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Agent

Skill kstonekuan/webskills/.agents/skills/agent

WebSkills turns any webpage into an agent skill - npx webskills

Install
npx -y skills add kstonekuan/webskills --skill agent

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Use when creating, validating, or integrating reusable skill packages for AI agents. Reach for this skill when building domain-specific instructions, packaging workflows, or setting up skill discovery in agent systems.

SKILL.md

8.8 KB, as published. Nobody here has run it

Agent Skills

Product summary

Agent Skills is a lightweight, open format for extending AI agent capabilities with specialized knowledge and workflows. Skills are directories containing a SKILL.md file (YAML frontmatter + Markdown instructions) plus optional supporting files (scripts, references, assets). Use the skills-ref CLI to validate skills and generate prompt XML for agent integration. Primary documentation: https://agentskills.io

When to use

Reach for this skill when:

  • Creating a new skill: You need to package domain expertise, repeatable workflows, or new agent capabilities into a reusable format
  • Validating skills: You're checking that a skill's SKILL.md file meets format requirements before deployment
  • Integrating skills into agents: You're setting up skill discovery, loading metadata, or injecting skills into agent system prompts
  • Authoring instructions: You're writing the Markdown body of a skill to guide agent behavior
  • Managing skill resources: You're organizing scripts, templates, reference files, or assets alongside skill instructions

Quick reference

SKILL.md frontmatter (required fields)

FieldConstraintsExample
nameMax 64 chars, lowercase + hyphens only, no leading/trailing hyphenspdf-processing
descriptionMax 1024 chars, non-empty, describes what skill does and when to useExtract text and tables from PDFs, fill forms, merge documents.

SKILL.md optional frontmatter fields

FieldPurposeExample
licenseLicense name or reference to bundled license fileMIT or licenses/LICENSE.txt
compatibilityEnvironment requirements (max 500 chars)Requires Python 3.8+, pdfplumber library
metadataArbitrary key-value pairs for custom propertiesauthor: org-name version: "1.0"
allowed-toolsSpace-delimited pre-approved tools (experimental)Bash(git:*) Bash(jq:*)

Directory structure

skill-name/
├── SKILL.md              # Required: frontmatter + instructions
├── scripts/              # Optional: executable code
├── references/           # Optional: detailed reference material
└── assets/               # Optional: templates, images, data files

skills-ref CLI commands

# Validate a skill directory
skills-ref validate ./my-skill

# Generate <available_skills> XML for agent prompts
skills-ref to-prompt /path/to/skill1 /path/to/skill2

File references in SKILL.md

Use relative paths from skill root:

See [the guide](references/REFERENCE.md) for details.
Run the script: `scripts/extract.py`

Decision guidance

When to use filesystem-based vs tool-based agent integration

ApproachWhen to useActivationResource access
Filesystem-basedAgent has shell/bash environment; maximum capability neededAgent issues cat /path/to/skill/SKILL.mdShell commands access bundled files
Tool-basedAgent lacks dedicated computer environment; sandboxed executionDeveloper implements custom toolsTools provide access to bundled assets

When to include optional frontmatter fields

FieldInclude whenSkip if
licenseSkill uses third-party code or has specific licensingSkill is internal-only or unlicensed
compatibilitySkill requires specific system packages, Python versions, or network accessSkill is pure Markdown instructions
metadataTracking author, version, or custom properties is importantNot needed for simple skills
allowed-toolsRestricting which tools the skill can invoke is requiredAgent doesn't support tool restrictions

When to move content to separate files

Content typeKeep in SKILL.mdMove to separate file
Core instructions< 500 linesDetailed reference material
Quick examplesYesExhaustive API documentation
Decision treesYesLarge lookup tables or schemas
Step-by-step workflowsYesMulti-page tutorials

Workflow

Creating a new skill

  1. Create the directory structure: Make a folder named skill-name (lowercase, hyphens only)

  2. Write the SKILL.md file:

    • Start with YAML frontmatter: name and description (required)
    • Write the Markdown body with clear instructions
    • Keep the main file under 500 lines
    • Use relative paths for file references: scripts/extract.py, references/GUIDE.md
  3. Organize supporting files:

    • scripts/: Executable code (Python, Bash, etc.)
    • references/: Detailed documentation, lookup tables, schemas
    • assets/: Templates, images, data files
  4. Validate the skill:

    skills-ref validate ./skill-name
    

    Fix any frontmatter errors (name format, description length, etc.)

  5. Test the skill: Read the SKILL.md file to verify instructions are clear and complete

Integrating skills into an agent

  1. Discover skills: Scan configured directories for folders containing SKILL.md files

  2. Load metadata at startup: Parse only the YAML frontmatter from each skill:

    name: pdf-processing
    description: Extract text and tables from PDFs...
    

    This keeps initial context usage low (~100 tokens per skill)

  3. Generate prompt XML:

    skills-ref to-prompt /path/to/skills/*
    

    Output includes name, description, and location for each skill

  4. Inject into system prompt: Add the XML to your agent's system prompt so it knows available skills:

    <available_skills>
      <skill>
        <name>pdf-processing</name>
        <description>Extract text and tables from PDFs...</description>
        <location>/path/to/skills/pdf-processing/SKILL.md</location>
      </skill>
    </available_skills>
    
  5. Activate on demand: When a user task matches a skill's description, load the full SKILL.md into context (~5000 tokens max)

  6. Execute resources: Load referenced files or run scripts only when the skill instructions require them

Common gotchas

  • Name format violations: Names must be lowercase with hyphens only, no leading/trailing hyphens. my-skill ✓, MySkill ✗, -my-skill
  • Description too long: Max 1024 characters. Descriptions longer than this will fail validation. Be concise about what the skill does and when to use it.
  • Forgetting YAML delimiters: Frontmatter must start and end with --- on its own line. Missing delimiters break parsing.
  • Absolute paths in file references: Always use relative paths from skill root (scripts/extract.py), never absolute paths (/home/user/scripts/extract.py)
  • Bloated SKILL.md: Keeping the main file under 500 lines is a guideline, not a hard limit, but large files waste context. Move detailed reference material to references/ subdirectory.
  • Missing location field for filesystem agents: When injecting skills into filesystem-based agent prompts, include the absolute path to SKILL.md. Tool-based agents can omit this.
  • Not validating before deployment: Always run skills-ref validate before sharing or deploying a skill. Frontmatter errors are silent until validation.
  • Unclear "when to use" description: The description field is critical for agent matching. Write it as a clear trigger: "Use when the user needs to extract text from PDFs" not "PDF processing skill".

Verification checklist

Before submitting a skill:

  • SKILL.md frontmatter has name (max 64 chars, lowercase + hyphens) and description (max 1024 chars)
  • Frontmatter is wrapped in --- delimiters on separate lines
  • All file references use relative paths from skill root (e.g., scripts/extract.py)
  • Main SKILL.md is under 500 lines; detailed material moved to references/ or assets/
  • Skill passes validation: skills-ref validate ./skill-name
  • Instructions are clear and actionable (imperative voice, step-by-step)
  • Optional fields (license, compatibility, metadata) are accurate if included
  • Skill description clearly states when an agent should use it (not just what it does)

Resources


For additional documentation and navigation, see: https://agentskills.io/llms.txt

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.