Huawei Cloud Cloudrobo Workspace logo

Huawei Cloud Cloudrobo Workspace

Organization
huaweicloud
huawei-cloud-cloudrobo-workspace

Manage CloudRobo workspaces — create, list, show, update, delete workspaces; manage workspace members (add, list, update role, delete); view workspace overview statistics (capacity, used, available, member count); switch active workspace context for use by other cloudrobo skills (dataset, train, eval, infer, etc.). Workspace is the foundational resource container — all other CloudRobo operations require a valid workspace_id. Triggers include: workspace management, workspace member management, workspace switching, workspace overview, workspace configuration, default workspace, 工作空间管理, 工作空间成员, 工作空间切换, 工作空间概览, 工作空间配置, 默认工作空间.

Overview

Publisherhuaweicloud
Repositoryhuaweicloud-skills
Skill namehuawei-cloud-cloudrobo-workspace
Stars
59
Forks
24
Bundled files
9
LicenseMIT
Links
  • 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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huaweicloud on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/ai/cloudrobo/huawei-cloud-cloudrobo-workspace
  4. 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.
2

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/huaweicloud/huaweicloud-skills.git /tmp/huaweicloud-skills
mkdir -p .claude/skills
cp -r /tmp/huaweicloud-skills/skills/ai/cloudrobo/huawei-cloud-cloudrobo-workspace .claude/skills/huawei-cloud-cloudrobo-workspace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Huawei Cloud Cloudrobo Workspace 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 Huawei Cloud Cloudrobo Workspace 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 Huawei Cloud Cloudrobo Workspace 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.

Windows / PowerShell: Examples use bash syntax. To run on Windows PowerShell:

  • Flatten \ line continuations to a single line, or end lines with a backtick.
  • Set env vars with $env:NAME="value" instead of export NAME="value".
  • Single-quoted JSON '{"a":"b"}' works as-is.

Overview 概述

The cloudrobo-workspace skill manages the full lifecycle of CloudRobo workspaces. A workspace is the foundational resource container — all other CloudRobo operations (dataset processing, model training, evaluation, inference, etc.) require a valid workspace_id. This skill covers workspace CRUD, member management (add/list/update-role/delete), workspace overview statistics, and workspace context switching (use/current).

Applicable scenarios:

  • Workspace management — Create, list, show, update, or delete workspaces
  • Member management — Add members, list members, update member roles, delete members
  • Context switching — Switch active workspace via use, verify current via current; all other skills (dataset, train, eval, infer) read the active workspace from ~/.cloudrobo/workspace.json
  • Overview & quota — View workspace capacity, used count, available count, member count
  • Onboarding — First-time setup: create workspace → switch to it → start using other skills

Architecture:

Agent / LLM
    │
    ├── CLI  →  cloudrobo workspace <command>
    ├── SDK  →  WorkspaceClient (Python)
                    │
                    ▼
              cloudrobo-service (REST API)
              /v1/workspaces/*
              /v1/workspaces/{workspace_id}/members/*
              /v1/workspaces/statistic/overview

All operations target the cloudrobo-service backend. Workspace is a domain-level (account-level) resource — no --workspace-id override needed for workspace operations themselves. The active workspace context is stored in ~/.cloudrobo/workspace.json and consumed by all other skills.

Prerequisites 前置条件

See references/cli-installation-guide.md for CLI installation, AK/SK authentication, and initial configuration. Workspace is the first skill to use during onboarding — create a workspace and run cloudrobo workspace use before using any other cloudrobo skill.

Workflow 工作流

Onboarding Workflow (First-Time Setup) 首次设置

  1. Verify credentials — Ensure HUAWEI_CLOUD_AK/HUAWEI_CLOUD_SK are set
  2. List existing workspaces — cloudrobo workspace list (a default workspace may already exist)
  3. Create a workspace (if needed) — cloudrobo workspace create --name <name> --default-obs-path <obs://...>
  4. Switch to workspace — cloudrobo workspace use --workspace-id <id> (validates and saves context)
  5. Verify — cloudrobo workspace current (shows active workspace config)
  6. Proceed — Other skills (dataset, train, eval, infer) now use the active workspace

Standard Workflow (Workspace CRUD) 工作空间管理

  1. Create — cloudrobo workspace create --name <name> --default-obs-path <obs://...> [--description ...] [--tags ...] [--member-list ...]
  2. List — cloudrobo workspace list [--limit N] [--offset N] (returns workspaces + page_info + last_count)
  3. Show — cloudrobo workspace show --workspace-id <id> (returns full detail including asset_catalog_id)
  4. Update — cloudrobo workspace update --workspace-id <id> [--name ...] [--description ...] [--tags ...] [--owner-id ...]
  5. Delete — cloudrobo workspace delete --workspace-id <id> (irreversible; triggers async cleanup)

Member Management Workflow 成员管理

  1. List members — cloudrobo workspace list-members --workspace-id <id>
  2. Add members — cloudrobo workspace add-members --workspace-id <id> --member-list '<json>'
  3. Update member role — cloudrobo workspace update-member --workspace-id <id> --user-id <uid> --role-ids <r1,r2>
  4. Delete members — cloudrobo workspace delete-members --workspace-id <id> --user-ids <u1,u2>

Overview Workflow 概览统计

  1. Get overview — cloudrobo workspace overview (returns workspace_capacity, workspace_used, workspace_available, member_capacity, member_count)

Context Switching Workflow 工作空间切换

  1. Switch — cloudrobo workspace use --workspace-id <id> (validates workspace, saves to ~/.cloudrobo/workspace.json)
  2. Verify — cloudrobo workspace current (displays saved workspace config)

CLI Command Format Standard CLI命令格式标准

bash
cloudrobo workspace <command> [OPTIONS]
FeatureDescriptionExample
Command groupworkspacecloudrobo workspace
Subcommandkebab-casecreate, list, show, add-members
Workspace ID--workspace-id <id>--workspace-id c1d2e3f4-a5b6-7890-cdef-901234567890
Output formatJSON to stdoutout(result)
Dry-run--dry-run (create/update/delete)Preview without executing
OBS policy bind--bind-obs-policy (update only)Bind OBS bucket policy only; no other field updated
Comma list--user-ids id1,id2,id3--user-ids aaa,bbb
JSON string--member-list '<json>'--member-list '[{"user_id":"u1","role_ids":["r1"]}]'
Tags--tags tag1,tag2 (comma-separated)--tags dev,internal

Core Commands 核心命令

Workspace Management 工作空间管理

Create a workspace
bash
cloudrobo workspace create --name <workspace-name> --default-obs-path <obs://bucket/path> [--description <description>] [--tags <tag1,tag2>] [--member-list '<json>'] [--dry-run]
  • SDK: client.create_workspace(req: dict)
  • API: POST /v1/workspaces

Required fields: name (3-64 chars, pattern: ^[\u4e00-\u9fa5a-zA-Z0-9-_./]{3,64}$), default_obs_path (obs:// protocol, max 512 chars). Optional: description (max 512), tags (max 10, each 1-16 chars), member_list (max 100 members). See references/workspace-config-catalog.md for full field mapping and validation rules.

Server-side actions on create: check name uniqueness → check workspace quota → create Workspace entity → bind OBS bucket policy → create asset catalog → insert DB → add administrator (if non-root user) → create default quota → add members (if provided).

List workspaces
bash
cloudrobo workspace list [--limit <n>] [--offset <n>]
  • SDK: client.list_workspaces(limit=..., offset=...)
  • API: GET /v1/workspaces?limit=<n>&offset=<n>

Returns: workspaces (list), page_info (total, current_count, offset), last_count (remaining workspace quota). A default workspace named "default" is auto-created on first list if it does not exist.

Show workspace detail
bash
cloudrobo workspace show --workspace-id <workspace-id>
  • SDK: client.show_workspace(workspace_id: str)
  • API: GET /v1/workspaces/{workspace_id}

Returns: full workspace detail including workspace_id, name, description, asset_catalog_id, default_obs_path, domain_id, owner_id, owner (name), create_at, update_at, tags, role_ids, repository_id.

Update a workspace
bash
cloudrobo workspace update --workspace-id <workspace-id> [--name <new-name>] [--description <new-description>] [--tags <tag1,tag2>] [--owner-id <user-id>] [--default-obs-path <obs://...>] [--bind-obs-policy] [--dry-run]
  • SDK: client.update_workspace(workspace_id: str, req: dict)
  • API: PUT /v1/workspaces/{workspace_id}

Updatable fields: name, description, tags (full replacement), owner_id (transfer ownership), default_obs_path, bind_obs_policy (boolean flag — binds the OBS bucket policy only, does not update any other workspace field). The default workspace can only set default_obs_path once (first time); other updates to the default workspace are rejected.

Delete a workspace
bash
cloudrobo workspace delete --workspace-id <workspace-id> [--dry-run]
  • SDK: client.delete_workspace(workspace_id: str)
  • API: DELETE /v1/workspaces/{workspace_id}

Deletion is irreversible. Only root user or workspace owner can delete. The default workspace cannot be deleted. Server creates async cleanup tasks on deletion.

Member Management 成员管理

List workspace members
bash
cloudrobo workspace list-members --workspace-id <workspace-id>
  • SDK: client.list_workspace_members(workspace_id: str)
  • API: GET /v1/workspaces/{workspace_id}/members

Returns: members (list with workspace_id, user_id, username, roles (role_id + role_name), is_owner), remain_count (remaining member quota). The root user is always listed as super_administrator.

Add workspace members
bash
cloudrobo workspace add-members --workspace-id <workspace-id> --member-list '<json>'
  • SDK: client.add_workspace_members(workspace_id: str, req: dict)
  • API: POST /v1/workspaces/{workspace_id}/members

Required: member_list JSON array, each entry has user_id (32-char hex, IAM project ID format) and role_ids (UUID list, max 10). Root users cannot be added as members. Duplicate members are rejected. The default workspace does not support member operations.

Update a member's roles
bash
cloudrobo workspace update-member --workspace-id <workspace-id> --user-id <user-id> --role-ids <role-id-1,role-id-2>
  • SDK: client.update_workspace_member(workspace_id: str, req: dict)
  • API: PUT /v1/workspaces/{workspace_id}/members

Required: user_id and role_ids. The workspace owner's role cannot be updated. The default workspace does not support member operations.

Delete workspace members
bash
cloudrobo workspace delete-members --workspace-id <workspace-id> --user-ids <user-id-1,user-id-2>
  • SDK: client.delete_workspace_members(workspace_id: str, user_ids: list)
  • API: DELETE /v1/workspaces/{workspace_id}/members?user_ids=<ids>

The workspace owner cannot be deleted. The default workspace does not support member operations. Max 20 user IDs per request (API constraint) / 100 per request (CLI constraint).

Overview & Statistics 概览统计

Get workspace overview
bash
cloudrobo workspace overview
  • SDK: client.get_workspace_overview()
  • API: GET /v1/workspaces/statistic/overview

Returns: workspace_capacity (max workspaces), workspace_used (current count), workspace_available (remaining), member_capacity (max members, requires BASIC resource SKU), member_count (current member count including root user).

Workspace Context 工作空间上下文

Switch active workspace
bash
cloudrobo workspace use --workspace-id <workspace-id>

Validates the workspace exists and is accessible, then saves to ~/.cloudrobo/workspace.json: workspace_id, name, asset_catalog_id, default_obs_path. All other skills read this file to determine the active workspace.

Show current workspace
bash
cloudrobo workspace current

Displays the saved workspace config from ~/.cloudrobo/workspace.json. If no workspace is configured, outputs "未配置工作空间".

Reference Documents 参考文档

Edge Cases 边界情况

ScenarioHandling
Missing workspace_idOperations fail with validation error; use cloudrobo workspace list to find valid IDs
Workspace not foundServer rejects with WORKSPACE_NOT_EXIST_ERROR; verify ID via workspace list
Default workspaceAuto-created on first list; cannot be deleted; only supports first-time default_obs_path update; does not support member add/update/delete operations
Workspace name conflictServer rejects with WORKSPACE_NAME_EXIST error; names are case-insensitive unique per domain
Workspace quota exceededServer rejects with WORKSPACE_EXCEED_NUM_LIMIT; check overview for capacity; last_count in list response shows remaining
Root user as memberServer rejects with ADD_ADMINISTRATOR_LIMIT_ERROR; root user is always super_administrator
Update/delete workspace ownerServer rejects with REQUEST_FORBIDDEN_ERROR; owner's role cannot be changed, owner cannot be removed as member
Transfer ownershipupdate --owner-id <new-owner> — new owner must be an existing IAM user; if not root, must be or become a workspace member with administrator role
default_obs_path formatMust match ^obs://[a-z0-9\-]{3,63}/[^\s]+$; s3:// is prohibited
workspace_id formatMust be UUID: ^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$
user_id formatMust be 32-char lowercase hex (IAM project ID): ^[0-9a-z]{32}$
role_ids formatMust be UUID format; validated against role cache; max 10 per member
Member already existsServer rejects with WORKSPACE_MEMBER_HAS_EXIST
Member not foundServer rejects with WORKSPACE_MEMBER_NOT_EXIST
AK/SK not setOperations fail at HTTP signing step; set HUAWEI_CLOUD_AK/HUAWEI_CLOUD_SK
workspace use on invalid IDCLI catches error, outputs "切换失败: ..." and exits with code 1
workspace current with no configOutputs "未配置工作空间" (no workspace configured)
Tags full replacementupdate --tags replaces all tags; to remove all tags, pass empty string
Delete triggers cleanupServer creates async cleanup tasks (CleanupService); workspace resources (OBS, catalog, quota) are cleaned asynchronously
asset_catalog_idAuto-created on workspace creation (or first default_obs_path set for default workspace); stored in workspace entity; used by asset/dataset skills
Role system3 roles: super_administrator (sort=0, root user only), administrator (sort=1), member (sort=2); role_ids are dynamic UUIDs fetched from server
Member sortingMembers sorted by: owner first, then by role sort value (super_administrator > administrator > member)
Internal API/v1/internal/workspaces exists for service-to-service calls (no IAM auth); not used by CLI/SDK
Object storage pathsMust use obs:// protocol; s3:// is prohibited
API pathsSourced from SDK source code (_url() calls in client.py), not inferred
Mutating operationscreate/update/delete/add-members/update-member/delete-members should be confirmed by the user
Cross-skill invocationThis skill does not call other skills by name; provides foundational workspace context for all other skills
workspace.json permissionsFile created with 0o600 permissions (owner read/write only)
Member list in createWhen creating workspace with member_list, the creator is automatically excluded (no duplicate)

Verification Method 验证方法

Specification Compliance Verification 规范合规验证

bash
bash scripts/test-cli-commands.sh -s . -e cli

Functional Testing 功能测试

bash
# CLI / SDK (read-only cases TC-01..TC-10 / TC-19..TC-22 run automatically)
bash scripts/test-cli-commands.sh -s . -e {cli|sdk}

# Include mutation cases (TC-11..TC-18, TC-23..TC-28) — each prompts for confirmation
bash scripts/test-cli-commands.sh -s . -e {cli|sdk} -m

API-level verification is manual — replay the paths in references/api-paths.md in API Explorer.

Test Cases 测试用例

See templates/test-vars.json for the full test case list covering workspace CRUD, member management, overview, and context switching scenarios.

Verification Checklist 验证清单

  • After creating a workspace, verify via show --workspace-id <id> that asset_catalog_id is populated
  • After use --workspace-id <id>, verify current outputs the correct workspace config
  • After adding members, verify via list-members that new members appear with correct roles
  • After deleting a workspace, verify it no longer appears in list
  • Verify overview returns consistent workspace_used count with list results

Best Practices 最佳实践

  • Always run workspace list first to check existing workspaces before creating a new one
  • Use --dry-run with create/update/delete to preview operations before executing
  • After creating a workspace, immediately run workspace use --workspace-id <id> to switch context
  • Before deleting a workspace, confirm the workspace ID to avoid irreversible deletion
  • When adding members, use list-members first to check existing members and avoid duplicates
  • The default workspace ("default") has special restrictions — prefer creating a named workspace for production use
  • Check workspace overview periodically to monitor workspace and member quota usage
  • workspace current is a quick way to verify the active workspace before running other cloudrobo commands
  • Transfer ownership with caution — the new owner gains full control of the workspace
  • Tags are fully replaced on update, not merged — include all desired tags in the update command

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Huawei Cloud Cloudrobo Workspace AI skill do?

Manage CloudRobo workspaces — create, list, show, update, delete workspaces; manage workspace members (add, list, update role, delete); view workspace overview statistics (capacity, used, available, member count); switch active workspace context for use by other cloudrobo skills (dataset, train, eval, infer, etc.). Workspace is the foundational resource container — all other CloudRobo operations require a valid workspace_id. Triggers include: workspace management, workspace member management, workspace switching, workspace overview, workspace configuration, default workspace, 工作空间管理, 工作空间成员...

Why use Huawei Cloud Cloudrobo Workspace on TypingMind?

Because you install it once and use it with any model. Huawei Cloud Cloudrobo Workspace 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 Huawei Cloud Cloudrobo Workspace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huaweicloud/huaweicloud-skills/tree/master/skills/ai/cloudrobo/huawei-cloud-cloudrobo-workspace. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Huawei Cloud Cloudrobo Workspace?

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 Huawei Cloud Cloudrobo Workspace?

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

Is the Huawei Cloud Cloudrobo Workspace AI skill free?

Yes. It is published on GitHub by huaweicloud under the MIT license. 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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