: assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates, assets/frontend-template/ for HTML/React boilerplate, assets/font.ttf for typography
  • Use cases: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
  • Benefits: Separates output resources from documentation, enables agent to use files without loading them into context

What to Not Include in a Skill

A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:

  • README.md
  • INSTALLATION_GUIDE.md
  • QUICK_REFERENCE.md
  • CHANGELOG.md
  • etc.

The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.

Progressive Disclosure Design Principle

Skills use a three-level loading system to manage context efficiently:

  1. Metadata (name + description) - Always in context (~100 words)
  2. SKILL.md body - When skill triggers (<5k words)
  3. Bundled resources - As needed by agent (Unlimited because scripts can be executed without reading into context window)

Progressive Disclosure Patterns

Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.

Key principle: When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.

Pattern 1: High-level guide with references

# PDF Processing ## Quick start Extract text with pdfplumber: [code example] ## Advanced features - **Form filling**: See [FORMS.md](FORMS.md) for complete guide - **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods - **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns

Agent load FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.

Pattern 2: Domain-specific organization

For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:

bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
    ├── finance.md (revenue, billing metrics)
    ├── sales.md (opportunities, pipeline)
    ├── product.md (API usage, features)
    └── marketing.md (campaigns, attribution)

When a user asks about sales metrics, agent only read sales.md.

Similarly, for skills supporting multiple frameworks or variants, organize by variant:

cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
    ├── aws.md (AWS deployment patterns)
    ├── gcp.md (GCP deployment patterns)
    └── azure.md (Azure deployment patterns)

When the user chooses AWS, agent only read aws.md.

Pattern 3: Conditional details

Show basic content, link to advanced content:

# DOCX Processing ## Creating documents Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md). ## Editing documents For simple edits, modify the XML directly. **For tracked changes**: See [REDLINING.md](REDLINING.md) **For OOXML details**: See [OOXML.md](OOXML.md)

Agent read REDLINING.md or OOXML.md only when the user needs those features.

Important guidelines:

  • Avoid deeply nested references - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
  • Structure longer reference files - For files longer than 100 lines, include a table of contents at the top so agent can see the full scope when previewing.

Skill Creation Process

Skill creation involves these steps:

  1. Understand the skill with concrete examples
  2. Plan reusable skill contents (scripts, references, assets)
  3. Initialize the skill (run init_skill.py)
  4. Edit the skill (implement resources and write SKILL.md)
  5. Package the skill (run package_skill.py)
  6. Iterate based on real usage

Follow these steps in order, skipping only if there is a clear reason why they are not applicable.

Step 1: Understanding the Skill with Concrete Examples

Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.

To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.

For example, when building an image-editor skill, relevant

五维分析
清晰度8/10
创新性7/10
实用性9/10
完整性8/10
可维护性9/10
优缺点分析

优点

  • 提供结构化的技能创建方法
  • 鼓励模块化设计以提高可维护性
  • 提供清晰的文档和资源指南

缺点

  • 可能需要技术专长才能有效实施
  • 对于没有先前知识的初学者可能会感到压倒
  • 有限的示例可能会阻碍某些用户的理解

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免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。

版权归原作者所有 observerw.