How do I install the Openai Plugin Eval Evaluate Skill skill?

Run npx skillmds add yanacuti1121/openai-plugin-eval-evaluate-skill in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Openai Plugin Eval Evaluate Skill skill do?

plugin-eval — evaluate-skill It is listed under Coding & Dev Tools on SkillMD.

Is Openai Plugin Eval Evaluate Skill safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Openai Plugin Eval Evaluate Skill?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Openai Plugin Eval Evaluate Skill free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Openai Plugin Eval Evaluate Skill?

yanacuti1121 (@yanacuti1121) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Openai Plugin Eval Evaluate Skill by yanacuti1121

Name: Openai Plugin Eval Evaluate Skill
Rating: 4.5 (4 reviews)
Author: yanacuti1121

File contents

Evaluate Skill

Use this skill when the target is a local skill directory or SKILL.md file.

Workflow

Treat "Evaluate this skill." as the default entrypoint.
If the user names a skill instead of giving a path, resolve it locally first, preferring ~/.codex/skills/<skill-name> and then repo-local skills/<skill-name>.
If the user says the request in natural language first, use plugin-eval start <skill-path> --request "<user request>" --format markdown to show the routed path clearly.
Run plugin-eval analyze <skill-path> --format markdown.
Review At a Glance, Why It Matters, Fix First, and Recommended Next Step before drilling into details.
Explain which findings are structural, which are budget-related, and which are code-related.
If the user asks for an "analysis" of the skill, do not stop at the report. Also run plugin-eval init-benchmark <skill-path> and show the setup questions for refining the starter scenarios in .plugin-eval/benchmark.json.
If the user wants real usage numbers, switch to "Measure the real token usage of this skill." and run the benchmark flow.
After observed usage is available, use plugin-eval measurement-plan <skill-path> --observed-usage <usage.jsonl> --format markdown to recommend what to instrument or improve next.
If the user wants a rewrite plan, route to ../improve-skill/SKILL.md.

Skill-Specific Priorities

frontmatter validity
name and description quality
progressive disclosure and reference usage
broken relative links
oversized SKILL.md or descriptions
helper script quality for TypeScript and Python files

Chat Requests To Recognize

Evaluate this skill.
Give me an analysis of the game dev skill.
Audit this skill.
Why did this skill score that way?
What should I fix first?
Measure the real token usage of this skill.

Commands

plugin-eval start <skill-path> --request "Evaluate this skill." --format markdown
plugin-eval analyze <skill-path> --format markdown
plugin-eval explain-budget <skill-path> --format markdown
plugin-eval measurement-plan <skill-path> --format markdown
plugin-eval init-benchmark <skill-path>
plugin-eval benchmark <skill-path> --dry-run

Reference

../../references/chat-first-workflows.md

---name: openai-plugin-eval-evaluate-skilldescription: plugin-eval — evaluate-skilllicense: MIT--- # Evaluate Skill Use this skill when the target is a local skill directory or `SKILL.md` file. ## Workflow 1. Treat "Evaluate this skill." as the default entrypoint. 2. If the user names a skill instead of giving a path, resolve it locally first, preferring `~/.codex/skills/<skill-name>` and then repo-local `skills/<skill-name>`. 3. If the user says the request in natural language first, use `plugin-eval start <skill-path> --request "<user request>" --format markdown` to show the routed path clearly. 4. Run `plugin-eval analyze <skill-path> --format markdown`. 5. Review `At a Glance`, `Why It Matters`, `Fix First`, and `Recommended Next Step` before drilling into details. 6. Explain which findings are structural, which are budget-related, and which are code-related. 7. If the user asks for an "analysis" of the skill, do not stop at the report. Also run `plugin-eval init-benchmark <skill-path>` and show the setup questions for refining the starter scenarios in `.plugin-eval/benchmark.json`. 8. If the user wants real usage numbers, switch to "Measure the real token usage of this skill." and run the benchmark flow. 9. After observed usage is available, use `plugin-eval measurement-plan <skill-path> --observed-usage <usage.jsonl> --format markdown` to recommend what to instrument or improve next. 10. If the user wants a rewrite plan, route to `../improve-skill/SKILL.md`. ## Skill-Specific Priorities - frontmatter validity - `name` and `description` quality - progressive disclosure and reference usage - broken relative links - oversized `SKILL.md` or descriptions - helper script quality for TypeScript and Python files ## Chat Requests To Recognize - `Evaluate this skill.` - `Give me an analysis of the game dev skill.` - `Audit this skill.` - `Why did this skill score that way?` - `What should I fix first?` - `Measure the real token usage of this skill.` ## Commands ```bash plugin-eval start <skill-path> --request "Evaluate this skill." --format markdown plugin-eval analyze <skill-path> --format markdown plugin-eval explain-budget <skill-path> --format markdown plugin-eval measurement-plan <skill-path> --format markdown plugin-eval init-benchmark <skill-path> plugin-eval benchmark <skill-path> --dry-run ``` ## Reference - `../../references/chat-first-workflows.md`

Frequently asked questions