output-dev-skill-file

name	output-dev-skill-file
description	Create .md skill files for Output framework's lazy-loaded instruction system. Use when adding skills to prompts, configuring skill loading, or debugging skill resolution.
allowed-tools	["Read","Write","Edit"]

Creating Skill Files

Overview

This skill documents how to create .md skill files for the Output framework's skills system. Skills are lazy-loaded instruction packages that keep prompts lightweight. The LLM sees a list of skill names and descriptions in the system message, then calls a load_skill tool to retrieve full instructions on demand.

Important: These are framework skills (.md files loaded by LLMs at runtime), not Claude Code plugin skills. The naming is similar but the systems are separate.

When to Use This Skill

• Adding reusable instruction sets to LLM prompts
• Configuring how skills are loaded (auto-discovery, frontmatter, inline)
• Debugging skill resolution or load_skill tool issues
• Organizing shared expertise across multiple prompts

Location Convention

Skill files live in a skills/ folder next to the prompt file. Output auto-discovers them with no configuration needed:

src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts
├── types.ts
└── prompts/
    ├── [e-mail]
    └── skills/
        ├── clarity_guidelines.md
        ├── response_format.md
        └── structure_guide.md

The skills/ folder is relative to the prompt file location, not the workflow root.

Skill File Format

Skill files are markdown documents with an optional YAML frontmatter block:

---
name: clarity_guidelines
description: Rules for writing clear, readable technical content
---

# Clarity Guidelines

When reviewing or writing technical content for clarity:

1. **Sentence length**: Keep sentences under 25 words when possible.
   Break complex ideas into multiple sentences.

2. **Active voice**: Prefer active voice ("The function returns X")
   over passive ("X is returned by the function").

3. **Jargon**: Define technical terms on first use.
   Avoid unnecessary acronyms without explanation.

4. **Concrete examples**: Every abstract concept should have
   a concrete example.

When applying this skill, flag any violations you find
and suggest improvements.

Frontmatter Fields

Field	Required	Default	Description
name	No	Filename without .md	Identifier the LLM uses with load_skill
description	No	Same as name	Shown in system message, helps LLM decide when to load
Body	Yes	-	Full instructions returned when LLM calls load_skill

If you omit the frontmatter entirely, the filename (without .md) is used as both the name and description. A file named clarity_guidelines.md with no frontmatter gets name: "clarity_guidelines" and description: "clarity_guidelines".

Write good descriptions. They appear in the system message and are what the LLM uses to decide whether to load a skill. "Rules for writing clear, readable technical content" is better than "clarity_guidelines".

How Skills Are Loaded

Method 1: Colocated Auto-Discovery (Default)

Place .md files in a skills/ folder next to your prompt file. Output discovers them automatically. The prompt file needs no special configuration. (Model lines below are current as of 2026-05-04 — refresh via output-dev-model-selection.)

---
provider: anthropic
model: claude-sonnet-4-6
maxTokens: 2048
---

<system>
You are an expert technical writing assistant.
Use load_skill to get full instructions for any skill before applying it.
</system>

<user>
Review the following {{ content_type }} content focusing on {{ focus }}.

Content:
{{ content }}
</user>

At runtime, Output finds the colocated skills/ directory, loads all .md files, and:

1. Adds a summary of available skills to the system message
2. Injects a load_skill tool the LLM can call

Method 2: Frontmatter Paths (Explicit)

Reference specific skill files or directories in the prompt YAML frontmatter. Paths resolve relative to the prompt file:

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
skills:
  - ./skills/
  - ../shared_skills/tone_guide.md
---

When skills: is set in frontmatter, auto-discovery is skipped. Only the listed paths are loaded.

Method 3: Inline Skills (Code)

Create skills programmatically with the skill() function from @outputai/llm:

import { skill } from '@outputai/llm';

const audienceSkill = skill( {
  name: 'audience_adaptation',
  description: 'Tailor feedback for the specified expertise level',
  instructions: `# Audience Adaptation

When the target audience is specified, adjust your feedback:

**Beginner**: Flag jargon as high-priority issues.
**Expert**: Focus on accuracy and completeness.

Always mention the audience level in your summary.`
} );

Pass inline skills to generateText or Agent:

const { result } = await generateText( {
  prompt: 'writing_assistant@v1',
  variables: { content_type: 'documentation', focus: 'clarity', content: input.content },
  skills: [ audienceSkill ],
  maxSteps: 5
} );

Inline skills are merged with any file-based skills.

Resolution Priority

Skills are resolved in this order:

1. Frontmatter paths: If skills: is set in the prompt frontmatter, those paths are loaded
2. Colocated auto-discovery: If no skills: in frontmatter, the skills/ directory next to the prompt file is scanned
3. Caller-provided skills: Skills passed via code (skills: [...] in generateText or Agent) are always merged in

Frontmatter paths and colocated auto-discovery are mutually exclusive. Setting skills: in frontmatter disables auto-discovery. Caller-provided skills are always added regardless of which file-based method is used.

Disabling Skills

Set skills: [] in the prompt frontmatter to opt out of auto-discovery:

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-haiku-4-5-20251001
skills: []
---

This is useful when you have a skills/ directory for other prompts in the same folder, but a specific prompt should not load any skills.

Complete Example

Skill File

---
name: response_format
description: Standard format requirements for all review responses
---

# Response Format

Every response MUST end with the exact string "OUTPUT_COMPLETE" on its own line.

Structure your review as follows:

1. **Summary**: 2-3 sentence overview of the content quality
2. **Issues**: Numbered list of specific problems found
3. **Suggestions**: Actionable improvements for each issue
4. **Score**: Overall quality score from 0-100

OUTPUT_COMPLETE

Prompt File Using Skills

---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
maxTokens: 2048
---

<system>
You are an expert technical writing assistant.
Use load_skill to get the full instructions for any skill before applying it.
After reviewing, provide structured feedback with specific issues and suggestions.
</system>

<user>
Review the following {{ content_type }} content focusing on {{ focus }}.

Content:
{{ content }}
</user>

Step Using the Prompt

import { step, z } from '@outputai/core';
import { Agent, Output } from '@outputai/llm';

export const reviewContent = step( {
  name: 'reviewContent',
  description: 'Review content using skills for specialized expertise',
  inputSchema: z.object( {
    content: z.string(),
    content_type: z.string(),
    focus: z.string()
  } ),
  outputSchema: z.object( {
    summary: z.string(),
    issues: z.array( z.string() ),
    suggestions: z.array( z.string() ),
    score: z.number()
  } ),
  fn: async input => {
    const agent = new Agent( {
      prompt: 'writing_assistant@v1',
      variables: input,
      output: Output.object( {
        schema: z.object( {
          summary: z.string().describe( '2-3 sentence overview' ),
          issues: z.array( z.string() ).describe( 'Specific problems found' ),
          suggestions: z.array( z.string() ).describe( 'Actionable improvements' ),
          score: z.number().describe( 'Quality score 0-100' )
        } )
      } ),
      maxSteps: 5
    } );
    const { output } = await agent.generate();
    return output;
  }
} );

Best Practices

1. Write Focused Skills

Each skill should cover one area of expertise. Prefer multiple focused skills over one large skill:

skills/
├── clarity_guidelines.md      # Writing clarity
├── structure_guide.md         # Document structure
└── response_format.md         # Output formatting

2. Write Descriptive Descriptions

The description appears in the system message. Make it clear when the LLM should load this skill:

---
name: clarity_guidelines
description: Rules for writing clear, readable technical content
---

Not:

---
name: clarity_guidelines
description: clarity_guidelines
---

3. Structure Instructions with Headers

Use markdown headers and lists for scannable instructions:

# Clarity Guidelines

## Rules
1. Keep sentences under 25 words
2. Prefer active voice
3. Define jargon on first use

## When to Flag
- Sentences over 30 words
- Passive voice in instructions
- Undefined acronyms

4. Include Actionable Guidance

Tell the LLM what to do with the skill, not just what the skill is about:

When applying this skill, flag any violations you find and suggest improvements.

Verification Checklist

• Skill files are .md format in a skills/ directory next to the prompt file
• Each skill has a clear, descriptive description in frontmatter
• Skill body contains actionable instructions
• Prompt file mentions load_skill in system message (when using auto-discovery)
• If using frontmatter paths, all paths resolve correctly
• If using skills: [], auto-discovery is intentionally disabled
• Skills are focused (one area of expertise per file)
• Step code passes maxSteps (default 10) to allow tool loop iterations

Related Skills

• output-dev-prompt-file - Creating .prompt files that use skills
• output-dev-agent-class - Using the Agent class with skills
• output-dev-step-function - Using skills in step functions
• output-dev-folder-structure - Understanding skill file locations