pi-skills

name

pi-skills

description

Create and manage pi skills with proper SKILL.md format, frontmatter, and organization. Use for skill creation, naming conventions, bundled resources (scripts/references/assets), progressive disclosure patterns, MCP-related skill documentation, and deciding when to use skills vs Pi MCP servers. Use proactively when user says "create a skill", "SKILL.md", "add a skill", "document this workflow", "add MCP server docs", or asks about skill structure.

Pi Skills

Create loadable knowledge modules that enhance agent capabilities.

A skill is a markdown file with optional bundled resources (scripts, references, assets) that provides specialized knowledge, workflows, or tool integrations. Skills are loaded on-demand when needed.

How Skills Work

Progressive Disclosure: Skills use a three-level loading system to manage context efficiently:

1. Metadata (name + description) - Always in context at startup (~100 words)
2. SKILL.md body - Loaded when skill triggers (<5k words / 500 lines)
3. Bundled resources - Loaded as needed by the agent (unlimited)

Runtime Flow (using grep_app as example):

1. Agent starts, scans ~/.pi/agent/skills/, sees grep_app/SKILL.md frontmatter
2. User says "search GitHub for useState examples"
3. Agent sees description, decides grep_app is relevant
4. Agent reads full SKILL.md to learn the CLI commands
5. Agent runs: bun ~/.pi/agent/skills/grep_app/grep_app.js searchGitHub --query "useState("
6. CLI executes, prints results to stdout
7. Agent reads results, responds to user

Skill Location & Structure

Path: ~/.pi/agent/skills/<skill-name>/

Directory Structure:

~/.pi/agent/skills/my-skill/
├── SKILL.md           # REQUIRED - Main skill definition
├── package.json       # Optional - npm dependencies for scripts
├── scripts/           # Optional - Executable scripts
│   └── validate.sh
├── references/        # Optional - Reference documents loaded on-demand
│   └── schema.md
└── assets/            # Optional - Files used in output (templates, data)
    └── template.json

Naming Rules:

• Lowercase kebab-case: my-skill (not My-Skill or my_skill)
• Folder name SHOULD match name in frontmatter for consistency

SKILL.md Format

Frontmatter (Required)

---
name: my-skill-name
description: [Capability summary]. Use for [use cases]. Use proactively when [trigger contexts].
---

Description Pattern (CRITICAL for discovery):

The description is the PRIMARY triggering mechanism. It MUST include:

1. Capability statement: What the skill does (1-2 sentences)
2. Use cases: Specific scenarios or tasks
3. Triggers: Keywords or phrases that should activate this skill

Example:

description: Search real-world code examples across a million GitHub repositories. Use when you need to find actual code patterns, library usage examples, or implementation patterns. Triggers include "search GitHub for", "find code examples of", "how do developers use", "show real-world usage of", or any need to see production code patterns.

Body (Required)

The body provides full instructions for using the skill. Keep it focused and under 500 lines.

Recommended Sections:

# Skill Title

Brief overview of what this skill enables.

## Setup

Any required environment variables, installation steps, or one-time configuration.

## Usage

Show the actual commands with examples. Use absolute paths from skill directory.

## Tools / Commands

List each tool/command with description and parameters.

## Patterns

Common workflows or usage patterns.

## Troubleshooting

Common issues and solutions.

Auth, Secrets, and Environment Variables

When a skill needs tokens, API keys, hostnames, or other auth-related configuration:

• put secrets in shell environment variables
• prefer ~/.zshrc.local for persistent local machine config
• prefer appending with echo 'export ...' >> ~/.zshrc.local
• tell the user to source ~/.zshrc.local after changes
• do not hardcode secrets in SKILL.md, scripts, repo config, or examples unless they are obviously placeholders

Recommended pattern:

echo 'export COOLIFY_BASE_URL="https://example.com"' >> ~/.zshrc.local
echo 'export COOLIFY_ACCESS_TOKEN="your-token"' >> ~/.zshrc.local
source ~/.zshrc.local

Note: repeated echo >> ~/.zshrc.local commands create duplicate lines. If updating an existing value, replace or remove the old line first.

Bundled Resources

Scripts (scripts/)

Executable code (bash, Node.js, Python, etc.) for tasks requiring deterministic reliability or repeated execution.

When to include:

• The same code is being rewritten repeatedly
• Deterministic reliability is needed
• Complex API interactions

Example: scripts/rotate_pdf.py for PDF rotation tasks

Setup with npm dependencies:

my-skill/
├── SKILL.md
├── package.json       # Declares dependencies
└── scripts/
    └── search.js      # Uses dependencies from package.json

Run npm install or bun install in the skill directory to install dependencies.

Reference in SKILL.md:

## Usage

```bash
bun ~/.pi/agent/skills/my-skill/scripts/search.js "query"

### References (`references/`)

Documentation and reference material loaded on-demand to inform the agent's process.

**When to include**:
- Detailed documentation that would bloat SKILL.md
- Database schemas, API docs, domain knowledge
- Company policies or templates
- Detailed workflow guides

**Benefits**:
- Keeps SKILL.md lean
- Loaded only when needed
- Avoids context bloat

**Best Practice**: If files are large (>10k words), include grep search patterns in SKILL.md

**Reference in SKILL.md**:
```markdown
See `references/schema.md` for the complete database schema.

Reference-First Pattern for Complex Skills:

When a skill covers a large platform (many products, APIs, or modes), structure references by product and keep SKILL.md as a router.

my-platform-skill/
├── SKILL.md           # Decision tree + routing logic
└── references/
    ├── product-a/
    │   ├── README.md
    │   ├── api.md
    │   ├── configuration.md
    │   ├── patterns.md
    │   └── gotchas.md
    └── product-b/
        ├── README.md
        ├── api.md
        └── gotchas.md

SKILL.md as Router:

## Which Product?

Need to store data?
├─ Cache/read-heavy key-value → `references/kv/`
├─ SQL queries and relational data → `references/d1/`
├─ Object/blob storage → `references/r2/`
└─ Stateful per-entity coordination → `references/durable-objects/`

Once you've identified the product, read:
1. `references/<product>/README.md` - Overview and when to use
2. For setup: `references/<product>/configuration.md`
3. For implementation: `references/<product>/api.md` and `references/<product>/patterns.md`
4. For debugging: `references/<product>/gotchas.md`

Assets (assets/)

Files not loaded into context, but used in the output the agent produces.

When to include:

• Templates, images, icons
• Boilerplate code
• Sample documents that get copied or modified

Skills vs MCP Servers

Use a skill when:

• the main value is guidance, workflow, conventions, examples, or scripts
• the agent needs to know how to approach a task
• you want routing logic or documentation for a capability

Use an MCP server when:

• there is a real external tool or API to call
• the tool surface is structured and remote
• Pi should discover and invoke live tools dynamically

Often you want both:

• an MCP server for the actual tool calls
• a skill for best practices, setup, and workflow guidance

MCP Guidance for Skills

Going forward:

• prefer Pi MCP support through pi-mcp-adapter
• configure MCP servers in ~/.pi/agent/mcp.json
• put auth in ~/.zshrc.local
• use ${VAR} interpolation in mcp.json
• do not recommend MCPorter as the default path

If documenting an MCP-backed workflow, mention:

• how to install pi-mcp-adapter
• where MCP config lives
• where auth lives
• whether the skill is standalone or depends on a live MCP server

Core Principles

Concise is Key

The context window is a public good. Skills share context with system prompt, conversation history, and other skills.

Default assumption: The agent is already very smart.

Only add context the agent doesn't already have. Challenge each piece: "Does the agent really need this?" and "Does this justify its token cost?"

Prefer concise examples over verbose explanations.

Set Appropriate Degrees of Freedom

Match specificity to task fragility:

• High freedom (text instructions): Multiple approaches valid, context-dependent decisions
• Medium freedom (pseudocode with parameters): Preferred pattern exists, some variation acceptable
• Low freedom (specific scripts): Operations fragile, consistency critical, specific sequence required

Progressive Disclosure

• Keep SKILL.md body under 500 lines
• Split complex content into references/
• Load reference files on-demand
• Keep cross-references one level deep (SKILL.md → reference, no deep chains)

What NOT to Include

Do NOT create extraneous documentation:

• README.md (SKILL.md IS the readme)
• INSTALLATION_GUIDE.md
• QUICK_REFERENCE.md
• CHANGELOG.md

Skills should only contain information needed for the agent to do the job.

Skill Creation Process

1. Understand with Concrete Examples

Start by understanding how the skill will be used:

• "What functionality should this skill support?"
• "Can you give examples of how this skill would be used?"
• "What would a user say that should trigger this skill?"

2. Plan Reusable Contents

Analyze examples to identify reusable resources:

• Scripts (repeated code, API integrations)
• References (schemas, documentation, domain knowledge)
• Assets (templates, boilerplate, files for output)

3. Create the Skill

Option A: Create from Scratch

mkdir -p ~/.pi/agent/skills/my-skill
touch ~/.pi/agent/skills/my-skill/SKILL.md

Add frontmatter and body following the format above.

Option B: Create an MCP Guidance Skill

If the task involves an MCP server, prefer creating:

• MCP server config in ~/.pi/agent/mcp.json
• a skill that explains setup, auth, workflows, and common commands

Recommended flow:

1. Install adapter if needed:

   pi install npm:pi-mcp-adapter
   

2. Append auth to ~/.zshrc.local with echo 'export ...' >> ~/.zshrc.local
3. Run source ~/.zshrc.local
4. Add server config to ~/.pi/agent/mcp.json
5. Create a skill documenting:
   • what the MCP server does
   • when to use it
   • required env vars
   • common workflows
   • troubleshooting

4. Implement Resources

Scripts: Create executable files in scripts/, add shebang, make executable References: Create markdown files in references/ Assets: Add templates, data files to assets/

If using npm dependencies, create package.json and run npm install or bun install.

Test scripts by actually running them.

5. Write SKILL.md

Follow the format above:

• Complete frontmatter with comprehensive description
• Clear setup instructions
• Concrete usage examples with full paths
• Reference bundled resources
• Mention env-var based auth where applicable

Writing Guidelines:

• Use imperative/infinitive form
• Include "when to use" information in description, NOT in body
• Body is only loaded after triggering
• Prefer durable setup guidance over one-off hacks

6. Iterate

Use the skill on real tasks and refine:

1. Notice struggles or inefficiencies
2. Identify improvements to SKILL.md or bundled resources
3. Implement changes and test again

Examples

Simple Bash Script Skill

~/.pi/agent/skills/transcribe/
├── SKILL.md
└── transcribe.sh

SKILL.md:

---
name: transcribe
description: Speech-to-text transcription using Groq Whisper API. Supports m4a, mp3, wav, ogg, flac, webm. Use when user needs to transcribe audio files.
---

# Transcribe

Speech-to-text using Groq Whisper API.

## Setup

Append `GROQ_API_KEY` to `~/.zshrc.local`, then reload:

```bash
echo 'export GROQ_API_KEY="your-groq-key"' >> ~/.zshrc.local
source ~/.zshrc.local

Usage

bash ~/.pi/agent/skills/transcribe/transcribe.sh <audio-file>

### Complex Platform Skill

~/.pi/agent/skills/cloudflare/ ├── SKILL.md ├── references/ │ ├── kv/ │ │ ├── README.md │ │ ├── api.md │ │ └── gotchas.md │ └── d1/ │ ├── README.md │ ├── api.md │ └── gotchas.md └── scripts/ └── deploy.sh

## Validation

After creating or updating a skill, test it:

1. Restart pi or wait for hot-reload
2. Verify skill appears in skill list
3. Test triggering: Ask a question that should activate the skill
4. Verify the agent loads and uses the skill correctly

## Best Practices

### MUST
- Use the three-part description pattern (capability + use cases + triggers)
- Keep SKILL.md focused and under 500 lines
- Use `references/` for detailed documentation
- Include validation steps
- Use absolute paths from skill directory in examples
- Put auth and secrets in environment variables, preferably `~/.zshrc.local`

### SHOULD
- Prefer bash scripts for simple tasks
- Use npm/bun scripts only when bash + curl insufficient
- Test all scripts before publishing
- Include gotchas.md for complex domains
- Keep reference files under 250 lines each
- Distinguish clearly between standalone skills and MCP-backed workflows

### MUST NOT
- Create overly broad skills (prefer focused, composable skills)
- Duplicate content from other skills
- Include secrets or credentials in skill files
- Create deep reference chains (>1 level)
- Add extraneous documentation files
- Recommend legacy MCPorter workflows as the preferred MCP path

## Related Resources

See `references/workflows.md` for sequential workflow patterns.
See `references/output-patterns.md` for template and example patterns.