LLM Judge Validation
ai-evals-course/evals-skills
Checks an LLM judge against human labels using train, dev and test splits, TPR and TNR, and a bias correction applied to production data.
Build custom LLM evaluation pipelines using the OpenJudge framework.
Install 01 Graders And Pipeline
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipeline --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .claude/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .claude/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipelineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipeline --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .agents/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .agents/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipeline --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .cursor/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .cursor/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentscope-ai/OpenJudge.git --path skills/openjudge-core/01-graders-and-pipeline--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipeline --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .gemini/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .gemini/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipelineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .github/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .github/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentscope-ai/OpenJudge 01-graders-and-pipeline --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/OpenJudge.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/openjudge-core/01-graders-and-pipeline .opencode/skills/01-graders-and-pipeline && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "01-graders-and-pipeline" agent skill from https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge-core/01-graders-and-pipeline into .opencode/skills/01-graders-and-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "01-graders-and-pipeline", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
01-graders-and-pipeline- GitHub stars
- 871
- Token cost
- ~1.3k tokens
- SKILL.md length
- 200 words
- Files
- 5
- Skills in repo
- 19
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Build custom LLM evaluation pipelines using the OpenJudge framework.
- The user wants to evaluate LLM outputs
- SKILL.md covers When to Use This Skill, Sub-documents — Read When…, Install and Architecture Overview, plus 4 more sections
- Calls pip; reaches dashscope.aliyuncs.com
- Compare multiple models
What it does
01 Graders And Pipeline is an agent skill from agentscope-ai/OpenJudge. Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated…
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `analyzer.md`, `generator.md` and `graders.md`).
It sits in AI & LLM Engineering, covering LLM evaluation and Statistics. The repository describes itself as: OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards. The licence is Apache-2.0.
When your agent uses it
- The user wants to evaluate LLM outputs
- Compare multiple models
- Design scoring criteria
- Build an automated evaluation system
Example prompts
- “/01-graders-and-pipeline”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit d1e0642. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
dashscope.aliyuncs.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
01 Graders And Pipeline loads about 1.3k tokens when it runs. Until then it costs about 138 tokens; SKILL.md has 200 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from agentscope-ai/OpenJudge at commit d1e0642, republished under its Apache-2.0 licence (© agentscope-ai). 200 words, ~1,296 tokens.
Download SKILL.mdSave it as
.claude/skills/01-graders-and-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- 01-graders-and-pipeline
- description
- Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
OpenJudge Skill
Build evaluation pipelines for LLM applications using the openjudge library.
When to Use This Skill
- User wants to evaluate LLM output quality (correctness, relevance, hallucination, etc.)
- User wants to compare two or more models and rank them
- User wants to design a scoring rubric and automate evaluation
- User wants to analyze evaluation results statistically
- User wants to build a reward model or quality filter
Sub-documents — Read When Relevant
| Topic | File | Read when… |
|---|---|---|
| Grader selection & configuration | graders.md | User needs to pick or configure an evaluator |
| Batch evaluation pipeline | pipeline.md | User needs to run evaluation over a dataset |
| Auto-generate graders from data | generator.md | No rubric yet; generate from labeled examples |
| Analyze & compare results | analyzer.md | User wants win rates, statistics, or metrics |
Read the relevant sub-document before writing any code.
Install
pip install py-openjudgeArchitecture Overview
Dataset (List[dict])
│
▼
GradingRunner ← orchestrates everything
│
├─► Grader A ──► EvaluationStrategy ──► _aevaluate() ──► GraderScore / GraderRank
├─► Grader B ──► EvaluationStrategy ──► _aevaluate() ──► GraderScore / GraderRank
└─► Grader C ...
│
├─► Aggregator (optional) ← combine multiple grader scores into one
│
└─► RunnerResult ← {grader_name: [GraderScore, ...]}
│
▼
Analyzer ← statistics, win rates, validation metrics5-Minute Quick Start
Evaluate responses for correctness using a built-in grader:
import asyncio
from openjudge.models.openai_chat_model import OpenAIChatModel
from openjudge.graders.common.correctness import CorrectnessGrader
from openjudge.runner.grading_runner import GradingRunner
# 1. Configure the judge model (OpenAI-compatible endpoint)
model = OpenAIChatModel(
model="qwen-plus",
api_key="sk-xxx",
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
# 2. Instantiate a grader
grader = CorrectnessGrader(model=model)
# 3. Prepare dataset
dataset = [
{
"query": "What is the capital of France?",
"response": "Paris is the capital of France.",
"reference_response": "Paris.",
},
{
"query": "What is 2 + 2?",
"response": "The answer is five.",
"reference_response": "4.",
},
]
# 4. Run evaluation
async def main():
runner = GradingRunner(
grader_configs={"correctness": grader},
max_concurrency=8,
)
results = await runner.arun(dataset)
for i, result in enumerate(results["correctness"]):
print(f"[{i}] score={result.score} reason={result.reason}")
asyncio.run(main())Expected output:
[0] score=5 reason=The response accurately states Paris as capital...
[1] score=1 reason=The response gives the wrong answer (five vs 4)...Key Data Types
| Type | Description |
|---|---|
GraderScore | Pointwise result: .score (float), .reason (str), .metadata (dict) |
GraderRank | Listwise result: .rank (List[int]), .reason (str), .metadata (dict) |
GraderError | Error during evaluation: .error (str), .reason (str) |
RunnerResult | Dict[str, List[GraderResult]] — keyed by grader name |
Result Handling Pattern
from openjudge.graders.schema import GraderScore, GraderRank, GraderError
for grader_name, grader_results in results.items():
for i, result in enumerate(grader_results):
if isinstance(result, GraderScore):
print(f"{grader_name}[{i}]: score={result.score}")
elif isinstance(result, GraderRank):
print(f"{grader_name}[{i}]: rank={result.rank}")
elif isinstance(result, GraderError):
print(f"{grader_name}[{i}]: ERROR — {result.error}")Model Configuration
All LLM-based graders accept either a BaseChatModel instance or a dict config:
# Option A: instance
from openjudge.models.openai_chat_model import OpenAIChatModel
model = OpenAIChatModel(model="gpt-4o", api_key="sk-...")
# Option B: dict (auto-creates OpenAIChatModel)
model_cfg = {"model": "gpt-4o", "api_key": "sk-..."}
grader = CorrectnessGrader(model=model_cfg)
# OpenAI-compatible endpoints (DashScope / local / etc.)
model = OpenAIChatModel(
model="qwen-plus",
api_key="sk-xxx",
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)© agentscope-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files in skills/openjudge-core/01-graders-and-pipeline of agentscope-ai/OpenJudge.
- SKILL.md
- analyzer.md
- generator.md
- graders.md
- pipeline.md
Open the folder on GitHubat commit d1e0642
Compare with similar skills
01 Graders And Pipeline next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| 01 Graders And Pipeline this skillagentscope-ai/OpenJudge | 871 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| LLM Judge Validationai-evals-course/evals-skills | 1.5k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Looperksimback/looper | 710 | — | ~2.7k | Automated safety check: Notes | MIT | |
| Agent Eval Engineeringlangchain-ai/langchain-skills | 1.3k | — | ~4k | Automated safety check: Pass | MIT |
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- Official
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huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
Looper
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Scaffold a well-designed agent loop with best-practice coaching and a cross-model review council.
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Agent Eval Engineering
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Builds agent evaluations in stages: inspect the repository and traces, agree a Task Spec with you, then build, audit and run a Harbor task with an independent verifier.
Quality Flywheel
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A skill your agent uses when the user has changed a prompt (system prompt, RAG template, agent instruction, etc.) and wants to know whether the candidate is better or worse than the baseline.
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A skill your agent uses when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues.
Claude Authenticity
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Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project.
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Related
Categories
Questions about 01 Graders And Pipeline
What does 01 Graders And Pipeline do?
Build custom LLM evaluation pipelines using the OpenJudge framework. 01 Graders And Pipeline is an agent skill from agentscope-ai/OpenJudge. Build custom LLM evaluation pipelines using the OpenJudge framework.
When should I use 01 Graders And Pipeline?
01 Graders And Pipeline fits situations like: the user wants to evaluate LLM outputs; compare multiple models; design scoring criteria; build an automated evaluation system.
How do I install 01 Graders And Pipeline in Claude Code?
Run `npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a claude-code`. Or copy the skill folder (skills/openjudge-core/01-graders-and-pipeline in agentscope-ai/OpenJudge) into .claude/skills/01-graders-and-pipeline in your project. Claude Code loads it when a task matches its description.
How do I install 01 Graders And Pipeline in Codex?
Run `npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a codex`. Or copy the skill folder (skills/openjudge-core/01-graders-and-pipeline in agentscope-ai/OpenJudge) into .agents/skills/01-graders-and-pipeline in your project. Codex loads it when a task matches its description.
Can I use 01 Graders And Pipeline in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentscope-ai/OpenJudge --skill 01-graders-and-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/01-graders-and-pipeline, .gemini/skills/01-graders-and-pipeline, .github/skills/01-graders-and-pipeline and .opencode/skills/01-graders-and-pipeline in your project.
What does 01 Graders And Pipeline need to run?
Going by SKILL.md and its folder, 01 Graders And Pipeline needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does 01 Graders And Pipeline access the network?
SKILL.md names 1 domain. In commands or code: dashscope.aliyuncs.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is 01 Graders And Pipeline safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does 01 Graders And Pipeline use?
01 Graders And Pipeline is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does 01 Graders And Pipeline use?
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to 01 Graders And Pipeline?
Skills that share tags, products or a category with 01 Graders And Pipeline: LLM Judge Validation (ai-evals-course/evals-skills, 1.5k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars) and Looper (ksimback/looper, 710 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains 01 Graders And Pipeline?
agentscope-ai (a GitHub organization) maintains it in agentscope-ai/OpenJudge, which has 871 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on September 11, 2026.
Source: agentscope-ai/OpenJudge on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.