---
name: bluelovers/skill-creator
source: https://app.decimal.ai/s/bluelovers-skill-creator@1/SKILL.md
source_sha256: 8563ac21239a
---

# Skill Creator

## Core Workflow

Creating an effective skill follows this loop:

- Decide what you want the skill to do and roughly how it should do it
- Write a draft of the SKILL.md and any bundled resources
- Create a few realistic test prompts and run them with the skill
- Evaluate the results with the user, identify struggles or inefficiencies
- Rewrite based on feedback
- Repeat until satisfied, then package and share

Wherever possible, extract answers from the current conversation first (e.g., "turn this into a skill"). The user may need to fill gaps — confirm before proceeding.

## Communicating with the user

The skill creator may be used by people with varying familiarity with technical terminology. Pay attention to context cues:

- "evaluation" and "benchmark" are borderline but acceptable
- For "JSON" and "assertion", gauge the user's familiarity before using them without explanation
- It's OK to briefly explain terms or ask for clarification when unsure

---

## About Skills

Skills are modular, self-contained packages that extend Claude's capabilities by providing specialized knowledge, workflows, and tools—"onboarding guides" for specific domains or tasks.

### 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 Claude needs: system prompt, conversation history, other Skills' metadata, and the actual user request.

**Default assumption: Claude is already very smart.** Only add context Claude doesn't already have. Challenge each piece of information: "Does Claude 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 Claude as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).

### Capture Intent

Before drafting, confirm these four points with the user:

1. What should this skill enable Claude to do?
2. When should it trigger? (what user phrases/contexts)
3. What's the expected output format?
4. Should test cases verify the skill's behavior?

Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, creative work) often don't need them — suggest the appropriate default based on the skill type, but let the user decide.

### 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)
│   │   └── compatibility: (optional, rarely needed)
│   └── 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)

Every SKILL.md consists of:

- **Frontmatter** (YAML): Contains `name` and `description` fields (required), plus optional fields like `license`, `metadata`, and `compatibility`. Only `name` and `description` are read by Claude to determine when the skill triggers, so be clear and comprehensive about what the skill is and when it should be used. The `compatibility` field is for noting environment requirements (target product, system packages, etc.) but most skills don't need it.
- **Body** (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers (if at all).

#### 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 Claude for patching or environment-specific adjustments

##### References (`references/`)

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

- **When to include**: For documentation that Claude 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 Claude 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 Claude 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 Claude to use files without loading them into context

#### What to Not Include in a Skill

A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:

- README.md
- INSTALLATION_GUIDE.md
- QUICK_REFERENCE.md
- CHANGELOG.md
- etc.

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.

### 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 (<500 lines ideal)
3. **Bundled resources** - As needed by Claude (unlimited — scripts can execute without loading)

For reference files longer than 300 lines, include a table of contents at the top so Claude can see the full scope when previewing.

#### Progressive Disclosure Patterns

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**

```markdown
# PDF Processing

## Quick start

Extract text with pdfplumber:
[code example]

## Advanced features

- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
```

Claude 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, Claude 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, Claude only reads aws.md.

**Pattern 3: Conditional details**

Show basic content, link to advanced content:

```markdown
# DOCX Processing

## Creating documents

Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).

## Editing documents

For simple edits, modify the XML directly.

**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)
```

Claude reads REDLINING.md or OOXML.md only when the user needs those features.

**Examples pattern**: Structure documentation with clear Input/Output pairs so Claude can recognize the desired format:

```markdown
## Commit message format

**Example:**
Input: Added user authentication with JWT tokens
Output: feat(auth): implement JWT-based authentication
```

**Important guidelines:**

- **Avoid deeply nested references** - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
- **Structure longer reference files** - For files longer than 100 lines, include a table of contents at the top so Claude can see the full scope when previewing.

## Skill Creation Process

Skill creation involves these steps:

1. Capture intent (4 questions above) and interview the user for edge cases, input/output formats, and success criteria
2. Plan reusable skill contents (scripts, references, assets)
3. Edit the skill — implement resources, write SKILL.md, and write the frontmatter description in a "pushy" way
4. Package the skill (run `scripts/package_skill.py <path/to/skill-folder>`)
5. Iterate based on real usage and user feedback

Follow these steps in order, skipping only if there is a clear reason why they are not applicable. Most work happens in steps 3–5.

### 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. Follow up with questions incrementally — avoid overwhelming the user.

Proactively ask about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.

**Interviewing questions to consider:**
- "What should the output look like in this scenario?"
- "Are there edge cases or inputs that should be handled differently?"
- "What does success look like for this skill?"

### 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.

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:

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

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 Claude to use. Include information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.

#### Learn Proven Design Patterns

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

- **Multi-step processes**: See references/workflows.md for sequential workflows and conditional logic
- **Specific output formats or quality standards**: See references/output-patterns.md for template and example patterns

These files contain established best practices for effective skill design.

#### 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/`.

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/`, and `assets/` to demonstrate structure, but most skills won't need all of them.

#### Update SKILL.md

**Writing Guidelines:** Always use imperative/infinitive form.

##### Frontmatter

Write the YAML frontmatter with `name` and `description`:

- `name`: The skill name
- `description`: This is the primary triggering mechanism for your skill.
  - Include **what the skill does** AND **specific contexts for when to use it**.
  - All "when to use" information goes here, not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to Claude.
  - **Note**: Claude tends to "undertrigger" skills — failing to use them when useful. To combat this, make the description a little "pushy": include explicit trigger phrases and contexts so the skill fires even when the user doesn't name it directly.
  - **Good example**: "How to build a dashboard for internal metrics. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'"
  - **Bad example**: "Helps with dashboards." (too vague to trigger reliably)

Do not include any other fields in YAML frontmatter.

##### Body

Write instructions for using the skill and its bundled resources.

### Step 5: Packaging a Skill

Once the skill is ready to share, package it into a distributable `.skill` file. See `references/packaging.md` for the full packaging workflow, validation steps, and how to handle updates when the skill is already installed.

### 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

**Optional: Test case tracking.** For skills where you want a record of test prompts, create an `evals/evals.json` file alongside the skill:

```json
{
  "skill_name": "my-skill",
  "evals": [
    {
      "id": 1,
      "prompt": "What the user would say",
      "expected_output": "Description of expected result",
      "files": []
    }
  ]
}
```

This is optional — use it when the skill is mature enough to justify a structured test record. It does not replace running the skill with real prompts.

**How to think about improvements:**

1. **Generalize from the feedback.** Ask: "Would this change help on other inputs, or does it only fix this one case?" Avoid overfitting to a specific example.
2. **Keep the prompt lean.** Remove instructions that aren't pulling their weight. Watch the transcripts — if the skill makes the model waste time on unproductive steps, trim those parts.
3. **Explain the why.** When telling the model to do something, explain the reasoning rather than relying on ALWAYS / NEVER in all caps. LLMs respond better to understanding the rationale.
4. **Look for repeated work.** If multiple test cases inspire the same script or pattern, it's a signal the skill should bundle it. Write it once, put it in `scripts/`, and reference it from `SKILL.md`.