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Yige 1c00b8e6c8 feat(skills): 智能体 CRUD 四技能改经 ManageAgent 内置工具（实例鉴权适配） (#52 )

## 摘要 / Summary

### 中文

主仓库 desirecore#1225 新增 `ManageAgent` 内置工具后,智能体 CRUD 四技能从"调本机 HTTP
API/curl"改写为调用该工具(实例鉴权上线后 Agent 直接访问本机 API 会 401):

- **create-agent 2.5.0**:`POST /api/agents` →
`ManageAgent(action='create', ...)`;补充保留标识(core/desirecore)拒创、config 仅允许
llm 白名单的错误处理
- **delete-agent 2.5.0**:`DELETE /api/agents/:id` →
`action='delete'`(工具层强制用户确认);错误处理改为工具拒绝语义(核心智能体/自删/活跃状态);补团队级联说明
- **discover-agent 2.6.0**:`GET /api/agents(/:id)` →
`action='list'/'get'`
- **update-agent
3.1.0**:结构化字段(name/description/llm/persona/principles)改经
`action='update'`(白名单+schema 校验+字段级合并语义),自由格式文件仍 Read/Write

四技能声明 `market.required_client_version:
10.0.90`,老客户端在市场端被门控禁装。中英双语正文同步改写,i18n source_hash 已重算(validate-i18n.py
通过);manifest 1.2.10。

### English

After desirecore#1225 shipped the `ManageAgent` builtin tool, the four
agent-CRUD skills are rewritten from local-HTTP-API/curl instructions to
tool calls (direct local API access now returns 401 under instance
auth). Each skill declares `market.required_client_version: 10.0.90` so
older clients are gated from installing. Both locales are rewritten in
sync and i18n source hashes recomputed (validate-i18n.py passes);
manifest bumped to 1.2.10.

主仓库回填:合并后将在 desirecore 主仓库执行 `npm run sync-market` 生成新的
defaults/market.zip 并单独提 PR。

2026-07-18 23:05:18 +08:00

12 KiB

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name, description, version, type, risk_level, status, disable-model-invocation, tags, metadata, market

name

description

version

type

risk_level

status

disable-model-invocation

discover-agent Skill

L0: One-sentence summary

Match and recommend the most suitable Agent from the registered Agents based on the user’s needs.

L1: Overview and use cases

Capability description

discover-agent is a procedural Skill that gives DesireCore the ability to discover and recommend suitable Agents for users. By understanding the user’s needs, it performs multi-dimensional matching across the registered Agent list and shows a candidate list for the user to choose from.

Use cases

The user describes a need but does not know which Agent to ask for help
The user wants to browse the currently available Agents and their capabilities
The user needs to find the most suitable specialist assistant for a specific task
A new user is using the system for the first time and needs to know which Agents are available
The user is unhappy with the current Agent’s performance and wants a better alternative

Core value

Lower the barrier: Users do not need to remember each Agent’s name and capabilities
Precise matching: Intelligent recommendations based on semantic needs, not simple keyword search
Smooth handoff: If no match is found, automatically suggest creating a new Agent (handoff to the create-agent Skill)

L2: Detailed specification

Execution flow

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   需求理解    │ ──→ │   Agent 检索  │ ──→ │   匹配评分    │
└──────────────┘     └──────────────┘     └──────────────┘
                                                  │
                                                  ↓
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   引导选择    │ ←── │   结果展示    │ ←── │   候选排序    │
└──────────────┘     └──────────────┘     └──────────────┘

Stage 1: Needs understanding

Trigger conditions (any one of the following):

The user says "帮我找一个...", "有没有...", or "谁能帮我..."
The user describes a task but does not specify a particular Agent
The user says "有哪些智能体" or "看看都有谁"
The system detects that the user’s need does not match the current Agent’s capabilities

Need parsing:

Extract the following dimensions from the user’s description:

Dimension	Description	Example
`domain`	Professional domain	law, finance, technology, education
`task_type`	Task type	consultation, review, analysis, creation
`keywords`	Keywords	contract, report, code, paper
`urgency`	Urgency	routine / urgent

Stage 2: Agent retrieval

Data source: Call ManageAgent(action='list') to get the list of all registered Agents.

Tool call:

ManageAgent(action='list')

Returned content: a compact list where each line contains the following key fields:

name — Agent name
id — unique Agent identifier
status — current status (online/busy/idle/offline)
description — Agent description

list is a read-only query; no user confirmation is required and it is approval-free.

Filtering rules:

By default, show Agents other than offline ones; offline Agents are shown only as a supplement when no better candidate is available
Exclude internal system Agents (such as DesireCore itself, unless explicitly requested by the user)

Stage 3: Matching evaluation

Evaluate the match score based on the following dimensions (using LLM semantic understanding, not formula-based calculation):

Dimension	Description
Description relevance	Semantic relevance between the Agent’s description / persona and the user’s need
Skill match	Correlation between the Agent’s skills and the task type
Domain fit	Degree of fit between the Agent’s professional domain and the user’s domain
Status availability	The Agent’s current status (online takes priority over offline)

Display rules:

High match (clearly suitable for the task) → mark as "推荐"
Partial match (may be helpful) → mark as "可能相关"
No obvious relevance → do not display

Stage 4: Candidate ranking

Ranking rules:

Sort by overall score in descending order
If scores are tied, prefer online status
Show at most 5 candidates

Stage 5: Result display

When there are matching results:

Based on your needs, I recommend the following Agents:

┌─────────────────────────────────────────────────────┐
│ 1. 法律顾问助手                          匹配度: 92% │
│    专注合同审查和法律风险评估                          │
│    技能：合同审查、风险评估、法律研究                   │
│    状态：在线                                        │
├─────────────────────────────────────────────────────┤
│ 2. AI 文书助手                           匹配度: 71% │
│    专业文书撰写和格式优化                              │
│    技能：文书撰写、格式排版、合规检查                   │
│    状态：在线                                        │
├─────────────────────────────────────────────────────┤
│ 3. 数据分析师                            匹配度: 45% │
│    数据分析和可视化报告                                │
│    技能：数据分析、报表生成、趋势预测                   │
│    状态：离线                                        │
└─────────────────────────────────────────────────────┘

Please choose an Agent, or tell me more specific requirements.

When there are no matching results:

No fully matching Agent was found for your needs at the moment.

You can:
1. Try again with a more specific description
2. Create a new specialist Agent (I can help you)
3. Browse all available Agents

What would you like to do?

Browse mode (when the user asks to view all):

Currently available Agents:

Online:
  - 法律顾问助手 — 合同审查和法律风险评估
  - AI 文书助手 — 专业文书撰写和格式优化

Offline:
  - 数据分析师 — 数据分析和可视化报告
  - 翻译助手 — 多语言翻译和本地化

A total of 4 Agents. Do you need detailed information about any one Agent?

Stage 6: Guidance and selection

Actions after the user makes a choice:

User choice	Follow-up action
Chose an Agent	Switch to that Agent’s conversation and pass the user need context
Asked for more details	Call `ManageAgent(action='get', id='<agent-id>')` to get details, then show structured information (see below)
Unsatisfied with candidates	Guide the user to refine the need or suggest creating a new Agent
Chose "create a new one"	Call the create-agent Skill and pass the collected need information

Implementation of "learn more":

Call ManageAgent(action='get', id='<agent-id>') to get details of the specified Agent:

ManageAgent(action='get', id='legal-assistant')

Key fields in the returned content:

name, description, status
version
skill count / tool count
Git repository status

get is a read-only query; no user confirmation is required and it is approval-free. If the target does not exist, it returns the error "智能体不存在: ".

When presenting to the user, show key information in natural language/table format:

「法律顾问助手」详细信息

| 字段 | 内容 |
|------|------|
| 描述 | 专注合同审查和法律风险评估 |
| 当前状态 | 在线 |
| 版本 | 1.2.0 |
| 技能 / 工具 | 3 个技能，5 个工具 |
| Git 仓库 | 干净（无未提交变更） |

Need to talk with this Agent?

Context handoff:

context_handoff:
  source_agent: desirecore
  target_agent: legal-assistant
  user_intent: '帮我审查这份合同的风险点'

Collaboration with other Skills

Collaboration Skill	Collaboration method
create-agent	When there is no match, suggest creating a new Agent and pass the user need as initial information
task-management	After a successful match, tasks can be created automatically and assigned to the target Agent

Error handling

Error scenario	Handling method
Tool call failure	Prompt the error message and suggest trying again later
Agent list is empty	Guide the user to create the first Agent
User description is too vague	Ask follow-up questions and provide domain options as guidance
Target Agent does not exist	When `get` returns "智能体不存在: ", fall back to `list` to re-confirm available Agents
Recommended Agent has an abnormal status	Mark the status and suggest choosing another online Agent

Permission requirements

Complete Agent retrieval and detail lookup via the built-in tool ManageAgent
Both list and get are read-only queries; no user confirmation is required, they are approval-free, and carry no risk

Dependencies

Built-in tool ManageAgent (action='list' to retrieve the list, action='get' to query details)

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