mcpField
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skill-creator

skill-creator

github.com/webmaxruAgent workflows52471 installsUpdated 4w ago
Repository

About skill-creator

A procedural skill for creating and validating Agent Skills that follow the agentskills.io specification and progressive disclosure principles. It guides authors from metadata checks through directory structure, instruction writing, and final review.

Key features

  1. 01Validate skill metadata
  2. 02Set up skill directories
  3. 03Draft progressive instructions
  4. 04Identify scripts for fragile tasks
  5. 05Review paths and completeness

Use cases

  1. 01Create a skill that meets specification
  2. 02Structure long procedures with references
  3. 03Add deterministic scripts to a skill

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/webmaxru/web-ai-agent-skills --skill skill-creator

After installing, type/skill-creatorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
SKILL.template.md
references/
checklist.md
scripts/
validate-metadata.py

Details

From the project README

Skill Authoring Procedure

Follow these steps to generate a skill that adheres to the agentskills.io specification and progressive disclosure principles.

Step 1: Initialize and Validate Metadata

  1. Define a unique name: 1-64 characters, lowercase, numbers, and single hyphens only.
  2. Draft a description: Max 1,024 characters, written in the third person, including negative triggers.
  3. Execute Validation Script: Run the validation script to ensure compliance before proceeding: python scripts/validate-metadata.py --name "[name]" --description "[description]"
  4. If the script returns an error, self-correct the metadata based on the stderr output and re-run until successful.

Step 2: Structure the Directory

  1. Create the root directory using the validated name.
  2. Initialize the following subdirectories:
    • scripts/: For tiny CLI tools and deterministic logic.
    • references/: For flat (one-level deep) context like schemas or API docs.
    • assets/: For output templates, JSON schemas, or static files.
  3. Ensure no human-centric files (README.md, INSTALLATION.md) are created.

Step 3: Draft Core Logic (SKILL.md)

  1. Use the template in assets/SKILL.template.md as the starting point.
  2. Write all instructions in the third-person imperative (e.g., "Extract the text," "Run the build").
  3. Enforce Progressive Disclosure:
    • Keep the main logic under 500 lines.
    • If a procedure requires a large schema or complex rule set, move it to references/.
    • Command the agent to read the specific file only when needed: "Read references/api-spec.md to identify the correct endpoint."

Step 4: Identify and Bundle Scripts

  1. Identify "fragile" tasks (regex, complex parsing, or repetitive boilerplate).
  2. Outline a single-purpose script for the scripts/ directory.
  3. Ensure the script uses standard output (stdout/stderr) to communicate success or failure to the agent.

Step 5: Final Logic Validation

  1. Review the SKILL.md for "hallucination gaps" (points where the agent is forced to guess).
  2. Verify all file paths are relative and use forward slashes (/).
  3. Cross-reference the final output against references/checklist.md.

Error Handling

  • Metadata Failure: If scripts/validate-metadata.py fails, identify the specific error (e.g., "STYLE ERROR") and rewrite the field to remove first/second person pronouns.
  • Context Bloat: If the draft exceeds 500 lines, extract the largest procedural block and move it to a file in references/.

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