What does this skill do, and when should you use it?
Skill Creator helps developers create new skills and update existing ones. It explains the required SKILL.md structure, frontmatter, instruction body, and the roles of optional scripts, references, and assets. The guide emphasizes concise context, progressive disclosure, appropriate freedom, and validation integrity. It covers the workflow from understanding concrete use cases through initialization, editing, validation, and iteration.
Uses requirements and concrete examples to plan reusable scripts, references, and assets; describes how to create a skill directory with SKILL.md; explains how to write the name and description frontmatter and the instruction body; recommends running init_skill.py for initialization and quick_validate.py for validation; explains how to keep agents/openai.yaml aligned with SKILL.md and how to forward-test skills on realistic tasks.
- A developer creating a domain-specific Agent Skill can use it to plan the folder structure, resources, and trigger description.
- A team extending an existing workflow can use it to decide what belongs in SKILL.md, references, or scripts.
- A maintainer checking metadata and naming conventions can use the initialization and validation workflow.
- A skill author optimizing context usage can use its progressive-disclosure patterns to split large reference material.
- A developer evaluating whether a skill generalizes can use its forward-testing and iteration process.
How do you install this skill?
- This is a static review; no scripts or validation commands were executed, so documented procedures should not be treated as proven runtime results.
- The initializer creates directories and files; confirm the destination, permissions, and retention or cleanup expectations before use.
- Add verification that referenced icon assets exist, plus version and maintenance information, abnormal-input tests, and guidance on sensitive data and external side effects.
- Shell / CLI
- Local filesystem
Python
The source does not document a standalone installation procedure for skill-creator. It is located at codex-rs/skills/src/assets/samples/skill-creator/SKILL.md in the openai/codex repository. The README documents Codex CLI installation via curl -fsSL https://chatgpt.com/codex/install.sh | sh on macOS or Linux, powershell -ExecutionPolicy ByPass -c "irm https://chatgpt.com/codex/install.ps1 | iex" on Windows, or npm install -g @openai/codex and brew install --cask codex.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/openai/codex.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/codex-rs/skills/src/assets/samples/skill-creator" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Create a new PDF-processing skill and plan its scripts, references, and assets.
In Codex, make a request such as: “Create a new PDF-processing skill and plan its scripts, references, and assets.” For initialization, the guide provides scripts/init_skill.py <skill-name> --path <output-directory> [--resources scripts,references,assets] [--examples]. After editing, run scripts/quick_validate.py <path/to/skill-folder>.
What are this skill's strengths and limitations?
- Covers the full lifecycle of skill creation, maintenance, validation, and iteration.
- Clearly separates the roles of SKILL.md, scripts, references, and assets.
- Provides concrete rules for naming, frontmatter, context management, and progressive disclosure.
- Includes copyable initialization and validation commands.
- The guidance is oriented toward Codex skill packaging and workflows, so other platforms may require adaptation.
- The source provides no standalone installation instructions or test results for this skill.
- It recommends forward-testing but does not include a ready-made test suite.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Codex Sample Skill Creator Guide this page ✓ OpenAI · Official | 46 · Use with care | ★ 129k | 11d ago | Apache-2.0 |
| Skill Builder Guide ✓ Anthropic · Official | 52 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
| OpenAI Skill Creator Guide ✓ OpenAI · Official | 55 · Use with care | ★ 28k | 4mo ago | — |
| Claude Code Plugin Skill Development ✓ Anthropic · Official | 51 · Use with care | ★ 38k | 11d ago | Apache-2.0 |
| Workflow Skill Builder ✓ Microsoft · Official | 50 · Use with care | ★ 194k | 11d ago | MIT |
How did FollowSkills review this skill?
The material asks for the destination before initialization, uses parameterized scripts, and provides structural validation. It does not systematically specify permission boundaries, sensitive-data handling, network or external effects, user confirmation, rollback, or dependency security, so points are deducted.
SKILL.md, the initializer, YAML generator, and validator form a generally coherent workflow with basic error handling. Static review cannot establish that the scripts run successfully, and committed tests for key paths, abnormal inputs, and cross-environment behavior are absent; the score is therefore conservatively limited.
The trigger is clear for creating or updating Codex skills, with guidance on workflows, resources, naming, and validation. Boundaries for non-fit cases, input/output contracts, semantic false-trigger conditions, Chinese-language use, and mainland-China network fit are not sufficiently stated, so points are deducted.
The documentation has clear information architecture, progressive disclosure, resource categorization, naming rules, and direct reference guidance. It lacks explicit versioning, changelog, maintenance ownership, and update path; openai.yaml also references icon assets whose presence is not established by the supplied evidence.
The skill directly provides a creation workflow, templates, and three helper scripts, so it plausibly completes the core task. Users must still fill in documentation and perform their own testing, and no real task artifacts or comparative-benefit evidence are supplied; static calibration limits this score to 6.
The source code, validator, and workflow instructions are auditable, making some claims traceable. There is no third-party execution evidence, CI coverage for the skill’s key paths, or independent reproduction, so corroboration remains thin and points are deducted.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →