Skill Creator

---
name: skill-creator
description: >
  Create or update AgentSkills. Use when:
  - User asks, in any language, to create or teach Jenny a new skill ("create a skill", "I want a new skill")
  - User wants to design, structure, or package skills with scripts, references, and assets
  - User asks for help with skill creation or improvement
  Do NOT use for editing existing skill content directly — guide the user through the conversation flow first.
locked: true
---

# Skill Creator

This skill provides guidance for creating effective skills.

<rule>
**When a user wants to create a new skill, ALWAYS follow the Guided Conversation Flow below.**
Do NOT create skills directly. Do NOT skip the conversation phases.
Only proceed to file creation AFTER the user has confirmed name and description in Phase 3.

**This conversation is not a sustained goal: do not call `long_task` for it.** Every phase
ends by asking the user something and waiting, which is the one thing a goal cannot do for
you. Ask the question of the current phase and end the turn.
</rule>

## Where a Skill's Code Lives, and How It Runs

<rule>
**A skill's helper code lives inside the skill folder, at `skills/<name>/scripts/<mod>.py`.
NEVER write a helper module to the workspace root.** Nothing sweeps the workspace root — the
danger is the opposite one. A module dropped there has no owner: it sits among the bootstrap
documents looking like leftover debris, nothing records which skill depends on it, and the
only way to find out whether it still matters is to open it and guess. Under
`skills/<name>/scripts/` the folder itself is the answer.

**`python_exec` is the only execution tool on this platform.** There is no shell: never write
`python3 …`, `bash …`, or any other command line into a skill.

**Every `python_exec` call that touches a skill's own scripts MUST pass
`working_dir="<workspace>/skills/<name>/scripts"`**, and then `import <mod>` plainly.
`working_dir` puts that directory at the head of `sys.path` and makes relative paths resolve
against it. Without it a bare `import <mod>` raises `ModuleNotFoundError` and a relative
`open("scripts/x.py")` reads from the workspace root instead. Every path *outside* the scripts
directory (a data file, a target folder) must then be absolute, because relative ones are now
measured from `working_dir`.

**Every SKILL.md you generate must carry `working_dir` in every `python_exec` example it
emits.** Do not replace it with prose ("run from the workspace root"): the next agent copies
the block verbatim, and a block without `working_dir` does not run. This has already happened.
</rule>

Copy-pasteable shape:

```
python_exec(
    working_dir="<workspace>/skills/my-skill/scripts",
    code="import my_helper; print(my_helper.run('<workspace>/data/input.json'))",
)
```

## Guided Conversation Flow

When a user initiates skill creation with a generic request (e.g., "I want to create a new skill"), follow this structured flow. Ask ONE question at a time. Do NOT overwhelm the user. The quoted lines show what to ask, not the words to use: ask in the user's language.

### Phase 1: Understand Purpose

**User says:** "I want to create a new skill" (or similar)

**Agent responds:**
> Sure! Tell me: what should this skill do? Give me a short description of its main purpose.

Wait for the user's answer. If the answer is vague, ask ONE clarifying question:
> Ok, so [summary]. Can you give me a concrete example of how you would use it?

### Phase 2: Understand Triggers

Once the purpose is clear, ask about when the skill should activate:
> Great! Now tell me: when should this skill kick in? What would you ask or say to use it?

If the user gives a broad answer, suggest specific triggers:
> So I'd say it kicks in when you say [example1], [example2], or ask [example3]. Right?

### Phase 3: Propose Name and Description

Based on the conversation, propose a skill name and description:
> Got it! Here is my proposal:
> - **Name:** `skill-name` (lowercase, with hyphens)
> - **Description:** [description that includes what it does + when to use it]
>
> Does that work, or would you like to change something?

**Name rules:**
- Lowercase alphanumeric, single hyphens as separators
- Max 64 characters
- Prefer short, verb-led phrases (e.g., `pdf-editor`, `api-client`)

**Description rules:**
- Include WHAT the skill does AND WHEN to use it
- Be concrete about triggers
- Max 1024 characters

### Phase 4: Create the Skill

Only after the user confirms the name and description:

1. Run `init_skill.py` to scaffold:
   ```
   python_exec(
       working_dir="<workspace>/skills/skill-creator/scripts",
       code="import init_skill; init_skill.init_skill('skill-name', '<workspace>/skills', [], False)",
   )
   ```

2. Read the created SKILL.md and show the body to the user:
   > I've created the skill! Now let's write the agent's instructions together. Here is the structure:
   >
   > [show the body template]
   >
   > Shall I guide you through filling it in, or would you rather write it yourself?

### Phase 5: Guide Body Writing

If the user wants guidance, ask ONE section at a time:
> Let's start with the "What I do" section. What are the 3-5 main things this skill should do?

After each section, move to the next:
> Now the "When to use me" section. Which situations should trigger this skill?

### Phase 6: Validate and Test

After the body is complete:
> Great! The skill is ready. Shall I validate it with `quick_validate.py`?

```
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import quick_validate; print(quick_validate.validate_skill('<workspace>/skills/skill-name'))",
)
```

If validation passes:
> The skill is valid! To test it, restart Jenny and try saying [trigger example].

If validation fails:
> I found a few problems: [list of errors]. Shall I fix them?

### Handling Edge Cases

- **User changes their mind mid-flow:** Acknowledge and adapt. "Ok, let's change direction. What would you like instead?"
- **User wants to skip questions:** Allow it. "Ok, I'll go with what I have. Shall I create the skill with this?"
- **User provides too much info:** Summarize and confirm. "So, to sum up: [short summary]. Right?"
- **User is unsure:** Offer examples. "I can suggest: [example1], [example2], [example3]. Does any of these work for you?"

## About Skills

Skills are modular, self-contained packages that extend the agent's capabilities by providing
specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
domains or tasks—they transform the agent 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 the agent needs: system prompt, conversation history, other Skills' metadata, and the actual user request.

**Default assumption: the agent is already very smart.** Only add context the agent doesn't already have. Challenge each piece of information: "Does the agent 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 the agent 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)
    ├── 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. These are the only fields that the agent 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 (`scripts/`)

Executable code (Python) 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 the agent for patching or environment-specific adjustments

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

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

- **When to include**: For documentation that the agent 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 the agent determines it's needed
- **Best practice**: If files are large (>10k words), include grep patterns in SKILL.md so the agent can use built-in search tools efficiently; mention when the default `grep(output_mode="files_with_matches")`, `grep(output_mode="count")`, `grep(fixed_strings=true)`, or pagination via `head_limit` / `offset` is the right first step
- **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 the agent 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 the agent 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 auxiliary 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 (<5k words)
3. **Bundled resources** - As needed by the agent (Unlimited because scripts can be executed without reading into context window)

#### 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
```

the agent 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, the agent 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)
```

When the user chooses AWS, the agent 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)
```

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

**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 the agent can see the full scope when previewing.

## Skill Creation Process

Skill creation involves these steps:

1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, assets)
3. Initialize the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. Package the skill (run package_skill.py)
6. Iterate based on real usage

Follow these steps in order, skipping only if there is a clear reason why they are not applicable.

### Skill Naming

- Use lowercase letters, digits, and hyphens only; normalize user-provided titles to hyphen-case (e.g., "Plan Mode" -> `plan-mode`).
- When generating names, generate a name under 64 characters (letters, digits, hyphens).
- Prefer short, verb-led phrases that describe the action.
- Namespace by tool when it improves clarity or triggering (e.g., `gh-address-comments`, `linear-address-issue`).
- Name the skill folder exactly after the skill name.

### 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.

When creating a new skill from scratch, always run the `init_skill.py` script via `python_exec`. 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.

For `Jenny`, custom skills should live under the active workspace `skills/` directory so they can be discovered automatically at runtime (for example, `<workspace>/skills/my-skill/SKILL.md`).

The script has no command line — it exposes
`init_skill(skill_name, path, resources, include_examples)`. Import it and call it:

```
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import init_skill; init_skill.init_skill('my-skill', '<workspace>/skills', [], False)",
)
```

`path` must be **absolute** — the script does `Path(path).resolve()`, which does not measure
from the workspace. `resources` is a list drawn from `scripts`, `references`, `assets`;
`include_examples` adds placeholder files inside them.

Examples:

```
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import init_skill; init_skill.init_skill('my-skill', '<workspace>/skills', ['scripts', 'references'], False)",
)
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import init_skill; init_skill.init_skill('my-skill', '<workspace>/skills', ['scripts'], True)",
)
```

The script:

- Creates the skill directory at the specified path
- Generates a SKILL.md template with proper frontmatter and TODO placeholders
- Optionally creates resource directories based on `--resources`
- Optionally adds example files when `--examples` is set

After initialization, customize the SKILL.md and add resources as needed. If you used `--examples`, replace or delete placeholder files.

### Step 4: Edit the Skill

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

#### 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.

If you used `--examples`, delete any placeholder files that are not needed for the skill. Only create resource directories that are actually required.

#### 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, and helps the agent understand when to use the skill.
  - Include both what the Skill does and specific triggers/contexts for when to use it.
  - Include all "when to use" information 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 the agent.
  - Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when the agent needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"

Keep frontmatter minimal. In `Jenny`, `metadata` and `always` are also supported when needed, but avoid adding extra fields unless they are actually required.

##### Body

Write instructions for using the skill and its bundled resources.

### Step 5: Packaging a Skill

Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with user. The packaging process automatically validates the skill first to ensure it meets all requirements:

```
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import package_skill; package_skill.package_skill('<workspace>/skills/my-skill')",
)
```

Optional output directory specification (second argument, absolute):

```
python_exec(
    working_dir="<workspace>/skills/skill-creator/scripts",
    code="import package_skill; package_skill.package_skill('<workspace>/skills/my-skill', '<workspace>/dist')",
)
```

The packaging script will:

1. **Validate** the skill automatically, checking:
   - YAML frontmatter format and required fields
   - Skill naming conventions and directory structure
   - Description completeness and quality
   - File organization and resource references

2. **Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.

   Security restriction: symlinks are rejected and packaging fails when any symlink is present.

If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command 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