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 the agent's capabilities with specialized knowledge, workflows, or tool integrations.
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This skill provides guidance for creating effective skills.
Skills are modular, self-contained packages that equip the agent — an LLM-powered system that can be extended — with specialized capabilities. Each skill provides domain-specific knowledge, optimized workflows, and purpose-built tools, effectively transforming a general-purpose agent into a domain specialist.
Unlike static model training, skills operate as plug-and-play components. They inject precise procedural knowledge that large language models inherently lack, enabling reliable execution of complex tasks. This modular approach allows skills to be combined, updated, or replaced independently—accelerating adaptation to new domains without retraining the core agent.
The context window is a public good. Skills share the context window with everything else the agent needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
Default assumption: The agent is already very smart. Only add context the agent doesn't already have. Challenge each piece of information: "Does the agent really need this explanation?" and "Does this paragraph justify its token cost?"
Prefer concise examples over verbose explanations.
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 it as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
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.)
Every SKILL.md consists of:
namedescriptionscripts/Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
scripts/rotate_pdf.pyreferences/Documentation and reference material intended to be loaded as needed into context to inform the agent's process and thinking.
references/finance.mdreferences/mnda.mdreferences/policies.mdreferences/api_docs.mdassets/Files not intended to be loaded into context, but rather used within the output that the agent produces.
assets/logo.pngassets/slides.pptxassets/frontend-template/assets/font.ttfA skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
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.
Skills use a three-level loading system to manage context efficiently:
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 [references/FORMS.md](FORMS.md) for complete guide - **API reference**: See [references/REFERENCE.md](references/REFERENCE.md) for all methods - **Examples**: See [references/EXAMPLES.md](references/EXAMPLES.md) for common patterns
The agent loads 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, the agent only reads 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, the agent only reads aws.md.
Pattern 3: Conditional details
Show basic content, link to advanced content:
# DOCX Processing ## Creating documents Use docx-js for new documents. See [references/DOCX-JS.md](references/DOCX-JS.md). ## Editing documents For simple edits, modify the XML directly. **For tracked changes**: See [references/REDLINING.md](references/REDLINING.md) **For OOXML details**: See [references/OOXML.md](references/OOXML.md)
The agent reads REDLINING.md or OOXML.md only when the user needs those features.
Important guidelines:
Skill creation involves these steps:
scripts/init_skill.pyscripts/package_skill.pyFollow these steps in order, skipping only if there is a clear reason why they are not applicable.
uvAction Required:
uvuv --version
Provide this command to the user:
curl -LsSf https://astral.sh/uv/install.sh | sh
Verification: After user installation, confirm success with:
uv --version
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 questions include:
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
Conclude this step when there is a clear sense of the functionality the skill should support.
To turn concrete examples into an effective skill, analyze each example by:
Example: When building a
pdf-editorscripts/rotate_pdf.pyExample: When designing a
frontend-webapp-builderassets/hello-world/Example: When building a
big-queryreferences/schema.mdTo establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the
scripts/init_skill.pyUsage:
uv run scripts/init_skill.py <skill-name> --path <output-directory>
The script:
scripts/references/assets/After initialization, customize or remove the generated SKILL.md and example files as needed.
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of the agent to use. Include information that would be beneficial and non-obvious to the agent. Consider what procedural knowledge, domain-specific details, or reusable assets would help another agent instance execute these tasks more effectively.
Consult these helpful guides based on your skill's needs:
references/workflows.mdreferences/output-patterns.mdThese files contain established best practices for effective skill design.
To begin implementation, start with the reusable resources identified above:
scripts/references/assets/brand-guidelinesassets/references/Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in
scripts/references/assets/Writing Guidelines: Always use imperative/infinitive form.
Write the YAML frontmatter with
namedescriptionnamedescriptiondocxDo not include any other fields in YAML frontmatter.
Write instructions for using the skill and its bundled resources.
Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
uv run scripts/package_skill.py <path/to/skill-folder>
Optional output directory specification:
uv run scripts/package_skill.py <path/to/skill-folder> ./dist
The packaging script will:
Validate the skill automatically, checking:
Package the skill if validation passes, creating a .skill file named after the skill (e.g.,
my-skill.skillIf validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
Iteration workflow: