Eval

Community

jh941213

eval

코드 산출물을 4축(기능/품질/독창성/보안)으로 평가하고 점수 산출. Evaluator 에이전트를 스폰하여 독립 평가 실행. Triggers on: eval, 평가, 품질 점수, 코드 평가, quality score. NOT for: 코드 작성, 구현, 리뷰.

Overview

Publisher	jh941213
Repository	`my-cc-harness`
Skill name	`eval`
Stars	125
Forks	35
Bundled files	Instructions only
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by jh941213 on GitHub. Read the source before you install it.

Installation

Install the Eval AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills/eval .claude/skills/eval

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eval in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eval on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eval is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

코드 Eval (독립 평가)

Generator(구현자)와 분리된 Evaluator 에이전트를 스폰하여 산출물을 독립 평가합니다.

별도 모델로 평가 실행 방법: gemini CLI(설치되어 있으면) 또는 별도 새 세션에서 실행한다. 둘 다 불가하면 같은 세션이라도 생성 컨텍스트를 참조하지 않는 독립 서브에이전트로 실행한다.

실행 프로세스

Step 1: Evaluator 에이전트 스폰


Agent(subagent_type="evaluator",
  prompt="~/.claude/agents/evaluator.md를 읽고 현재 프로젝트를 평가하라.
         4축(기능 정확성/코드 품질/독창성/사용성&보안) 100점 만점.
         결과를 EVAL_REPORT.md에 저장.")

Step 2: 결과 확인

Evaluator가 완료되면 EVAL_REPORT.md를 읽고 사용자에게 요약 보고:


📊 Eval 결과: [PASS/CONDITIONAL/FAIL] — [N]/100점

기능 정확성: [N]/40 | 코드 품질: [N]/25
독창성: [N]/20 | 사용성&보안: [N]/15

[수정 필요 항목 요약]

Step 3: CONDITIONAL/FAIL 시

수정 필요 항목을 구체적으로 안내하고, 수정 후 재평가할지 질문. 재평가 시 동일 기준 적용 (최대 3라운드).

pass@k 멱등성 테스트 (선택)

동일 프롬프트로 k회 실행하여 품질 일관성을 측정:

bash
# k=3 실행 예시
for i in 1 2 3; do
  /eval 실행 → 점수 기록
done
# 3회 모두 85+ → 멱등성 확보
# 점수 분산 > 15점 → 불안정 (하네스 조정 필요)

수준의 멱등성: 정확히 같은 코드가 아니라 같은 품질 수준이 유지되는지 측정.

Frequently asked questions

What does the Eval AI skill do?

Why use Eval on TypingMind?

Because you install it once and use it with any model. Eval is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills/eval. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Eval?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eval?

As many as you like. As long as a model supports skills, you can use Eval with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eval AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

코드 Eval (독립 평가)

실행 프로세스

Step 1: Evaluator 에이전트 스폰

Step 2: 결과 확인

Step 3: CONDITIONAL/FAIL 시

pass@k 멱등성 테스트 (선택)

Frequently asked questions

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