packmind-create-skill

name	packmind-create-skill
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 GitLab Duo's capabilities with specialized knowledge, workflows, or tool integrations.
license	Complete terms in LICENSE.txt
metadata	{"packmind-cli-version":"< 0.25.0"}

Create skill

This skill provides guidance for creating effective skills.

About Skills

Skills are modular, self-contained packages that extend GitLab Duo's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform GitLab Duo from a general-purpose agent into a specialized agent 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

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)

Metadata Quality: The name and description in YAML frontmatter determine when GitLab Duo will use the skill. Be specific about what the skill does and when to use it. Use the third-person (e.g. "This skill should be used when..." instead of "Use this skill when...").

Bundled Resources (optional)

Scripts (scripts/)

Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.

• When to include: When the same code is being rewritten repeatedly or deterministic reliability is needed
• Example: scripts/rotate_pdf.py for PDF rotation tasks
• Benefits: Token efficient, deterministic, may be executed without loading into context
• Note: Scripts may still need to be read by GitLab Duo for patching or environment-specific adjustments

References (references/)

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

• When to include: For documentation that GitLab Duo 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 GitLab Duo determines 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 GitLab Duo produces.

• 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 GitLab Duo to use files without loading them into context

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 GitLab Duo (Unlimited*)

*Unlimited because scripts can be executed without reading into context window.

Prerequisites

Before running any skill scripts, verify that the required tools are available:

Python 3

Check if Python 3 is installed:

python3 --version

If not available, install it:

• macOS: brew install python3
• Ubuntu/Debian: sudo apt-get install python3
• Windows: Download from https://python.org or use winget install Python.Python.3

Packmind CLI

Check if packmind-cli is installed:

packmind-cli --version

If not available, install it:

npm install -g packmind-cli

Skill Creation Process

To create a skill, follow the "Skill Creation Process" in order, skipping steps 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 questions include:

• "What functionality should the image-editor skill support? Editing, rotating, anything else?"
• "Can you give some examples of how this skill would be used?"
• "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
• "What would a user say that should trigger this skill?"

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.

Step 2: Planning the Reusable Skill Contents

To turn concrete examples into an effective skill, analyze each example by:

1. Considering how to execute on the example from scratch
2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly

Example: When building a pdf-editor skill to handle queries like "Help me rotate this PDF," the analysis shows:

1. Rotating a PDF requires re-writing the same code each time
2. A scripts/rotate_pdf.py script would be helpful to store in the skill

Example: When designing a frontend-webapp-builder skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:

1. Writing a frontend webapp requires the same boilerplate HTML/React each time
2. An assets/hello-world/ template containing the boilerplate HTML/React project files would be helpful to store in the skill

Example: When building a big-query skill to handle queries like "How many users have logged in today?" the analysis shows:

1. Querying BigQuery requires re-discovering the table schemas and relationships each time
2. A references/schema.md file documenting the table schemas would be helpful to store in the skill

To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.

Step 3: Initializing the Skill

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.

Before running the script, verify that python3 and packmind-cli are available (see Prerequisites section). If not installed, install them first.

When creating a new skill from scratch, always run the init_skill.py script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.

Usage:

python3 scripts/init_skill.py <skill-name> --path <output-directory>

IMPORTANT: The --path argument must be the parent directory where the skill folder will be created, not the skill directory itself. The script automatically creates a subdirectory named after the skill.

• ✅ Correct: python3 scripts/init_skill.py my-skill --path .claude/skills → creates .claude/skills/my-skill/
• ❌ Wrong: python3 scripts/init_skill.py my-skill --path .claude/skills/my-skill → creates .claude/skills/my-skill/my-skill/ (nested!)

The script:

• Creates the skill directory at the specified path
• Generates a SKILL.md template with proper frontmatter and TODO placeholders
• Creates example resource directories: scripts/, references/, and assets/
• Adds example files in each directory that can be customized or deleted

After initialization, customize or remove the generated SKILL.md and example files as needed.

Step 4: Edit the Skill

When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of GitLab Duo to use. Focus on including information that would be beneficial and non-obvious to GitLab Duo. Consider what procedural knowledge, domain-specific details, or reusable assets would help another GitLab Duo instance execute these tasks more effectively.

File Placement Rule: Always create files directly in their target subdirectory. Never create a file at the skill root and move it later.

• Reference docs → references/filename.md
• Scripts → scripts/filename.py
• Assets → assets/filename.ext
• Only SKILL.md, README.md, and LICENSE.txt belong at the skill root.

Start with Reusable Skill Contents

To begin implementation, start with the reusable resources identified above: scripts/, references/, and assets/ files. Note that this step may require user input. For example, when implementing a brand-guidelines skill, the user may need to provide brand assets or templates to store in assets/, or documentation to store in references/.

Also, delete any example files and directories not needed for the skill. The initialization script creates example files in scripts/, references/, and assets/ to demonstrate structure, but most skills won't need all of them.

Update SKILL.md

Writing Style: Write the entire skill using imperative/infinitive form (verb-first instructions), not second person. Use objective, instructional language (e.g., "To accomplish X, do Y" rather than "You should do X" or "If you need to do X"). This maintains consistency and clarity for AI consumption.

To complete SKILL.md, answer the following questions:

1. What is the purpose of the skill, in a few sentences?
2. When should the skill be used?
3. In practice, how should GitLab Duo use the skill? All reusable skill contents developed above should be referenced so that GitLab Duo knows how to use them.

Step 5: Validating a Skill

Before distributing, validate the skill to ensure it meets all requirements.

Before running the script, verify that python3 is available (see Prerequisites section). If not installed, install it first.

python3 scripts/quick_validate.py <path/to/skill-folder>

The validation script checks:

• YAML frontmatter format and required fields
• Skill naming conventions and directory structure
• Description completeness and quality

If validation fails, fix the reported errors and run the validation again.

Step 6: Iterate

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:

1. Use the skill on real tasks
2. Notice struggles or inefficiencies
3. Identify how SKILL.md or bundled resources should be updated
4. Implement changes and test again

Step 7: Distributing a Skill

After successful validation, always run the distribution command to register the skill with Packmind. Do not skip this step.

Before running the command, verify that packmind-cli is available (see Prerequisites section). If not installed, install it first.

Run the following command with the actual skill path:

packmind-cli skills add <path/to/skill-folder>

This registers the skill with Packmind, making it available for deployment to target repositories and AI coding agents.

Step 8: Offer to Add to Package

After successful distribution, check if the skill fits an existing package:

1. Run packmind-cli install --list to get available packages
2. If no packages exist, skip this step silently and end the workflow
3. Analyze the created skill's name and description against each package's name and description
4. If a package is a clear semantic fit (the skill's domain/technology aligns with the package's purpose):
   • Present to user: "This skill seems to fit the <package-slug> package."
   • Offer three options:
     • Add to <package-slug>
     • Choose a different package
     • Skip
5. If no clear fit is found, skip silently (do not mention packages)
6. If user chooses to add:
   • Run: packmind-cli packages add --to <package-slug> --skill <skill-slug>
   • Ask: "Would you like me to run packmind-cli install to sync the changes?"
   • If yes, run: packmind-cli install