imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or…
Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources.
$ npx -y skills add openai/codex --skill skill-creator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/skill-creatorContext preview
The summary Claude sees to decide when to auto-load this skill.
Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources.
name: skill-creator description: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. metadata: short-description: Create or update a skill
Create skills that give Codex useful, non-obvious guidance without constraining unrelated work.
**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.
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.
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.
Use `scripts/` for executable code when the same logic would otherwise be rewritten repeatedly or deterministic execution materially improves reliability.
Use `references/` for documentation that is needed only in particular contexts.
Repo: openai/codex
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or…
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