Agent skill

Batch Submit Experiment

by deepmodeling in deepmodeling/Uni-Lab-OS

Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate nodeparams from registry schemas, submit multiple rounds, check notebook status.

GPL-3.0Auto-check passed

Install Batch Submit Experiment

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .claude/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .claude/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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

$skill-installer install https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment

Type 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.

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .agents/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .agents/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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.

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .cursor/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .cursor/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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

$ gemini skills install https://github.com/deepmodeling/Uni-Lab-OS.git --path .cursor/skills/batch-submit-experiment

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .gemini/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .gemini/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment

Installs 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).

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .github/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .github/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .github/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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.

skills CLI

$ npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install deepmodeling/Uni-Lab-OS batch-submit-experiment --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/deepmodeling/Uni-Lab-OS.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.cursor/skills/batch-submit-experiment .opencode/skills/batch-submit-experiment && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "batch-submit-experiment" agent skill from https://github.com/deepmodeling/Uni-Lab-OS/tree/main/.cursor/skills/batch-submit-experiment into .opencode/skills/batch-submit-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "batch-submit-experiment", 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: batch-submit-experiment
GitHub stars: 178
Token cost: ~2.3k tokens
SKILL.md length: 398 words
Files: 2 (incl. scripts)
Skills in repo: 10
Repo updated: Sep 24, 2026
First seen: Oct 7, 2026
Licence: GPL-3.0

At a glance

Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate nodeparams from registry schemas, submit multiple rounds, check notebook status.

Works in 10 steps: ak / sk → AUTH → addr → BASE URL → req_device_registry_upload.json（设备注册表） → …
The user wants to submit experiments
SKILL.md covers 前置条件（缺一不可）, Session State, 请求约定 and API Endpoints, plus 4 more sections
Runs Python scripts from its folder; calls curl and python; reaches leap-lab.test.bohrium.com and leap-lab.uat.bohrium.com

What it does

Batch Submit Experiment is an agent skill from deepmodeling/Uni-Lab-OS. Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate nodeparams from registry schemas, submit multiple rounds, check notebook status. Use when the user wants to submit experiments, create notebooks, batch run workflows, check experiment status, or mentions 提交实验/批量实验/notebook/实验轮次/实验状态.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/gen_notebook_params.py`).

It works with PowerShell. The repository describes itself as: A Platform for Laboratory Automation. The licence is GPL-3.0.

When your agent uses it

The user wants to submit experiments
Create notebooks
Batch run workflows
Check experiment status

Example prompts

“/batch-submit-experiment”

Requirements

Python 3

Workflow steps

10 steps, taken from the step headings in SKILL.md.

ak / sk → AUTH
addr → BASE URL
req_device_registry_upload.json（设备注册表）
workflow_uuid（目标工作流）
获取实验室信息（自动获取 lab_uuid）
列出实验室项目（让用户选择项目）
列出可用 workflow
获取 workflow 模板详情
提交实验（创建 notebook）
查询 notebook 状态

What it can do on your machine

Read from SKILL.md and the folder at commit 43923ec. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- curl
- python
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:
- leap-lab.test.bohrium.com
- leap-lab.uat.bohrium.com
- leap-lab.bohrium.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

Batch Submit Experiment loads about 2.3k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 398 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~91
When it runs · the whole SKILL.md, loaded when a task matches: ~2.3k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from deepmodeling/Uni-Lab-OS at commit 43923ec, republished under its GPL-3.0 licence (© deepmodeling). 398 words, ~2,295 tokens.

Download SKILL.mdSave it as .claude/skills/batch-submit-experiment/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: batch-submit-experiment
description: Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate node_params from registry schemas, submit multiple rounds, check notebook status. Use when the user wants to submit experiments, create notebooks, batch run workflows, check experiment status, or mentions 提交实验/批量实验/notebook/实验轮次/实验状态.

Uni-Lab 批量提交实验指南

通过 Uni-Lab 云端 API 批量提交实验（notebook），支持多轮实验参数配置。根据 workflow 模板详情和本地设备注册表自动生成 node_params 模板。

重要：本指南中的 Authorization: Lab <token> 是 Uni-Lab 平台专用的认证方式，Lab 是 Uni-Lab 的 auth scheme 关键字，不是 HTTP Basic 认证。请勿将其替换为 Basic。

前置条件（缺一不可）

使用本指南前，必须先确认以下信息。如果缺少任何一项，立即向用户询问并终止，等补齐后再继续。

1. ak / sk → AUTH

询问用户的启动参数，从 --ak --sk 或 config.py 中获取。

生成 AUTH token（任选一种方式）：

bash

# 方式一：Python 一行生成（注意：scheme 是 "Lab" 不是 "Basic"）
python -c "import base64,sys; print('Authorization: Lab ' + base64.b64encode(f'{sys.argv[1]}:{sys.argv[2]}'.encode()).decode())" <ak> <sk>

# 方式二：手动计算
# base64(ak:sk) → Authorization: Lab <token>
# ⚠️ 这里的 "Lab" 是 Uni-Lab 平台的 auth scheme，绝对不能用 "Basic" 替代

2. --addr → BASE URL

`--addr` 值	BASE
`test`	`https://leap-lab.test.bohrium.com`
`uat`	`https://leap-lab.uat.bohrium.com`
`local`	`http://127.0.0.1:48197`
不传（默认）	`https://leap-lab.bohrium.com`

确认后设置：

bash

BASE="<根据 addr 确定的 URL>"
# ⚠️ Auth scheme 必须是 "Lab"（Uni-Lab 专用），不是 "Basic"
AUTH="Authorization: Lab <上面命令输出的 token>"

3. req_device_registry_upload.json（设备注册表）

批量提交实验时需要本地注册表来解析 workflow 节点的参数 schema。

必须先用 Glob 工具搜索文件，不要直接猜测路径：

Glob: **/req_device_registry_upload.json

常见位置（仅供参考，以 Glob 实际结果为准）：

<workspace>/unilabos_data/req_device_registry_upload.json
<workspace>/req_device_registry_upload.json

找到后检查文件修改时间并告知用户。超过 1 天提醒用户是否需要重新启动 unilab。

如果 Glob 搜索无结果 → 告知用户先运行 unilab 启动命令，等注册表生成后再执行。可跳过此步，但将无法自动生成参数模板，需要用户手动填写 param。

4. workflow_uuid（目标工作流）

用户需要提供要提交的 workflow UUID。如果用户不确定，通过 API #3 列出可用 workflow 供选择。

四项全部就绪后才可开始。

Session State

在整个对话过程中，agent 需要记住以下状态，避免重复询问用户：

lab_uuid — 实验室 UUID（首次通过 API #1 自动获取，不需要问用户）
project_uuid — 项目 UUID（通过 API #2 列出项目列表，让用户选择）
workflow_uuid — 工作流 UUID（用户提供或从列表选择）
workflow_nodes — workflow 中各 action 节点的 uuid、设备 ID、动作名（从 API #4 获取）

请求约定

所有请求使用 curl -s，POST 需加 Content-Type: application/json。

Windows 平台必须使用 curl.exe（而非 PowerShell 的 curl 别名），示例中的 curl 均指 curl.exe。

PowerShell JSON 传参：PowerShell 中 -d '{"key":"value"}' 会因引号转义失败。请将 JSON 写入临时文件，用 -d '@tmp_body.json'（单引号包裹 @，否则会被解析为 splatting 运算符）。

API Endpoints

1. 获取实验室信息（自动获取 lab_uuid）

bash

curl -s -X GET "$BASE/api/v1/edge/lab/info" -H "$AUTH"

json

{ "code": 0, "data": { "uuid": "xxx", "name": "实验室名称" } }

记住 data.uuid 为 lab_uuid。

2. 列出实验室项目（让用户选择项目）

bash

curl -s -X GET "$BASE/api/v1/lab/project/list?lab_uuid=$lab_uuid" -H "$AUTH"

json

{
  "code": 0,
  "data": {
    "items": [
      {
        "uuid": "1b3f249a-...",
        "name": "bt",
        "description": null,
        "status": "active",
        "created_at": "2026-04-09T14:31:28+08:00"
      },
      {
        "uuid": "b6366243-...",
        "name": "default",
        "description": "默认项目",
        "status": "active",
        "created_at": "2026-03-26T11:13:36+08:00"
      }
    ]
  }
}

展示 data.items[] 中每个项目的 name 和 uuid，让用户选择。用户必须选择一个项目，记住 project_uuid（即选中项目的 uuid），后续创建 notebook 时需要提供。

3. 列出可用 workflow

bash

curl -s -X GET "$BASE/api/v1/lab/workflow/workflows?page=1&page_size=20&lab_uuid=$lab_uuid" -H "$AUTH"

返回 workflow 列表，展示给用户选择。列出每个 workflow 的 uuid 和 name。

4. 获取 workflow 模板详情

bash

curl -s -X GET "$BASE/api/v1/lab/workflow/template/detail/$workflow_uuid" -H "$AUTH"

返回 workflow 的完整结构，包含所有 action 节点信息。需要从响应中提取：

每个 action 节点的 node_uuid
每个节点对应的设备 ID（resource_template_name）
每个节点的动作名（node_template_name）
每个节点的现有参数（param）

注意：此 API 返回格式可能因版本不同而有差异。首次调用时，先打印完整响应分析结构，再提取节点信息。常见的节点字段路径为 data.nodes[] 或 data.workflow_nodes[]。

5. 提交实验（创建 notebook）

bash

curl -s -X POST "$BASE/api/v1/lab/notebook" \
  -H "$AUTH" -H "Content-Type: application/json" \
  -d '<request_body>'

请求体结构：

json

{
  "lab_uuid": "<lab_uuid>",
  "project_uuid": "<project_uuid>",
  "workflow_uuid": "<workflow_uuid>",
  "name": "<实验名称>",
  "node_params": [
    {
      "sample_uuids": ["<样品UUID1>", "<样品UUID2>"],
      "datas": [
        {
          "node_uuid": "<workflow中的节点UUID>",
          "param": {},
          "sample_params": [
            {
              "container_uuid": "<容器UUID>",
              "sample_value": {
                "liquid_names": "<液体名称>",
                "volumes": 1000
              }
            }
          ]
        }
      ]
    }
  ]
}

注意：sample_uuids 必须是 UUID 数组（[]uuid.UUID），不是字符串。无样品时传空数组 []。

6. 查询 notebook 状态

提交成功后，使用返回的 notebook UUID 查询执行状态：

bash

curl -s -X GET "$BASE/api/v1/lab/notebook/status?uuid=$notebook_uuid" -H "$AUTH"

提交后应立即查询一次状态，确认 notebook 已被正确接收并开始调度。

Notebook 请求体详解

Show full SKILL.md (168 more words)Show less

node_params 结构

node_params 是一个数组，每个元素代表一轮实验：

要跑 2 轮 → node_params 有 2 个元素
要跑 N 轮 → node_params 有 N 个元素

每轮的字段

字段	类型	说明
`sample_uuids`	array<uuid>	该轮实验的样品 UUID 数组，无样品时传 `[]`
`datas`	array	该轮中每个 workflow 节点的参数配置

datas 中每个节点

字段	类型	说明
`node_uuid`	string	workflow 模板中的节点 UUID（从 API #4 获取）
`param`	object	动作参数（根据本地注册表 schema 填写）
`sample_params`	array	样品相关参数（液体名、体积等）

sample_params 中每条

字段	类型	说明
`container_uuid`	string	容器 UUID
`sample_value`	object	样品值，如 `{"liquid_names": "水", "volumes": 1000}`

从本地注册表生成 param 模板

自动方式 — 运行脚本

bash

python scripts/gen_notebook_params.py \
  --auth <token> \
  --base <BASE_URL> \
  --workflow-uuid <workflow_uuid> \
  [--registry <path/to/req_device_registry_upload.json>] \
  [--rounds <轮次数>] \
  [--output <输出文件路径>]

脚本位于本文档同级目录下的 scripts/gen_notebook_params.py。

脚本会：

调用 workflow detail API 获取所有 action 节点
读取本地注册表，为每个节点查找对应的 action schema
生成 notebook_template.json，包含：
- 完整 node_params 骨架
- 每个节点的 param 字段及类型说明
- _schema_info 辅助信息（不提交，仅供参考）

手动方式

如果脚本不可用或注册表不存在：

调用 API #4 获取 workflow 详情
找到每个 action 节点的 node_uuid

在本地注册表中查找对应设备的 action_value_mappings：

resources[].id == <device_id>
→ resources[].class.action_value_mappings.<action_name>.schema.properties.goal.properties

将 schema 中的 properties 作为 param 的字段模板
按轮次复制 node_params 元素，让用户填写每轮的具体值

注册表结构参考

json

{
  "resources": [
    {
      "id": "liquid_handler.prcxi",
      "class": {
        "module": "unilabos.devices.xxx:ClassName",
        "action_value_mappings": {
          "transfer_liquid": {
            "type": "LiquidHandlerTransfer",
            "schema": {
              "properties": {
                "goal": {
                  "properties": {
                    "asp_vols": {
                      "type": "array",
                      "items": { "type": "number" }
                    },
                    "sources": { "type": "array" }
                  },
                  "required": ["asp_vols", "sources"]
                }
              }
            },
            "goal_default": {}
          }
        }
      }
    }
  ]
}

param 填写时，使用 goal.properties 中的字段名和类型。

完整工作流 Checklist

Task Progress:
- [ ] Step 1: 确认 ak/sk → 生成 AUTH token
- [ ] Step 2: 确认 --addr → 设置 BASE URL
- [ ] Step 3: GET /edge/lab/info → 获取 lab_uuid
- [ ] Step 4: GET /lab/project/list → 列出项目，让用户选择 → 获取 project_uuid
- [ ] Step 5: 确认 workflow_uuid（用户提供或从 GET #3 列表选择）
- [ ] Step 6: GET workflow detail (#4) → 提取各节点 uuid、设备ID、动作名
- [ ] Step 7: 定位本地注册表 req_device_registry_upload.json
- [ ] Step 8: 运行 gen_notebook_params.py 或手动匹配 → 生成 node_params 模板
- [ ] Step 9: 引导用户填写每轮的参数（sample_uuids、param、sample_params）
- [ ] Step 10: 构建完整请求体（含 project_uuid）→ POST /lab/notebook 提交
- [ ] Step 11: 检查返回结果，记录 notebook UUID
- [ ] Step 12: GET /lab/notebook/status → 查询 notebook 状态，确认已调度

常见问题

Q: workflow 中有多个节点，每轮都要填所有节点的参数吗？

是的。datas 数组中需要包含该轮实验涉及的每个 workflow 节点的参数。通常每个 action 节点都需要一条 datas 记录。

Q: 多轮实验的参数完全不同吗？

通常每轮的 param（设备动作参数）可能相同或相似，但 sample_uuids 和 sample_params（样品信息）每轮不同。脚本生成模板时会按轮次复制骨架，用户只需修改差异部分。

Q: 如何获取 sample_uuids 和 container_uuid？

这些 UUID 通常来自实验室的样品管理系统。向用户询问，或从资源树（API GET /lab/material/download/$lab_uuid）中查找。

Files

SKILL.md and 1 other file (scripts) in .cursor/skills/batch-submit-experiment of deepmodeling/Uni-Lab-OS.

SKILL.md
scripts/gen_notebook_params.py

Open the folder on GitHubat commit 43923ec

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Batch Submit Experiment 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.

Batch Submit Experiment compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Batch Submit Experiment this skilldeepmodeling/Uni-Lab-OS	178	—	~2.3k	Automated safety check: Pass	GPL-3.0	14 days ago
Pester Failure AnalysisPowerShell/PowerShell	56k	—	~5.1k	Automated safety check: Pass	MIT	today
Copilot Session Failure Analysisdotnet/maui	23k	—	~3.4k	Automated safety check: Pass	MIT	today
Windows App SDK Issue Triage Reportmicrosoft/WindowsAppSDK	4.7k	—	~3.4k	Automated safety check: Pass	Apache-2.0	today
Evaluate PR Testsdotnet/maui	23k	—	~2.9k	Automated safety check: Pass	MIT	today
Translation ReviewDevolutions/UniGetUI	26k	—	~1.3k	Automated safety check: Pass	MIT	today

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Questions about Batch Submit Experiment

What does Batch Submit Experiment do?

Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate nodeparams from registry schemas, submit multiple rounds, check notebook status. Batch Submit Experiment is an agent skill from deepmodeling/Uni-Lab-OS. Batch submit experiments (notebooks) to the Uni-Lab cloud platform (leap-lab) — list workflows, generate nodeparams from registry schemas, submit multiple rounds, check notebook status.

When should I use Batch Submit Experiment?

Batch Submit Experiment fits situations like: the user wants to submit experiments; create notebooks; batch run workflows; check experiment status.

How do I install Batch Submit Experiment in Claude Code?

Run `npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a claude-code`. Or copy the skill folder (.cursor/skills/batch-submit-experiment in deepmodeling/Uni-Lab-OS) into .claude/skills/batch-submit-experiment in your project. Claude Code loads it when a task matches its description.

How do I install Batch Submit Experiment in Codex?

Run `npx skills add deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a codex`. Or copy the skill folder (.cursor/skills/batch-submit-experiment in deepmodeling/Uni-Lab-OS) into .agents/skills/batch-submit-experiment in your project. Codex loads it when a task matches its description.

Can I use Batch Submit Experiment 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 deepmodeling/Uni-Lab-OS --skill batch-submit-experiment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/batch-submit-experiment, .gemini/skills/batch-submit-experiment, .github/skills/batch-submit-experiment and .opencode/skills/batch-submit-experiment in your project.

What does Batch Submit Experiment need to run?

Going by SKILL.md and its folder, Batch Submit Experiment needs Python for the scripts in its folder and the command-line tools its instructions call (curl and python). Our summary lists: Python 3.

Does Batch Submit Experiment access the network?

SKILL.md names 3 domains. In commands or code: leap-lab.test.bohrium.com, leap-lab.uat.bohrium.com and leap-lab.bohrium.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Batch Submit Experiment 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Batch Submit Experiment use?

Batch Submit Experiment is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Batch Submit Experiment use?

About 2.3k tokens (SKILL.md is roughly 9.2k 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 Batch Submit Experiment?

Skills that share tags, products or a category with Batch Submit Experiment: Pester Failure Analysis (PowerShell/PowerShell, 56k stars), Copilot Session Failure Analysis (dotnet/maui, 23k stars), Windows App SDK Issue Triage Report (microsoft/WindowsAppSDK, 4.7k stars) and Evaluate PR Tests (dotnet/maui, 23k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Batch Submit Experiment?

deepmodeling (a GitHub organization) maintains it in deepmodeling/Uni-Lab-OS, which has 178 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on September 24, 2026.

Source: deepmodeling/Uni-Lab-OS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Batch Submit Experiment

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Uni-Lab 批量提交实验指南

前置条件（缺一不可）

1. ak / sk → AUTH

2. --addr → BASE URL

3. req_device_registry_upload.json（设备注册表）

4. workflow_uuid（目标工作流）

Session State

请求约定

API Endpoints

1. 获取实验室信息（自动获取 lab_uuid）

2. 列出实验室项目（让用户选择项目）

3. 列出可用 workflow

4. 获取 workflow 模板详情

5. 提交实验（创建 notebook）

6. 查询 notebook 状态

Notebook 请求体详解

node_params 结构

每轮的字段

datas 中每个节点

sample_params 中每条

从本地注册表生成 param 模板

自动方式 — 运行脚本

手动方式

注册表结构参考

完整工作流 Checklist

常见问题

Q: workflow 中有多个节点，每轮都要填所有节点的参数吗？

Q: 多轮实验的参数完全不同吗？

Q: 如何获取 sample_uuids 和 container_uuid？

Files

Compare with similar skills

Similar skills

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Questions about Batch Submit Experiment