codex-desktop-skills
Skill5 starsChanged yesterdayMITView on GitHub
Author Agent Skills for the ChatGPT desktop app (formerly the Codex app): SKILL.md frontmatter, trigger-rich descriptions, references/assets/scripts layout, agents/openai.yaml MCP tool dependencies, @skill-creator, and activation testing with direct, indirect, incomplete, negative and edge-case prompts. Use when the user wants to write, fix or test a skill that runs in the ChatGPT or Codex desktop app, including one bundled in a plugin.
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 51
Prefer scripts for deterministic work (parsing, validation) over prose.
- Distinctive: line 54
No secrets, no machine-specific absolute paths.
- Distinctive: line 58
Scope: write 5 to 10 real prompts the skill should handle first.
- Very specific: line 61
Add
agents/openai.yaml: only if the skill needs MCP tools: references/openai-yaml-and-testing.md. - Distinctive: line 70
Never embed credentials; reference environment variables or the MCP server's own auth.
- Distinctive: line 71
Do not rely on a skill to enforce security.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
ChatGPT / Codex desktop skills
A skill is a folder with a SKILL.md. The app reads only name and description up front and loads the body when the task matches, so the description decides whether the skill ever runs. Source (re-check): https://developers.openai.com/plugins/build/skills.md.
Layout
my-skill/
SKILL.md required: name + description, then instructions
references/ long docs loaded on demand
assets/ templates, sample files
scripts/ deterministic helpers
agents/openai.yaml optional: MCP tool dependenciesInside a plugin the folder is skills/<name>/ and is auto-discovered (codex-desktop-plugins).
Rules
name: lowercase kebab-case, matches the folder.description: what it does and when to use it, with the words users actually say. Include near-miss exclusions. One focused job per skill.- Body: imperative steps, short. Move long material to
references/and link it from the body so it loads only when needed. - Prefer scripts for deterministic work (parsing, validation) over prose.
- Tell the model what to do when inputs are missing or ambiguous (ask the user).
- State that explicit user instructions outrank skill guidance. For GPT-6 Astra, audit skills for conflicting or stale instructions, because it follows them literally.
- No secrets, no machine-specific absolute paths.
Workflow
- Scope: write 5 to 10 real prompts the skill should handle first.
- Scaffold with
@skill-creator(Work mode) or$skill-creator(Codex), or by hand. - Write the description last-but-carefully; then the body.
- Add
agents/openai.yamlonly if the skill needs MCP tools: references/openai-yaml-and-testing.md. - Test activation with direct, indirect, incomplete, negative and edge-case prompts (same reference).
- Iterate on the description first when triggering is wrong, then the body when behavior is wrong.
- Bundle in a plugin or share the folder (
codex-desktop-plugins).
Safety
- Skills carry instructions the model follows with the user's permissions; never include instructions to exfiltrate data, disable approvals or hide actions.
- Scripts in
scripts/run on the user's machine: keep them readable, non-destructive by default, and document side effects. - Never embed credentials; reference environment variables or the MCP server's own auth.
- Do not rely on a skill to enforce security; use approval modes and sandbox settings.
Verification checklist
namematches the folder; kebab-case- Description states what and when, with user phrasing and exclusions
- Body is concise; long content moved to
references/ - Every
references/file is linked from the body - Missing-input behavior defined
- User instructions outrank the skill (stated)
agents/openai.yamlvalid if present; URLs reachable- Positive, negative and edge prompts tested in the app
- No secrets or absolute local paths
Anti-patterns
- A description that only names the topic ("Notes helper") with no triggers.
- One mega-skill covering several jobs.
- Putting everything in
SKILL.mdinstead ofreferences/. - Contradictory rules ("always ask" and "never ask").
- Declaring MCP dependencies you do not use.
- Testing only prompts that name the skill.
Pointers
| Need | Read |
agents/openai.yaml and activation test matrix | references/openai-yaml-and-testing.md |
| Plugin packaging | codex-desktop-plugins |
| UI entrypoints | codex-desktop-extensions |
In this repository30 other files it gives its agents.
- docs/agents.mdAGENTS.md
- website/AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/claude-desktop-skills.mdcCursor rule
- .cursor/rules/claude-mod.mdcCursor rule
- .cursor/rules/codex-desktop-skills.mdcCursor rule
- .cursor/rules/desktop-and-publishing-skills.mdcCursor rule
- .cursor/rules/release.mdcCursor rule
- claude-code-mods-guideSkill
- claude-desktop-extensionsSkill
- claude-desktop-modsSkill
- claude-desktop-pluginsSkill
- codex-desktop-extensionsSkill
- codex-desktop-pluginsSkill
- codex-desktop-skillsSkill
- copilot-desktop-extensionsSkill
- cursor-extensions-and-pluginsSkill
- opencode-pluginsSkill
- publishing-mental-to-storesSkill
- claude-code-mods-guideSkill
- claude-desktop-extensionsSkill
- claude-desktop-modsSkill
- claude-desktop-pluginsSkill
- codex-desktop-extensionsSkill
- codex-desktop-pluginsSkill
- copilot-desktop-extensionsSkill
- cursor-extensions-and-pluginsSkill
- opencode-pluginsSkill
- publishing-mental-to-storesSkill
- mental-setupSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

