market/skills/discover-agent/SKILL.md

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

name	description	version	type	risk_level	status	disable-model-invocation	tags	metadata	market
discover-agent	根据用户需求推荐最匹配的智能体，展示候选列表并引导选择。Use when 用户描述需求但不确定该找哪个智能体帮忙，或想浏览可用的智能体。	2.5.2	procedural	low	enabled	true	agent discovery recommendation	author updated_at i18n desirecore 2026-02-28 default_locale source_locale locales zh-CN en-US en-US zh-CN zh-CN en-US name short_desc description body source_hash translated_by 发现智能体 根据需求描述智能推荐最匹配的智能体，引导快速选择 根据用户需求推荐最匹配的智能体，展示候选列表并引导选择。Use when 用户描述需求但不确定该找哪个智能体帮忙，或想浏览可用的智能体。 ./SKILL.zh-CN.md sha256:28ecd07724adda9a human name short_desc description body source_hash translated_by translated_at Discover Agent Intelligently recommend the best-matching Agent based on the user's needs and guide a quick selection Recommend the best-matching Agent based on the user's needs, present a candidate list, and guide the user's selection. Use when the user describes a need but is unsure which Agent to ask, or wants to browse available Agents. ./SKILL.md sha256:28ecd07724adda9a ai:claude-opus-4-7 2026-05-03	icon category maintainer compatible_agents channel <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none"><defs><linearGradient id="da-a" x1="2" y1="2" x2="22" y2="22" gradientUnits="userSpaceOnUse"><stop stop-color="#AF52DE" stop-opacity="0.12"/><stop offset="1" stop-color="#007AFF" stop-opacity="0.06"/></linearGradient></defs><circle cx="12" cy="12" r="10" fill="url(#da-a)" stroke="#AF52DE" stroke-width="1.5"/><path d="M16.24 7.76l-1.8 5.41a2 2 0 0 1-1.27 1.27L7.76 16.24" fill="#FF9500" fill-opacity="0.85"/><path d="M7.76 16.24l1.8-5.41a2 2 0 0 1 1.27-1.27L16.24 7.76" fill="#AF52DE" fill-opacity="0.7"/><circle cx="12" cy="12" r="1.5" fill="white" stroke="#AF52DE" stroke-width="0.8"/></svg> productivity name verified DesireCore Official true latest

discover-agent skill

L0: One-Sentence Summary

Match and recommend the most suitable registered Agent based on the user's described needs.

L1: Overview and Use Cases

Capability Description

discover-agent is a Procedural Skill that gives DesireCore the ability to discover and recommend a suitable Agent for the user. It understands the user's described needs, performs multi-dimensional matching across the registered Agent list, and presents a candidate list for the user to choose from.

Use Cases

• The user describes a need but doesn't know which Agent to ask for help
• The user wants to browse currently available Agents and their capabilities
• The user needs to find the best specialist assistant for a specific task
• A new user trying the system for the first time needs to learn which Agents are available

Core Value

• Lower the barrier: Users don't have to remember each Agent's name and capabilities
• Precise matching: Smart recommendations based on the semantics of the need, not simple keyword search
• Smooth handoff: When there's no match, automatically suggest creating a new Agent (handing off 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: Need Understanding

Trigger conditions (any one is sufficient):

• The user says "find me a...", "is there a...", "who can help me..."
• The user describes a task without specifying a particular Agent
• The user says "what Agents are there?", "show me who's available"
• 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	Examples
domain	Specialty domain	Legal, finance, technology, education
task_type	Task type	Consultation, review, analysis, writing
keywords	Keywords	Contract, report, code, paper
urgency	Urgency level	Routine / urgent

Stage 2: Agent Retrieval

Data source: Call GET /api/agents to fetch the list of all registered Agents.

API call:

GET /api/agents

Key fields in the returned data:

• id — Unique Agent identifier
• name — Agent name
• description — Agent description
• skills — Skill list
• status — Current status (online/offline/busy)

Filter rules:

• By default, show only Agents with status: online or status: offline
• Exclude system-internal Agents (e.g. DesireCore itself, unless the user explicitly requests them)

Stage 3: Match Evaluation

Comprehensively judge match degree based on the following dimensions (using LLM semantic understanding rather than formula-based computation):

Dimension	Description
Description relevance	Semantic relevance between Agent description / persona and user's need
Skill match	Relevance of the Agent's skills to the task type
Domain fit	Fit between the Agent's specialty domain and the user's need domain
Status availability	Agent's current status (online preferred over offline)

Display rules:

• Highly matched (clearly suited to the task) → tag as "Recommended"
• Partial match (may help) → tag as "Possibly relevant"
• No clear relation → do not display

Stage 4: Candidate Ranking

Ranking rules:

1. Sort by overall score in descending order
2. On ties, online status takes precedence
3. Show at most 5 candidates

Stage 5: Result Display

When matches are found:

根据你的需求，我推荐以下智能体：

┌─────────────────────────────────────────────────────┐
│ 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                                        │
└─────────────────────────────────────────────────────┘

请选择一个智能体，或告诉我更具体的需求。

When no matches are found:

目前没有找到完全匹配你需求的智能体。

你可以：
1. 用更具体的描述再试一次
2. 创建一个新的专业智能体（我可以帮你）
3. 浏览所有可用的智能体

你想怎么做？

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

当前可用的智能体：

在线：
  - 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

离线：
  - 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

共 4 个智能体。需要了解某个智能体的详细信息吗？

Stage 6: Guided Selection

Actions after the user selects:

User Choice	Follow-up Action
Selected an Agent	Switch to a conversation with that Agent and pass the need context
Asked to learn more	Call GET /api/agents/:id for details and present structured info (see below)
Not satisfied with candidates	Guide the user to refine the need or suggest creating a new Agent
Chose "create new"	Invoke the create-agent skill, passing the need info already collected

Implementation of "learn more":

Call GET /api/agents/:id for details, and optionally call structured endpoints for persona/principles:

# 获取基本信息
GET /api/agents/{agentId}
# 返回: { id, name, description, skillsCount, toolsCount, status, config, persona, principles }

# 获取结构化 persona（可选，用于展示更丰富的信息）
GET /api/agents/{agentId}/persona
# 返回: { L0, L1: { role, personality, communication_style }, L2 }

When presenting to the user, render key information in natural language / table form:

「法律顾问助手」详细信息

| 字段 | 内容 |
|------|------|
| 角色定位 | 专注合同审查和法律风险评估 |
| 性格特征 | 专业、严谨、审慎 |
| 技能数量 | 3 个 |
| 当前状态 | 在线 |

需要与这个智能体对话吗？

Context handoff on switch:

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

Collaborating Skill	How It Collaborates
create-agent	When there's no match, suggest creating a new Agent and pass the user's need as initial info
task-management	After a successful match, optionally auto-create a task and assign it to the target Agent

Error Handling

Error Scenario	Handling
API call fails	Indicate a network error and suggest retrying later
Agent list is empty	Guide the user to create their first Agent
User description too vague	Ask follow-up questions and offer domain options to guide
Recommended Agent has bad status	Mark the status and suggest selecting another online Agent

Permission Requirements

• Prefer accessing the Agent Service HTTP API via the Bash tool with curl
• The API base URL is already injected into the "Local API" section of the system prompt; reference it directly
• Read-only operations; no risk

Dependencies

• Agent Service HTTP API (GET /api/agents)
• The local API URL declaration in the system prompt