skill-creation

name	skill-creation
description	Guide for creating modular, reusable skills with proper structure and progressive disclosure. Use when formalizing workflows or building capability packages. Trigger phrases: "create a new skill", "write a skill", "build a skill for this workflow", "turn this into a skill", "upgrade this to a skill".

OpenClaw Integration: This skill is invoked by the Dojo Genesis plugin via /dojo run skill-creation. The agent receives project context automatically via the before_agent_start hook. Use dojo_get_context for full state, dojo_save_artifact to persist outputs, and dojo_update_state to record phase transitions and decisions.

Skill Creator

This skill provides guidance for creating effective skills.

About Skills

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

Core Principles

Concise is Key

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

Default assumption: Manus is already very smart. Only add context Manus doesn't already have. Challenge each piece of information: "Does Manus 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 Manus 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
    └── templates/        - 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 Manus reads 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/ - Executable code for repetitive or deterministic tasks (e.g., rotate_pdf.py). Token efficient, can run without loading into context.
• references/ - Documentation loaded as needed (schemas, API docs, policies). Keeps SKILL.md lean. For large files (>10k words), include grep patterns in SKILL.md.
• templates/ - Output assets not loaded into context (logos, fonts, boilerplate code).

Avoid duplication: Information lives in SKILL.md OR references, not both.

Do NOT include: README.md, CHANGELOG.md, or other auxiliary documentation. Skills are for AI agents, not users.

Progressive Disclosure

Three-level loading system:

1. Metadata - Always in context (~100 words)
2. SKILL.md body - When skill triggers (<500 lines)
3. Bundled resources - As needed

Keep SKILL.md under 500 lines. When splitting content to references, clearly describe when to read them.

Key principle: Keep core workflow in SKILL.md; move variant-specific details to reference files.

Example structure for multi-domain skills:

bigquery-skill/
├── SKILL.md (overview + navigation)
└── references/
    ├── finance.md
    ├── sales.md
    └── product.md

Manus only loads the relevant reference file when needed.

Skill Creation Process

Skill creation involves these steps:

1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, templates)
3. Initialize the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. Deliver the skill (send SKILL.md path via notify_user)
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.

Gather concrete examples of how the skill will be used. Ask questions like:

• "What functionality should this skill support?"
• "Can you give examples of how it would be used?"

Avoid asking too many questions at once. Conclude when you have a clear sense of the functionality.

Step 2: Planning the Reusable Skill Contents

For each example, identify reusable resources:

Resource Type	When to Use	Example
scripts/	Code rewritten repeatedly	rotate_pdf.py for PDF rotation
templates/	Same boilerplate each time	HTML/React starter for webapp builder
references/	Documentation needed repeatedly	Database schemas for BigQuery skill

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.

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:

python /home/ubuntu/skills/skill-creation/scripts/init_skill.py <skill-name>

The script:

• Creates the skill directory at /home/ubuntu/skills/<skill-name>/
• Generates a SKILL.md template with proper frontmatter and TODO placeholders
• Creates example resource directories: scripts/, references/, and templates/
• 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 Manus to use. Include information that would be beneficial and non-obvious to Manus. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Manus instance execute these tasks more effectively.

Learn Proven Design Patterns

Consult these helpful guides based on your skill's needs:

• Multi-step processes: See /home/ubuntu/skills/skill-creation/references/workflows.md for sequential workflows and conditional logic
• Output formats or quality standards: See /home/ubuntu/skills/skill-creation/references/output-patterns.md for template and example patterns
• Progressive Disclosure Patterns: See /home/ubuntu/skills/skill-creation/references/progressive-disclosure-patterns.md for splitting content across files.

These files contain established best practices for effective skill design.

Start with Reusable Skill Contents

Begin with the scripts/, references/, and templates/ files identified in Step 2. This may require user input (e.g., brand assets for templates/, documentation for references/).

Test added scripts by running them to ensure they work correctly. For many similar scripts, test a representative sample.

Delete any unused example files from initialization.

Update SKILL.md

Writing Guidelines: Always use imperative/infinitive form.

Frontmatter

Write the YAML frontmatter with name and description:

• name: The skill name
• description: Primary trigger mechanism. Must include what the skill does AND when to use it (body only loads after triggering).
  • Example: "Document creation and editing with tracked changes. Use for: creating .docx files, modifying content, working with tracked changes."

Body

Write instructions for using the skill and its bundled resources.

Step 5: Delivering the Skill

Once development of the skill is complete, validate and deliver it to the user.

Validate the Skill

Run the validation script to ensure the skill meets all requirements:

python /home/ubuntu/skills/skill-creation/scripts/quick_validate.py <skill-name>

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

Deliver to User

Use message tool to send the SKILL.md file as attachment:

/home/ubuntu/skills/{skill-name}/SKILL.md

The system will automatically:

1. Detect the path pattern /home/ubuntu/skills/*/SKILL.md
2. Package the skill directory into a .skill file
3. Send to frontend as a special card with options:
   • Add to My Skills
   • Download
   • Preview

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

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OpenClaw Tool Integration

When running inside the Dojo Genesis plugin:

1. Start by calling dojo_get_context to retrieve full project state, history, and artifacts
2. During the skill, follow the workflow steps documented above
3. Save outputs using dojo_save_artifact with the artifacts output directory
4. Update project state by calling dojo_update_state to record skill completion and any phase transitions