skill-creator

Skill Creator

Create skills that give Codex useful, non-obvious guidance without constraining unrelated work.

Core Principles

Assume Codex is already capable. Include only information that changes its decisions or improves its work. Remove generic advice, repeated instructions, speculative edge cases, and examples that do not materially clarify the task.

Preserve user intent and scope. A skill should support the requested task, not replace the user's chosen product, expand the assignment, modify unrelated configuration, or imply permission for additional external actions. Do not turn a particular example, past failure, or personal preference into a universal requirement.

Approval to complete a task does not expand its scope or execution permissions. For retrying or externally mutating workflows, define a stopping condition proportional to the risk.

Match specificity to the risk. Give the model room to choose an appropriate approach when multiple approaches are reasonable. Use detailed steps, deterministic scripts, or absolute language only when correctness, safety, permissions, or a genuinely fragile workflow requires them.

For open-ended work, describe the outcome and relevant decision criteria. For workflows with a preferred shape, offer useful examples or configurable scripts. Reserve fixed sequences and narrow parameters for operations where deviation would cause a concrete problem. Preserve non-obvious operational invariants, distinguish actual requirements from optional recommendations or local conventions, and avoid restating policies already enforced elsewhere.

Keep discovery cheap and precise. Skill names and descriptions are available before a skill is loaded. Describe the actual capability and when it applies, adding exclusions only when they prevent likely misrouting. Avoid exhaustive capability lists and catchalls that attract unrelated requests.

Keep skills self-contained; refer to another skill or tool only when the requested workflow genuinely requires it and it is available in the target environment. Specialized review, hardening, or audit workflows should apply when requested or genuinely needed, not merely because ordinary work touches the same subject.

Disclose detail progressively. Keep shared purpose, essential constraints, and useful routing in SKILL.md. Put substantial mode-specific guidance, schemas, examples, or procedures in supporting references and read only the references relevant to the current task. A simple self-contained skill does not need a router or extra files.

Anatomy of a Skill

Every skill is a folder containing a required SKILL.md file and any optional resources its actual workflow needs:

skill-name/
|-- SKILL.md                 Required skill instructions
|   |-- YAML frontmatter     Required name and description
|   `-- Markdown body        Instructions loaded when the skill is used
|-- agents/                  Optional UI metadata and invocation policy
|   `-- openai.yaml
|-- scripts/                 Optional executable helpers
|-- references/              Optional documentation loaded as needed
`-- assets/                  Optional files used in generated output

Choose the structure that fits the actual task. Some skills are short and self-contained; others route among operating modes or delegate complex mechanics to scripts. Avoid creating directories, placeholders, examples, or ancillary documentation without a clear use.

SKILL.md

The YAML frontmatter identifies the skill and determines when it should be considered. Include the required name and description, and preserve supported optional fields such as existing metadata when appropriate.

The Markdown body is loaded only when the skill is used. Put the purpose, essential workflow, real constraints, and useful links there. Keep detailed procedures and examples in supporting references when they are relevant only to particular modes.

Skill information is disclosed in three stages:

1. Name and description: Available during skill selection, so keep them concise and discriminating.

2. SKILL.md body: Loaded when the skill applies, so keep its instructions relevant to that task.

3. Supporting resources: Read or execute only when the current task actually needs them.

The entrypoint should be as short as the task permits while retaining important constraints. A large upper bound is not a target: move conditional detail into references when doing so improves clarity or context use, rather than waiting for the file to become unwieldy.

Scripts

Use scripts/ for executable code when the same logic would otherwise be rewritten repeatedly or deterministic execution materially improves reliability.

• Example: scripts/rotate_pdf.py for a PDF operation that would otherwise require recreating the same code.
• Useful for: Repeated transformations, reliable API operations, data processing, and other concrete automation.
• Validation: Run new or changed scripts to verify their behavior. Scripts can usually be executed without loading their full implementation into context, although an agent may need to inspect them when patching or adapting them.

References

Use references/ for documentation that is needed only in particular contexts.

• Examples: references/schema.md for database tables, references/policies.md for domain rules, references/api_docs.md for an API, or separate writing guides for different deliverables.
• Useful for: Schemas, API documentation, company policies, format-specific procedures, detailed workflows, and substantial examples.
• Routing: Link each reference from SKILL.md or another relevant resource and explain when it should be read. Keep information in one place instead of duplicating it across the entrypoint and references.

Keep references focused on maintained, task-specific information that changes the agent's decisions. Avoid copied manuals, exhaustive catalogs, and generic tutorials already available from authoritative sources. Before removing existing resources, inspect their callers and purpose.

For large references, include useful search terms or a short contents section when that makes the needed material easier to find.

Assets

Use assets/ for files that belong in generated output rather than in the model's instructions.

• Examples: assets/logo.png, assets/slides.pptx, assets/font.ttf, or assets/frontend-template/.
• Useful for: Templates, images, fonts, icons, boilerplate projects, and other files copied or adapted into the result.
• Context: Do not load assets as instructions unless the task requires inspecting them.

UI Metadata and Invocation Policy

agents/openai.yaml can provide UI-facing metadata such as display_name, short_description, and default_prompt, along with invocation policy. When creating or updating those settings, read references/openai_yaml.md ↗ and keep the values consistent with the skill.

Automatic skill selection is allowed by default. Change that default only when the user explicitly requests an explicit-only skill:

policy:
  allow_implicit_invocation: false

This keeps the skill available when explicitly invoked as $skill-name without adding it to the model context automatically. Preserve unrelated existing UI, policy, and dependency fields when updating agents/openai.yaml.

The initializer creates this file automatically. For new or interface-only metadata, generate it with:

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

The generator replaces the entire file. If an existing file contains policy or dependencies, update only the intended fields in place instead of regenerating it.

Include optional interface fields only when the user provides or requests them.

What Not to Include

Include files that directly support the skill's work. Avoid adding a README.md, installation guide, changelog, duplicated quick reference, or other auxiliary documentation unless a specific task or packaging requirement calls for it.

Progressive Disclosure in Practice

For a skill with multiple substantial modes, keep the shared guidance and mode-selection criteria in SKILL.md. Link each supporting reference where its use becomes relevant. Do not load every reference by default, duplicate reference content in the entrypoint, or add a routing layer when there is nothing meaningful to route.

For example, a deployment skill can keep provider selection in SKILL.md and separate provider details:

cloud-deploy/
|-- SKILL.md
`-- references/
    |-- aws.md
    |-- gcp.md
    `-- azure.md

When the user chooses AWS, read references/aws.md; do not also load the GCP and Azure guides. The same pattern can separate business domains, deliverable types, or other genuinely distinct operating modes.

A short skill can instead route to details only when an advanced operation needs them:

## Documents

Handle ordinary edits directly.

- For tracked changes, read [references/redlining.md](references/redlining.md).
- For document internals, read [references/ooxml.md](references/ooxml.md).

These examples illustrate options, not a required structure. Choose the organization that makes the skill easier to use without loading irrelevant material.

Create or Update a Skill

Adapt the work to the request. Creating a complex new skill may involve understanding realistic use cases, choosing supporting resources, initializing files, writing instructions, and validating the result. A narrow update to an existing skill may require only a focused edit and validation.

Ask clarifying questions only when the missing information matters and cannot be reasonably inferred. Respect a user-specified location; otherwise create discoverable skills in $CODEX_HOME/skills, or ~/.codex/skills when CODEX_HOME is unset.

Keep automatic skill selection enabled unless the user explicitly requests an explicit-only skill. When the intended invocation mode is genuinely unclear and matters to the requested workflow, ask whether the user wants normal automatic discovery or explicit-only invocation; otherwise preserve the default. Do not infer explicit-only invocation from sensitive operations or required approvals: keep the skill discoverable and require authorization immediately before the actual mutation. Preserve an existing skill's invocation policy unless the user asks to change it.

For a new or substantially revised skill, consider the actual requests it should handle and which reusable resources would improve those tasks:

• A repeated PDF transformation may justify a scripts/rotate_pdf.py helper.
• An application-building workflow may benefit from an assets/frontend-template/ starter.
• A data-analysis skill may need a references/schema.md guide to avoid rediscovering table relationships.

Create those resources only when their concrete benefit justifies them. If the user has already explained the task clearly, proceed without requesting additional examples.

Naming

• Use lowercase letters, digits, and hyphens.
• Keep names under 64 characters and prefer short action-oriented names.
• Namespace by tool or domain when doing so improves discovery.
• Name the skill folder after the skill.

Initialize a New Skill

For a new skill, use the bundled initializer when it helps create the required files consistently:

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

For example:

scripts/init_skill.py my-skill --path "${CODEX_HOME:-$HOME/.codex}/skills"
scripts/init_skill.py my-skill --path "${CODEX_HOME:-$HOME/.codex}/skills" --resources references

Request only the resource directories the skill needs. Use --examples only when concrete placeholders would help, and replace or remove them before finishing. Do not initialize an existing skill again.

The initializer creates the skill directory, a concise SKILL.md starter, and agents/openai.yaml. It creates resource directories and example files only when requested. Pass generated UI values as --interface key=value when needed.

Write the Instructions

The frontmatter description should briefly explain what the skill does and when it applies. Include a meaningful boundary when similar requests should not activate the skill.

For example:

description: Create or edit Word documents when formatting, tracked changes, or comments require document-specific handling.

Put detailed workflows, tool choices, examples, and operating modes in the body or relevant references rather than listing them all in the description. Preserve supported optional frontmatter, such as existing metadata, when appropriate.

Write only the instructions needed for another Codex instance to perform the task well. State the desired outcome, non-obvious context, real constraints, and relevant references or tools. Preserve the user's explicit choices and existing authorization boundaries. Avoid prescribing a fixed structure, process, or number of steps when the task does not require one.

Validate and Iterate

Validate the completed skill with:

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

The validator checks frontmatter, naming, and unfinished scaffold placeholders; it does not prove that the skill makes good decisions. Also check that descriptions remain discriminating, instructions preserve user intent, references are discoverable, and any added scripts actually work.

When testing is warranted, verify observable behavior or meaningful invariants. Avoid tests that merely match generated wording, headings, or regex patterns.

Improve the skill based on real usage or demonstrated failures. Prefer a narrow correction to accumulating universal rules for every observed example.

Independent Forward-Testing

Use an independent subagent pass when a skill is sufficiently complex or risky that realistic behavioral validation would add meaningful confidence, and when delegation is available and authorized. Ordinary creation or small edits do not automatically require subagents.

Give the evaluating agent a realistic user request, the skill, and the minimum raw artifacts needed to perform the task. Do not provide the intended answer, suspected bug, proposed fix, or prior conclusions unless the evaluation genuinely requires them.

For example:

Use $skill-name at /path/to/skill-name to complete this realistic request.

Keep the evaluation scoped to permitted resources and side effects. Use an isolated temporary workspace for generated artifacts so they do not enter the working tree or contaminate later evaluations. Ask for approval when the proposed evaluation would require additional authorization, affect a live production system, or impose substantial time or cost. Review the actual outcome and artifacts, then make only changes supported by the observed behavior.