Judger

Judger is a skill for Claude Code, Codex from OpenDCAI/Dataflow-LoopAI. It costs 0 tokens per session (6,568 once invoked), scanned A, original, Apache-2.0.

An evaluation workflow for measuring how well generated code, SQL, or general text answers perform. TDD here is not involved; the workflow runs code tests, executes SQL against SQLite, or uses a general text evaluator.

In plain words
What is it for?
Use it to calculate pass@k for coding tasks such as HumanEval or MBPP, check generated SQL by running it, and evaluate general text responses.
Why use it?
It turns model outputs into repeatable scores instead of relying on manual inspection. It also supports separate settings for evaluation models, batches, samples, and GPUs.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Codex.

Good fit Use it to calculate pass@k for coding tasks such as HumanEval or MBPP, check generated SQL by running it, and evaluate general text responses.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/opendcai/dataflow-loopai/judger
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OpenDCAI/Dataflow-LoopAI --skill judger
Clone the repo
git clone --depth 1 https://github.com/OpenDCAI/Dataflow-LoopAI

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for Judger

README.md
[![agentmods](https://agentmods.dev/badge/skills/opendcai/dataflow-loopai/judger/github.svg)](https://agentmods.dev/skills/opendcai/dataflow-loopai/judger)
Your own site
<a href="https://agentmods.dev/skills/opendcai/dataflow-loopai/judger"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-loopai/judger/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for Judger

Your own site · 80×15
<a href="https://agentmods.dev/skills/opendcai/dataflow-loopai/judger"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-loopai/judger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,568 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.06568
Opus 5.5 $0.00000 $0.02627
Sonnet 5.5 $0.00000 $0.01314
Haiku 4.5 $0.00000 $0.00657

Measured 16d ago against content hash 27d5dbf45b09, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

Judger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 16d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/Judger/SKILL.md · 398 lines

How it starts

The opening of the file, as written. The whole thing — 398 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Judger Skill

Purpose

无 LangGraph 的独立评测流水线。支持四种任务类型:

  • code — 代码生成评测(evalplus 的 HumanEval+ / MBPP+,或 LiveCodeBench),计算 pass@k
  • text2sql — SQL 生成评测,SQLite 执行校验
  • general_text — 通用文本评测(One-Eval DataFlowEvalTool)
  • math — 数学/AIME 评测(生成、答案提取和判分在 Docker 镜像内完成)

How to Invoke

唯一入口:loopai.skills.Judger.run()

DB_PATH 和 TASK_ID 从环境变量自动获取:

DB_PATH=api/db/db.sqlite3 TASK_ID=<task_id> \
python -c "from loopai.skills.Judger import run; run()"

或通过 CLI:

DB_PATH=api/db/db.sqlite3 TASK_ID=<task_id> loopai-judger

首次修改 setup.py 后需重新安装项目才会生成 loopai-configer / loopai-judger 命令;开发环境也可直接用 python -m loopai.skills.Configer.cli 和 python -m loopai.skills.Judger.cli。

Configuration

配置通过 Configer skill 写入 TaskModel.state,分两部分:

全局字段(state["judger"] 顶层,所有 bench 共享)

字段 默认值 说明
eval_model_path 无 模型路径(必填)
eval_temperature 0 采样温度,bench 可覆盖
eval_top_p 0.95 Top-P 采样,bench 可覆盖
eval_max_tokens 16384 最大输出 token 数(含思考推理),bench 可覆盖。vLLM 以 --generation-config vllm 启动,模型目录 generation_config.json 的 max_new_tokens 不会再把上限压低(如 Qwen3-8B-Base 的 2048)
eval_enable_thinking 不设置 思考模式开关(None 跟随模型默认 / True 开 / False 关),bench 可覆盖
eval_batch_size 10 宿主 generate 阶段每批并发多少条 prompt;只对 code 的 evalplus 分支和 text2sql 生效(LiveCodeBench 在容器里自己生成,不吃这个值),bench 可覆盖
eval_case_num 10 每问题样本数,bench 可覆盖
eval_vllm_tensor_parallel_size 1 vLLM 张量并行数
eval_vllm_gpu_memory_utilization 0.9 vLLM GPU 显存利用率
cuda_visible_devices "0" 指定 GPU
output_dir "./outputs" 输出根目录
eval_model_name 空 /v1/models 暴露的模型名;留空使用 eval_model_path
eval_top_k / eval_min_p -1 / 0 math 请求采样参数

Bench 配置(state["judger"])

所有评测集通过 benchlist 和 extra_benchlist 列表配置。格式必须是 JSON 数组([{...},{...}]),不是 JSONL(每行一个对象):

{
  "benchlist": [
    {
      "name": "gsm8k",
      "task_type": "general_text",
      "problem_path": "/data/gsm8k/test.jsonl",
      "eval_type": "key2_qa",
      "key_mapping": {}
    },
    {
      "name": "human_eval",
      "task_type": "code",
      "problem_path": "data/evalplus/humaneval_plus.jsonl",
      "case_num": 10,
      "batch_size": 10,
      "format_type": "humaneval+"
    },
    {
      "name": "livecodebench_codegen",
      "task_type": "code",
      "problem_path": "data/livecodebench/test.jsonl",
      "format_type": "livecodebench",
      "lcb_scenario": "codegeneration",
      "case_num": 1
    },
    {
      "name": "bird_dev",
      "task_type": "text2sql",
      "problem_path": "/data/bird/dev.jsonl",
      "text2sql_dir": "/data/bird/dev_databases",
      "case_num": 10,
      "batch_size": 10
    },
    {
      "name": "aime26",
      "task_type": "math",
      "problem_path": "/data/aime26_test.jsonl",
      "case_num": 2
    }
  ],
  "extra_benchlist": []
}

Read the full file on GitHub · 398 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 16d ago Changed · +212 lines 27d5dbf45b09
  2. 1mo ago First seen · 186 lines · 0 tokens per session scan A b5c4d624ecae

Subscribe to this mod's changes

Judger is a skill published in the GitHub repository OpenDCAI/Dataflow-LoopAI (29 stars, last pushed 16d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 6,568 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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