ChatGPT Codex CLI OpenAI Skill

skill-creator

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

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Part of openai/skills — 44 skills

Install

skills CLI npx skills add https://github.com/openai/skills/tree/main/skills/.system/skill-creator
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install openai-skills@llmmart
Git git clone https://github.com/openai/skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole openai/skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Skill Creator

This skill provides guidance for creating effective skills.

About Skills

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

Default assumption: Codex is already very smart. Only add context Codex doesn't already have. Challenge each piece of information: "Does Codex 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 Codex 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)
├── agents/ (recommended)
│   └── openai.yaml - UI metadata for skill lists and chips
└── 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. These are the only fields that Codex 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).

Agents metadata (recommended)

  • UI-facing metadata for skill lists and chips
  • Read references/openai_yaml.md before generating values and follow its descriptions and constraints
  • Create: human-facing display_name, short_description, and default_prompt by reading the skill
  • Generate deterministically by passing the values as --interface key=value to scripts/generate_openai_yaml.py or scripts/init_skill.py
  • On updates: validate agents/openai.yaml still matches SKILL.md; regenerate if stale
  • Only include other optional interface fields (icons, brand color) if explicitly provided
  • See references/openai_yaml.md for field definitions and examples

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 Codex for patching or environment-specific adjustments
References (references/)

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

  • When to include: For documentation that Codex 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 Codex 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 Codex 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 Codex 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 Codex (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

# 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

Codex 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, Codex 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, Codex only reads aws.md.

Pattern 3: Conditional details

Show basic content, link to advanced content:

# 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)

Codex 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 Codex 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. Validate the skill (run quick_validate.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. 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:

scripts/init_skill.py <skill-name> --path <output-directory> [--resources scripts,references,assets] [--examples]

Examples:

scripts/init_skill.py my-skill --path skills/public
scripts/init_skill.py my-skill --path skills/public --resources scripts,references
scripts/init_skill.py my-skill --path skills/public --resources scripts --examples

The script:

  • Creates the skill directory at the specified path
  • Generates a SKILL.md template with proper frontmatter and TODO placeholders
  • Creates agents/openai.yaml using agent-generated display_name, short_description, and default_prompt passed via --interface key=value
  • 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.

Generate display_name, short_description, and default_prompt by reading the skill, then pass them as --interface key=value to init_skill.py or regenerate with:

scripts/generate_openai_yaml.py <path/to/skill-folder> --interface key=value

Only include other optional interface fields when the user explicitly provides them. For full field descriptions and examples, see references/openai_yaml.md.

Step 4: Edit the Skill

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

Do not include any other fields in YAML frontmatter.

Body

Write instructions for using the skill and its bundled resources.

Step 5: Validate the Skill

Once development of the skill is complete, validate the skill folder to catch basic issues early:

scripts/quick_validate.py <path/to/skill-folder>

The validation script checks YAML frontmatter format, required fields, and naming rules. If validation fails, fix the reported issues and run the 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
Files (skills)
  • agents
    • openai.yaml 192 B
      interface:
        display_name: "Skill Creator"
        short_description: "Create or update a skill"
        default_prompt: "Read my repository and create a skill to bootstrap new components for my project."
  • references
    • openai_yaml.md 2.1 KB
      # openai.yaml fields (full example + descriptions)
      
      `agents/openai.yaml` is an extended, product-specific config intended for the machine/harness to read, not the agent. Other product-specific config can also live in the `agents/` folder.
      
      ## Full example
      
      ```yaml
      interface:
        display_name: "Optional user-facing name"
        short_description: "Optional user-facing description"
        icon_small: "./assets/small-400px.png"
        icon_large: "./assets/large-logo.svg"
        brand_color: "#3B82F6"
        default_prompt: "Optional surrounding prompt to use the skill with"
      
      dependencies:
        tools:
          - type: "mcp"
            value: "github"
            description: "GitHub MCP server"
            transport: "streamable_http"
            url: "https://api.githubcopilot.com/mcp/"
      ```
      
      ## Field descriptions and constraints
      
      Top-level constraints:
      
      - Quote all string values.
      - Keep keys unquoted.
      - For `interface.default_prompt`: generate a helpful, short (typically 1 sentence) example starting prompt based on the skill. It must explicitly mention the skill as `$skill-name` (e.g., "Use $skill-name-here to draft a concise weekly status update.").
      
      - `interface.display_name`: Human-facing title shown in UI skill lists and chips.
      - `interface.short_description`: Human-facing short UI blurb (25–64 chars) for quick scanning.
      - `interface.icon_small`: Path to a small icon asset (relative to skill dir). Default to `./assets/` and place icons in the skill's `assets/` folder.
      - `interface.icon_large`: Path to a larger logo asset (relative to skill dir). Default to `./assets/` and place icons in the skill's `assets/` folder.
      - `interface.brand_color`: Hex color used for UI accents (e.g., badges).
      - `interface.default_prompt`: Default prompt snippet inserted when invoking the skill.
      - `dependencies.tools[].type`: Dependency category. Only `mcp` is supported for now.
      - `dependencies.tools[].value`: Identifier of the tool or dependency.
      - `dependencies.tools[].description`: Human-readable explanation of the dependency.
      - `dependencies.tools[].transport`: Connection type when `type` is `mcp`.
      - `dependencies.tools[].url`: MCP server URL when `type` is `mcp`.
      
  • scripts
    • generate_openai_yaml.py 6.5 KB
      #!/usr/bin/env python3
      """
      OpenAI YAML Generator - Creates agents/openai.yaml for a skill folder.
      
      Usage:
          generate_openai_yaml.py <skill_dir> [--name <skill_name>] [--interface key=value]
      """
      
      import argparse
      import re
      import sys
      from pathlib import Path
      
      import yaml
      
      ACRONYMS = {
          "GH",
          "MCP",
          "API",
          "CI",
          "CLI",
          "LLM",
          "PDF",
          "PR",
          "UI",
          "URL",
          "SQL",
      }
      
      BRANDS = {
          "openai": "OpenAI",
          "openapi": "OpenAPI",
          "github": "GitHub",
          "pagerduty": "PagerDuty",
          "datadog": "DataDog",
          "sqlite": "SQLite",
          "fastapi": "FastAPI",
      }
      
      SMALL_WORDS = {"and", "or", "to", "up", "with"}
      
      ALLOWED_INTERFACE_KEYS = {
          "display_name",
          "short_description",
          "icon_small",
          "icon_large",
          "brand_color",
          "default_prompt",
      }
      
      
      def yaml_quote(value):
          escaped = value.replace("\\", "\\\\").replace('"', '\\"').replace("\n", "\\n")
          return f'"{escaped}"'
      
      
      def format_display_name(skill_name):
          words = [word for word in skill_name.split("-") if word]
          formatted = []
          for index, word in enumerate(words):
              lower = word.lower()
              upper = word.upper()
              if upper in ACRONYMS:
                  formatted.append(upper)
                  continue
              if lower in BRANDS:
                  formatted.append(BRANDS[lower])
                  continue
              if index > 0 and lower in SMALL_WORDS:
                  formatted.append(lower)
                  continue
              formatted.append(word.capitalize())
          return " ".join(formatted)
      
      
      def generate_short_description(display_name):
          description = f"Help with {display_name} tasks"
      
          if len(description) < 25:
              description = f"Help with {display_name} tasks and workflows"
          if len(description) < 25:
              description = f"Help with {display_name} tasks with guidance"
      
          if len(description) > 64:
              description = f"Help with {display_name}"
          if len(description) > 64:
              description = f"{display_name} helper"
          if len(description) > 64:
              description = f"{display_name} tools"
          if len(description) > 64:
              suffix = " helper"
              max_name_length = 64 - len(suffix)
              trimmed = display_name[:max_name_length].rstrip()
              description = f"{trimmed}{suffix}"
          if len(description) > 64:
              description = description[:64].rstrip()
      
          if len(description) < 25:
              description = f"{description} workflows"
              if len(description) > 64:
                  description = description[:64].rstrip()
      
          return description
      
      
      def read_frontmatter_name(skill_dir):
          skill_md = Path(skill_dir) / "SKILL.md"
          if not skill_md.exists():
              print(f"[ERROR] SKILL.md not found in {skill_dir}")
              return None
          content = skill_md.read_text()
          match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
          if not match:
              print("[ERROR] Invalid SKILL.md frontmatter format.")
              return None
          frontmatter_text = match.group(1)
          try:
              frontmatter = yaml.safe_load(frontmatter_text)
          except yaml.YAMLError as exc:
              print(f"[ERROR] Invalid YAML frontmatter: {exc}")
              return None
          if not isinstance(frontmatter, dict):
              print("[ERROR] Frontmatter must be a YAML dictionary.")
              return None
          name = frontmatter.get("name", "")
          if not isinstance(name, str) or not name.strip():
              print("[ERROR] Frontmatter 'name' is missing or invalid.")
              return None
          return name.strip()
      
      
      def parse_interface_overrides(raw_overrides):
          overrides = {}
          optional_order = []
          for item in raw_overrides:
              if "=" not in item:
                  print(f"[ERROR] Invalid interface override '{item}'. Use key=value.")
                  return None, None
              key, value = item.split("=", 1)
              key = key.strip()
              value = value.strip()
              if not key:
                  print(f"[ERROR] Invalid interface override '{item}'. Key is empty.")
                  return None, None
              if key not in ALLOWED_INTERFACE_KEYS:
                  allowed = ", ".join(sorted(ALLOWED_INTERFACE_KEYS))
                  print(f"[ERROR] Unknown interface field '{key}'. Allowed: {allowed}")
                  return None, None
              overrides[key] = value
              if key not in ("display_name", "short_description") and key not in optional_order:
                  optional_order.append(key)
          return overrides, optional_order
      
      
      def write_openai_yaml(skill_dir, skill_name, raw_overrides):
          overrides, optional_order = parse_interface_overrides(raw_overrides)
          if overrides is None:
              return None
      
          display_name = overrides.get("display_name") or format_display_name(skill_name)
          short_description = overrides.get("short_description") or generate_short_description(display_name)
      
          if not (25 <= len(short_description) <= 64):
              print(
                  "[ERROR] short_description must be 25-64 characters "
                  f"(got {len(short_description)})."
              )
              return None
      
          interface_lines = [
              "interface:",
              f"  display_name: {yaml_quote(display_name)}",
              f"  short_description: {yaml_quote(short_description)}",
          ]
      
          for key in optional_order:
              value = overrides.get(key)
              if value is not None:
                  interface_lines.append(f"  {key}: {yaml_quote(value)}")
      
          agents_dir = Path(skill_dir) / "agents"
          agents_dir.mkdir(parents=True, exist_ok=True)
          output_path = agents_dir / "openai.yaml"
          output_path.write_text("\n".join(interface_lines) + "\n")
          print(f"[OK] Created agents/openai.yaml")
          return output_path
      
      
      def main():
          parser = argparse.ArgumentParser(
              description="Create agents/openai.yaml for a skill directory.",
          )
          parser.add_argument("skill_dir", help="Path to the skill directory")
          parser.add_argument(
              "--name",
              help="Skill name override (defaults to SKILL.md frontmatter)",
          )
          parser.add_argument(
              "--interface",
              action="append",
              default=[],
              help="Interface override in key=value format (repeatable)",
          )
          args = parser.parse_args()
      
          skill_dir = Path(args.skill_dir).resolve()
          if not skill_dir.exists():
              print(f"[ERROR] Skill directory not found: {skill_dir}")
              sys.exit(1)
          if not skill_dir.is_dir():
              print(f"[ERROR] Path is not a directory: {skill_dir}")
              sys.exit(1)
      
          skill_name = args.name or read_frontmatter_name(skill_dir)
          if not skill_name:
              sys.exit(1)
      
          result = write_openai_yaml(skill_dir, skill_name, args.interface)
          if result:
              sys.exit(0)
          sys.exit(1)
      
      
      if __name__ == "__main__":
          main()
      
    • init_skill.py 14.1 KB
      #!/usr/bin/env python3
      """
      Skill Initializer - Creates a new skill from template
      
      Usage:
          init_skill.py <skill-name> --path <path> [--resources scripts,references,assets] [--examples] [--interface key=value]
      
      Examples:
          init_skill.py my-new-skill --path skills/public
          init_skill.py my-new-skill --path skills/public --resources scripts,references
          init_skill.py my-api-helper --path skills/private --resources scripts --examples
          init_skill.py custom-skill --path /custom/location
          init_skill.py my-skill --path skills/public --interface short_description="Short UI label"
      """
      
      import argparse
      import re
      import sys
      from pathlib import Path
      
      from generate_openai_yaml import write_openai_yaml
      
      MAX_SKILL_NAME_LENGTH = 64
      ALLOWED_RESOURCES = {"scripts", "references", "assets"}
      
      SKILL_TEMPLATE = """---
      name: {skill_name}
      description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
      ---
      
      # {skill_title}
      
      ## Overview
      
      [TODO: 1-2 sentences explaining what this skill enables]
      
      ## Structuring This Skill
      
      [TODO: Choose the structure that best fits this skill's purpose. Common patterns:
      
      **1. Workflow-Based** (best for sequential processes)
      - Works well when there are clear step-by-step procedures
      - Example: DOCX skill with "Workflow Decision Tree" -> "Reading" -> "Creating" -> "Editing"
      - Structure: ## Overview -> ## Workflow Decision Tree -> ## Step 1 -> ## Step 2...
      
      **2. Task-Based** (best for tool collections)
      - Works well when the skill offers different operations/capabilities
      - Example: PDF skill with "Quick Start" -> "Merge PDFs" -> "Split PDFs" -> "Extract Text"
      - Structure: ## Overview -> ## Quick Start -> ## Task Category 1 -> ## Task Category 2...
      
      **3. Reference/Guidelines** (best for standards or specifications)
      - Works well for brand guidelines, coding standards, or requirements
      - Example: Brand styling with "Brand Guidelines" -> "Colors" -> "Typography" -> "Features"
      - Structure: ## Overview -> ## Guidelines -> ## Specifications -> ## Usage...
      
      **4. Capabilities-Based** (best for integrated systems)
      - Works well when the skill provides multiple interrelated features
      - Example: Product Management with "Core Capabilities" -> numbered capability list
      - Structure: ## Overview -> ## Core Capabilities -> ### 1. Feature -> ### 2. Feature...
      
      Patterns can be mixed and matched as needed. Most skills combine patterns (e.g., start with task-based, add workflow for complex operations).
      
      Delete this entire "Structuring This Skill" section when done - it's just guidance.]
      
      ## [TODO: Replace with the first main section based on chosen structure]
      
      [TODO: Add content here. See examples in existing skills:
      - Code samples for technical skills
      - Decision trees for complex workflows
      - Concrete examples with realistic user requests
      - References to scripts/templates/references as needed]
      
      ## Resources (optional)
      
      Create only the resource directories this skill actually needs. Delete this section if no resources are required.
      
      ### scripts/
      Executable code (Python/Bash/etc.) that can be run directly to perform specific operations.
      
      **Examples from other skills:**
      - PDF skill: `fill_fillable_fields.py`, `extract_form_field_info.py` - utilities for PDF manipulation
      - DOCX skill: `document.py`, `utilities.py` - Python modules for document processing
      
      **Appropriate for:** Python scripts, shell scripts, or any executable code that performs automation, data processing, or specific operations.
      
      **Note:** Scripts may be executed without loading into context, but can still be read by Codex for patching or environment adjustments.
      
      ### references/
      Documentation and reference material intended to be loaded into context to inform Codex's process and thinking.
      
      **Examples from other skills:**
      - Product management: `communication.md`, `context_building.md` - detailed workflow guides
      - BigQuery: API reference documentation and query examples
      - Finance: Schema documentation, company policies
      
      **Appropriate for:** In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Codex should reference while working.
      
      ### assets/
      Files not intended to be loaded into context, but rather used within the output Codex produces.
      
      **Examples from other skills:**
      - Brand styling: PowerPoint template files (.pptx), logo files
      - Frontend builder: HTML/React boilerplate project directories
      - Typography: Font files (.ttf, .woff2)
      
      **Appropriate for:** Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
      
      ---
      
      **Not every skill requires all three types of resources.**
      """
      
      EXAMPLE_SCRIPT = '''#!/usr/bin/env python3
      """
      Example helper script for {skill_name}
      
      This is a placeholder script that can be executed directly.
      Replace with actual implementation or delete if not needed.
      
      Example real scripts from other skills:
      - pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
      - pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
      """
      
      def main():
          print("This is an example script for {skill_name}")
          # TODO: Add actual script logic here
          # This could be data processing, file conversion, API calls, etc.
      
      if __name__ == "__main__":
          main()
      '''
      
      EXAMPLE_REFERENCE = """# Reference Documentation for {skill_title}
      
      This is a placeholder for detailed reference documentation.
      Replace with actual reference content or delete if not needed.
      
      Example real reference docs from other skills:
      - product-management/references/communication.md - Comprehensive guide for status updates
      - product-management/references/context_building.md - Deep-dive on gathering context
      - bigquery/references/ - API references and query examples
      
      ## When Reference Docs Are Useful
      
      Reference docs are ideal for:
      - Comprehensive API documentation
      - Detailed workflow guides
      - Complex multi-step processes
      - Information too lengthy for main SKILL.md
      - Content that's only needed for specific use cases
      
      ## Structure Suggestions
      
      ### API Reference Example
      - Overview
      - Authentication
      - Endpoints with examples
      - Error codes
      - Rate limits
      
      ### Workflow Guide Example
      - Prerequisites
      - Step-by-step instructions
      - Common patterns
      - Troubleshooting
      - Best practices
      """
      
      EXAMPLE_ASSET = """# Example Asset File
      
      This placeholder represents where asset files would be stored.
      Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
      
      Asset files are NOT intended to be loaded into context, but rather used within
      the output Codex produces.
      
      Example asset files from other skills:
      - Brand guidelines: logo.png, slides_template.pptx
      - Frontend builder: hello-world/ directory with HTML/React boilerplate
      - Typography: custom-font.ttf, font-family.woff2
      - Data: sample_data.csv, test_dataset.json
      
      ## Common Asset Types
      
      - Templates: .pptx, .docx, boilerplate directories
      - Images: .png, .jpg, .svg, .gif
      - Fonts: .ttf, .otf, .woff, .woff2
      - Boilerplate code: Project directories, starter files
      - Icons: .ico, .svg
      - Data files: .csv, .json, .xml, .yaml
      
      Note: This is a text placeholder. Actual assets can be any file type.
      """
      
      
      def normalize_skill_name(skill_name):
          """Normalize a skill name to lowercase hyphen-case."""
          normalized = skill_name.strip().lower()
          normalized = re.sub(r"[^a-z0-9]+", "-", normalized)
          normalized = normalized.strip("-")
          normalized = re.sub(r"-{2,}", "-", normalized)
          return normalized
      
      
      def title_case_skill_name(skill_name):
          """Convert hyphenated skill name to Title Case for display."""
          return " ".join(word.capitalize() for word in skill_name.split("-"))
      
      
      def parse_resources(raw_resources):
          if not raw_resources:
              return []
          resources = [item.strip() for item in raw_resources.split(",") if item.strip()]
          invalid = sorted({item for item in resources if item not in ALLOWED_RESOURCES})
          if invalid:
              allowed = ", ".join(sorted(ALLOWED_RESOURCES))
              print(f"[ERROR] Unknown resource type(s): {', '.join(invalid)}")
              print(f"   Allowed: {allowed}")
              sys.exit(1)
          deduped = []
          seen = set()
          for resource in resources:
              if resource not in seen:
                  deduped.append(resource)
                  seen.add(resource)
          return deduped
      
      
      def create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples):
          for resource in resources:
              resource_dir = skill_dir / resource
              resource_dir.mkdir(exist_ok=True)
              if resource == "scripts":
                  if include_examples:
                      example_script = resource_dir / "example.py"
                      example_script.write_text(EXAMPLE_SCRIPT.format(skill_name=skill_name))
                      example_script.chmod(0o755)
                      print("[OK] Created scripts/example.py")
                  else:
                      print("[OK] Created scripts/")
              elif resource == "references":
                  if include_examples:
                      example_reference = resource_dir / "api_reference.md"
                      example_reference.write_text(EXAMPLE_REFERENCE.format(skill_title=skill_title))
                      print("[OK] Created references/api_reference.md")
                  else:
                      print("[OK] Created references/")
              elif resource == "assets":
                  if include_examples:
                      example_asset = resource_dir / "example_asset.txt"
                      example_asset.write_text(EXAMPLE_ASSET)
                      print("[OK] Created assets/example_asset.txt")
                  else:
                      print("[OK] Created assets/")
      
      
      def init_skill(skill_name, path, resources, include_examples, interface_overrides):
          """
          Initialize a new skill directory with template SKILL.md.
      
          Args:
              skill_name: Name of the skill
              path: Path where the skill directory should be created
              resources: Resource directories to create
              include_examples: Whether to create example files in resource directories
      
          Returns:
              Path to created skill directory, or None if error
          """
          # Determine skill directory path
          skill_dir = Path(path).resolve() / skill_name
      
          # Check if directory already exists
          if skill_dir.exists():
              print(f"[ERROR] Skill directory already exists: {skill_dir}")
              return None
      
          # Create skill directory
          try:
              skill_dir.mkdir(parents=True, exist_ok=False)
              print(f"[OK] Created skill directory: {skill_dir}")
          except Exception as e:
              print(f"[ERROR] Error creating directory: {e}")
              return None
      
          # Create SKILL.md from template
          skill_title = title_case_skill_name(skill_name)
          skill_content = SKILL_TEMPLATE.format(skill_name=skill_name, skill_title=skill_title)
      
          skill_md_path = skill_dir / "SKILL.md"
          try:
              skill_md_path.write_text(skill_content)
              print("[OK] Created SKILL.md")
          except Exception as e:
              print(f"[ERROR] Error creating SKILL.md: {e}")
              return None
      
          # Create agents/openai.yaml
          try:
              result = write_openai_yaml(skill_dir, skill_name, interface_overrides)
              if not result:
                  return None
          except Exception as e:
              print(f"[ERROR] Error creating agents/openai.yaml: {e}")
              return None
      
          # Create resource directories if requested
          if resources:
              try:
                  create_resource_dirs(skill_dir, skill_name, skill_title, resources, include_examples)
              except Exception as e:
                  print(f"[ERROR] Error creating resource directories: {e}")
                  return None
      
          # Print next steps
          print(f"\n[OK] Skill '{skill_name}' initialized successfully at {skill_dir}")
          print("\nNext steps:")
          print("1. Edit SKILL.md to complete the TODO items and update the description")
          if resources:
              if include_examples:
                  print("2. Customize or delete the example files in scripts/, references/, and assets/")
              else:
                  print("2. Add resources to scripts/, references/, and assets/ as needed")
          else:
              print("2. Create resource directories only if needed (scripts/, references/, assets/)")
          print("3. Update agents/openai.yaml if the UI metadata should differ")
          print("4. Run the validator when ready to check the skill structure")
      
          return skill_dir
      
      
      def main():
          parser = argparse.ArgumentParser(
              description="Create a new skill directory with a SKILL.md template.",
          )
          parser.add_argument("skill_name", help="Skill name (normalized to hyphen-case)")
          parser.add_argument("--path", required=True, help="Output directory for the skill")
          parser.add_argument(
              "--resources",
              default="",
              help="Comma-separated list: scripts,references,assets",
          )
          parser.add_argument(
              "--examples",
              action="store_true",
              help="Create example files inside the selected resource directories",
          )
          parser.add_argument(
              "--interface",
              action="append",
              default=[],
              help="Interface override in key=value format (repeatable)",
          )
          args = parser.parse_args()
      
          raw_skill_name = args.skill_name
          skill_name = normalize_skill_name(raw_skill_name)
          if not skill_name:
              print("[ERROR] Skill name must include at least one letter or digit.")
              sys.exit(1)
          if len(skill_name) > MAX_SKILL_NAME_LENGTH:
              print(
                  f"[ERROR] Skill name '{skill_name}' is too long ({len(skill_name)} characters). "
                  f"Maximum is {MAX_SKILL_NAME_LENGTH} characters."
              )
              sys.exit(1)
          if skill_name != raw_skill_name:
              print(f"Note: Normalized skill name from '{raw_skill_name}' to '{skill_name}'.")
      
          resources = parse_resources(args.resources)
          if args.examples and not resources:
              print("[ERROR] --examples requires --resources to be set.")
              sys.exit(1)
      
          path = args.path
      
          print(f"Initializing skill: {skill_name}")
          print(f"   Location: {path}")
          if resources:
              print(f"   Resources: {', '.join(resources)}")
              if args.examples:
                  print("   Examples: enabled")
          else:
              print("   Resources: none (create as needed)")
          print()
      
          result = init_skill(skill_name, path, resources, args.examples, args.interface)
      
          if result:
              sys.exit(0)
          else:
              sys.exit(1)
      
      
      if __name__ == "__main__":
          main()
      
    • quick_validate.py 3.2 KB
      #!/usr/bin/env python3
      """
      Quick validation script for skills - minimal version
      """
      
      import re
      import sys
      from pathlib import Path
      
      import yaml
      
      MAX_SKILL_NAME_LENGTH = 64
      
      
      def validate_skill(skill_path):
          """Basic validation of a skill"""
          skill_path = Path(skill_path)
      
          skill_md = skill_path / "SKILL.md"
          if not skill_md.exists():
              return False, "SKILL.md not found"
      
          content = skill_md.read_text()
          if not content.startswith("---"):
              return False, "No YAML frontmatter found"
      
          match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
          if not match:
              return False, "Invalid frontmatter format"
      
          frontmatter_text = match.group(1)
      
          try:
              frontmatter = yaml.safe_load(frontmatter_text)
              if not isinstance(frontmatter, dict):
                  return False, "Frontmatter must be a YAML dictionary"
          except yaml.YAMLError as e:
              return False, f"Invalid YAML in frontmatter: {e}"
      
          allowed_properties = {"name", "description", "license", "allowed-tools", "metadata"}
      
          unexpected_keys = set(frontmatter.keys()) - allowed_properties
          if unexpected_keys:
              allowed = ", ".join(sorted(allowed_properties))
              unexpected = ", ".join(sorted(unexpected_keys))
              return (
                  False,
                  f"Unexpected key(s) in SKILL.md frontmatter: {unexpected}. Allowed properties are: {allowed}",
              )
      
          if "name" not in frontmatter:
              return False, "Missing 'name' in frontmatter"
          if "description" not in frontmatter:
              return False, "Missing 'description' in frontmatter"
      
          name = frontmatter.get("name", "")
          if not isinstance(name, str):
              return False, f"Name must be a string, got {type(name).__name__}"
          name = name.strip()
          if name:
              if not re.match(r"^[a-z0-9-]+$", name):
                  return (
                      False,
                      f"Name '{name}' should be hyphen-case (lowercase letters, digits, and hyphens only)",
                  )
              if name.startswith("-") or name.endswith("-") or "--" in name:
                  return (
                      False,
                      f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens",
                  )
              if len(name) > MAX_SKILL_NAME_LENGTH:
                  return (
                      False,
                      f"Name is too long ({len(name)} characters). "
                      f"Maximum is {MAX_SKILL_NAME_LENGTH} characters.",
                  )
      
          description = frontmatter.get("description", "")
          if not isinstance(description, str):
              return False, f"Description must be a string, got {type(description).__name__}"
          description = description.strip()
          if description:
              if "<" in description or ">" in description:
                  return False, "Description cannot contain angle brackets (< or >)"
              if len(description) > 1024:
                  return (
                      False,
                      f"Description is too long ({len(description)} characters). Maximum is 1024 characters.",
                  )
      
          return True, "Skill is valid!"
      
      
      if __name__ == "__main__":
          if len(sys.argv) != 2:
              print("Usage: python quick_validate.py <skill_directory>")
              sys.exit(1)
      
          valid, message = validate_skill(sys.argv[1])
          print(message)
          sys.exit(0 if valid else 1)
      
  • LICENSE.txt 11.1 KB
    
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  • SKILL.md 18.2 KB
    ---
    name: skill-creator
    description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
    metadata:
      short-description: Create or update a skill
    ---
    
    # Skill Creator
    
    This skill provides guidance for creating effective skills.
    
    ## About Skills
    
    Skills are modular, self-contained folders that extend Codex's capabilities by providing
    specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
    domains or tasks—they transform Codex 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 Codex needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
    
    **Default assumption: Codex is already very smart.** Only add context Codex doesn't already have. Challenge each piece of information: "Does Codex 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 Codex 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)
    ├── agents/ (recommended)
    │   └── openai.yaml - UI metadata for skill lists and chips
    └── 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. These are the only fields that Codex 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).
    
    #### Agents metadata (recommended)
    
    - UI-facing metadata for skill lists and chips
    - Read references/openai_yaml.md before generating values and follow its descriptions and constraints
    - Create: human-facing `display_name`, `short_description`, and `default_prompt` by reading the skill
    - Generate deterministically by passing the values as `--interface key=value` to `scripts/generate_openai_yaml.py` or `scripts/init_skill.py`
    - On updates: validate `agents/openai.yaml` still matches SKILL.md; regenerate if stale
    - Only include other optional interface fields (icons, brand color) if explicitly provided
    - See references/openai_yaml.md for field definitions and examples
    
    #### 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 Codex for patching or environment-specific adjustments
    
    ##### References (`references/`)
    
    Documentation and reference material intended to be loaded as needed into context to inform Codex's process and thinking.
    
    - **When to include**: For documentation that Codex 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 Codex 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 Codex 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 Codex 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 Codex (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
    ```
    
    Codex 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, Codex 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, Codex 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)
    ```
    
    Codex 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 Codex 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. Validate the skill (run quick_validate.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. 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> [--resources scripts,references,assets] [--examples]
    ```
    
    Examples:
    
    ```bash
    scripts/init_skill.py my-skill --path skills/public
    scripts/init_skill.py my-skill --path skills/public --resources scripts,references
    scripts/init_skill.py my-skill --path skills/public --resources scripts --examples
    ```
    
    The script:
    
    - Creates the skill directory at the specified path
    - Generates a SKILL.md template with proper frontmatter and TODO placeholders
    - Creates `agents/openai.yaml` using agent-generated `display_name`, `short_description`, and `default_prompt` passed via `--interface key=value`
    - 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.
    
    Generate `display_name`, `short_description`, and `default_prompt` by reading the skill, then pass them as `--interface key=value` to `init_skill.py` or regenerate with:
    
    ```bash
    scripts/generate_openai_yaml.py <path/to/skill-folder> --interface key=value
    ```
    
    Only include other optional interface fields when the user explicitly provides them. For full field descriptions and examples, see references/openai_yaml.md.
    
    ### Step 4: Edit the Skill
    
    When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Codex to use. Include information that would be beneficial and non-obvious to Codex. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Codex 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 Codex 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 Codex.
      - 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 Codex 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"
    
    Do not include any other fields in YAML frontmatter.
    
    ##### Body
    
    Write instructions for using the skill and its bundled resources.
    
    ### Step 5: Validate the Skill
    
    Once development of the skill is complete, validate the skill folder to catch basic issues early:
    
    ```bash
    scripts/quick_validate.py <path/to/skill-folder>
    ```
    
    The validation script checks YAML frontmatter format, required fields, and naming rules. If validation fails, fix the reported issues and run the 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
    

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