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perf(skills): 智能体 CRUD 四技能提示词改写压缩（功能不变，~65%） (#56)
## 摘要 / Summary ### 中文 四个智能体管理技能（create/update/delete/discover-agent）改用 `ManageAgent` 内置工具后，正文与工具契约大量重叠——ManageAgent 的 description + params 已**常驻每次 query 的上下文**，声明了五个 action 语义、参数约束、权限硬边界、错误语义、字段级合并、确认行为、list/get 返回格式；技能正文里再复述即冗余。本次对四技能做**强改写·语境融合**压缩： - 与工具契约重复的说明（参数/权限/错误码/确认行为/成功返回话术）**改写融入对应流程步骤**（如错误处理表 → 阶段一句、确认行为 → 阶段一句），不再照抄、不再表格化。 - 装饰性 ASCII 流程框图 → 一行文字流程；update 内部两张重复更新表 → 合并；YAML 元数据块（diff_metadata/context_handoff）融入流程；冗长示例（create 三份 JSON、update 附录 4 示例、discover ASCII 卡片）就地精简为代表示意。 - **不外置 references、不净删除任何内容**：所有决策/领域/交互意图完整保留——领域匹配表、persona/principles 的 L0/L1/L2 生成规范、update 两路径分流与字段级合并 vs 整体替换、防幻觉改名、回滚流程、discover 需求维度与无匹配衔接 create 等核心一字未丢。 zh 正文合计 **26542 → 9196 字符（降 ~65%）**；中英双份同步改写、逐段对齐，重算 i18n source_hash（validate-i18n 通过）。版本 create 2.5.1 / update 3.1.2 / delete 2.5.1 / discover 2.6.1，manifest 1.2.12。 ### English After the four agent-management skills adopted the `ManageAgent` builtin tool, their bodies heavily duplicated the tool contract — ManageAgent's description + params are **resident in every query's context** (action semantics, param constraints, permission hard-boundaries, error semantics, field-level merge, confirmation behavior, list/get return formats). This PR compresses all four via **aggressive rewrite + contextual fusion**: contract-duplicating text is rewritten into the relevant flow steps (not copied, not tabularized), decorative ASCII flow boxes become one-line text, update's two duplicate tables are merged, YAML metadata blocks are folded in, and long examples are trimmed in place to representative sketches. **No references externalization, no net deletion** — every decision/domain/interaction intent is preserved (domain matching table, persona/principles L0/L1/L2 generation spec, update's two-path split and field-level-merge-vs-full-replace, anti-hallucination rename, rollback flow, discover's need dimensions and create hand-off). zh bodies total **26542 → 9196 chars (~65% down)**; both locales rewritten and aligned, i18n source hashes recomputed (validate-i18n passes). Versions bumped, manifest 1.2.12.
This commit is contained in:
GitHub
@@ -1,5 +1,9 @@
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# Changelog
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## [2.6.1] - 2026-07-19
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- 提示词改写压缩，功能不变——与 ManageAgent 工具契约重复的说明、装饰框图、重复表改写融入流程，示例就地精简；不外置、不净删，意图全保留。
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## [2.6.0] - 2026-07-18
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- HTTP API 调用改为 ManageAgent 内置工具（实例鉴权后 Agent 不再直接访问本机 API）
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@@ -1,7 +1,7 @@
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---
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name: discover-agent
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description: 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。
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version: 2.6.0
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version: 2.6.1
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type: procedural
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risk_level: low
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status: enabled
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@@ -12,7 +12,7 @@ tags:
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- recommendation
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metadata:
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author: desirecore
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updated_at: '2026-07-18'
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updated_at: '2026-07-19'
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i18n:
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default_locale: en-US
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source_locale: zh-CN
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@@ -24,7 +24,7 @@ metadata:
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short_desc: 根据需求描述智能推荐最匹配的智能体，引导快速选择
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description: 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。
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body: ./SKILL.zh-CN.md
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source_hash: sha256:99bddbbaea15b194
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source_hash: sha256:671b1b159567a5a3
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translated_by: human
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en-US:
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name: Discover Agent
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@@ -32,9 +32,9 @@ metadata:
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description: >-
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Recommend the best-matching Agent based on the user’s needs, show a candidate list, and guide selection. Use when the user describes a need but is unsure which Agent to ask for help, or wants to browse available Agents.
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body: ./SKILL.md
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source_hash: sha256:99bddbbaea15b194
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source_hash: sha256:671b1b159567a5a3
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translated_by: ai:claude-fable-5
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translated_at: '2026-07-18'
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translated_at: '2026-07-19'
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market:
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icon: >-
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0
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@@ -56,247 +56,51 @@ market:
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required_client_version: 10.0.90
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---
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# discover-agent Skill
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# discover-agent skill
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## L0: One-sentence summary
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## L0: One-Sentence Summary
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Match and recommend the most suitable Agent from the registered Agents based on the user’s needs.
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Match and recommend the most suitable Agent among registered Agents based on the user's need description.
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## L1: Overview and use cases
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## L1: Overview
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### Capability description
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Procedural skill: understand the need → retrieve with `ManageAgent(action='list')` → semantic match scoring → rank and present → guide selection; on no match, hand off to create-agent automatically. Its value is what the tool can't give: semantic need matching, candidate ranking and presentation, and the create hand-off on no match. `list` / `get` are read-only, approval-free.
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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.
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## L2: Detailed Spec
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### Use cases
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Flow: need understanding → retrieval → match evaluation → ranking → presentation → guided selection.
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- The user describes a need but does not know which Agent to ask for help
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- The user wants to browse the currently available Agents and their capabilities
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- The user needs to find the most suitable specialist assistant for a specific task
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- A new user is using the system for the first time and needs to know which Agents are available
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- The user is unhappy with the current Agent’s performance and wants a better alternative
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### Stage 1: Need Understanding
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### Core value
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Trigger (any): user says "find me a… / is there a… / who can help me…", describes a task without naming an Agent, "which Agents are there", or the system detects a need that mismatches the current Agent. Extract dimensions from the description: `domain` (legal/finance/tech/education), `task_type` (consult/review/analyze/create), `keywords` (contract/report/code/paper…), `urgency` (routine/urgent).
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- **Lower the barrier**: Users do not need to remember each Agent’s name and capabilities
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- **Precise matching**: Intelligent recommendations based on semantic needs, not simple keyword search
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- **Smooth handoff**: If no match is found, automatically suggest creating a new Agent (handoff to the create-agent Skill)
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### Stage 2: Retrieval
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## L2: Detailed specification
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`ManageAgent(action='list')` fetches all registered Agents (returns a compact list with name / id / status / description). Filtering: by default show non-offline Agents; offline ones appear only as a fallback when there's no better candidate; exclude internal system Agents (e.g. DesireCore itself) unless the user explicitly asks.
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### Execution flow
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### Stage 3: Match Evaluation
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```
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ 需求理解 │ ──→ │ Agent 检索 │ ──→ │ 匹配评分 │
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└──────────────┘ └──────────────┘ └──────────────┘
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│
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↓
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ 引导选择 │ ←── │ 结果展示 │ ←── │ 候选排序 │
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└──────────────┘ └──────────────┘ └──────────────┘
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```
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Judge match with LLM semantic understanding (not a formula): relevance of description / persona to the need, association of skills to the task type, domain fit, status availability (online preferred). Presentation tiers: strong match → mark "recommended", partial → "possibly relevant", no clear relation → don't show.
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### Stage 1: Needs understanding
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### Stage 4: Ranking
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**Trigger conditions** (any one of the following):
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Descending by overall score; ties broken by online status; show at most 5 candidates.
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- The user says "帮我找一个...", "有没有...", or "谁能帮我..."
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- The user describes a task but does not specify a particular Agent
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- The user says "有哪些智能体" or "看看都有谁"
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- The system detects that the user’s need does not match the current Agent’s capabilities
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### Stage 5: Presentation
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**Need parsing**:
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- **With matches**: list candidates, each with name, description, key skills, status, and match score; ask the user to choose or refine the need. E.g. "1. Legal Advisor (92%) — contract review and legal risk assessment; skills: contract review / risk assessment / legal research; online".
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- **No match**: report that none were found and offer three options — retry with a more specific description / create a new specialized Agent (hand off to create-agent) / browse all.
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- **Browse mode** (user wants to see all): list name + description grouped by online / offline, and ask whether to view details of any.
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Extract the following dimensions from the user’s description:
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### Stage 6: Guided Selection
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| Dimension | Description | Example |
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| --------- | ----------- | ------- |
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| `domain` | Professional domain | law, finance, technology, education |
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| `task_type` | Task type | consultation, review, analysis, creation |
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| `keywords` | Keywords | contract, report, code, paper |
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| `urgency` | Urgency | routine / urgent |
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- Chose an Agent → switch to that Agent's conversation, passing the user's need context (source / target / user_intent).
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- Wants more detail → `ManageAgent(action='get', id)` for details (name / description / status / version / skill count / tool count / Git status); present the key info in natural language or a table and ask whether to chat.
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- Unhappy with candidates → guide the user to refine the need or suggest creating a new Agent.
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- Chose "create new" → invoke the create-agent skill, passing the gathered requirements.
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### Stage 2: Agent retrieval
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### Collaboration and Error Handling
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**Data source**: Call `ManageAgent(action='list')` to get the list of all registered Agents.
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**Tool call**:
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```
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ManageAgent(action='list')
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```
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**Returned content**: a compact list where each line contains the following key fields:
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- `name` — Agent name
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- `id` — unique Agent identifier
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- `status` — current status (online/busy/idle/offline)
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- `description` — Agent description
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> `list` is a read-only query; no user confirmation is required and it is approval-free.
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**Filtering rules**:
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- By default, show Agents other than offline ones; offline Agents are shown only as a supplement when no better candidate is available
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- Exclude internal system Agents (such as DesireCore itself, unless explicitly requested by the user)
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### Stage 3: Matching evaluation
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Evaluate the match score based on the following dimensions (using LLM semantic understanding, not formula-based calculation):
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| Dimension | Description |
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| --------- | ----------- |
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| Description relevance | Semantic relevance between the Agent’s description / persona and the user’s need |
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| Skill match | Correlation between the Agent’s skills and the task type |
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| Domain fit | Degree of fit between the Agent’s professional domain and the user’s domain |
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| Status availability | The Agent’s current status (online takes priority over offline) |
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**Display rules**:
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- High match (clearly suitable for the task) → mark as "推荐"
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- Partial match (may be helpful) → mark as "可能相关"
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- No obvious relevance → do not display
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### Stage 4: Candidate ranking
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**Ranking rules**:
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1. Sort by overall score in descending order
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2. If scores are tied, prefer online status
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3. Show at most 5 candidates
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### Stage 5: Result display
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**When there are matching results**:
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```
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Based on your needs, I recommend the following Agents:
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┌─────────────────────────────────────────────────────┐
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│ 1. 法律顾问助手 匹配度: 92% │
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│ 专注合同审查和法律风险评估 │
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│ 技能：合同审查、风险评估、法律研究 │
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│ 状态：在线 │
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├─────────────────────────────────────────────────────┤
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│ 2. AI 文书助手 匹配度: 71% │
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│ 专业文书撰写和格式优化 │
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│ 技能：文书撰写、格式排版、合规检查 │
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│ 状态：在线 │
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├─────────────────────────────────────────────────────┤
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│ 3. 数据分析师 匹配度: 45% │
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│ 数据分析和可视化报告 │
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│ 技能：数据分析、报表生成、趋势预测 │
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│ 状态：离线 │
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└─────────────────────────────────────────────────────┘
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Please choose an Agent, or tell me more specific requirements.
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```
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**When there are no matching results**:
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```
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No fully matching Agent was found for your needs at the moment.
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You can:
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1. Try again with a more specific description
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2. Create a new specialist Agent (I can help you)
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3. Browse all available Agents
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What would you like to do?
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```
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**Browse mode** (when the user asks to view all):
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```
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Currently available Agents:
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Online:
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- 法律顾问助手 — 合同审查和法律风险评估
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- AI 文书助手 — 专业文书撰写和格式优化
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Offline:
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- 数据分析师 — 数据分析和可视化报告
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- 翻译助手 — 多语言翻译和本地化
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A total of 4 Agents. Do you need detailed information about any one Agent?
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```
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### Stage 6: Guidance and selection
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**Actions after the user makes a choice**:
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| User choice | Follow-up action |
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| ----------- | ---------------- |
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| Chose an Agent | Switch to that Agent’s conversation and pass the user need context |
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| Asked for more details | Call `ManageAgent(action='get', id='<agent-id>')` to get details, then show structured information (see below) |
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| Unsatisfied with candidates | Guide the user to refine the need or suggest creating a new Agent |
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| Chose "create a new one" | Call the create-agent Skill and pass the collected need information |
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**Implementation of "learn more"**:
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Call `ManageAgent(action='get', id='<agent-id>')` to get details of the specified Agent:
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```
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ManageAgent(action='get', id='legal-assistant')
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```
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**Key fields in the returned content**:
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- name, description, status
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- version
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- skill count / tool count
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- Git repository status
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> `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: <id>".
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When presenting to the user, show key information in natural language/table format:
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```
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「法律顾问助手」详细信息
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| 字段 | 内容 |
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|------|------|
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| 描述 | 专注合同审查和法律风险评估 |
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| 当前状态 | 在线 |
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| 版本 | 1.2.0 |
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| 技能 / 工具 | 3 个技能，5 个工具 |
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| Git 仓库 | 干净（无未提交变更） |
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Need to talk with this Agent?
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```
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**Context handoff**:
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```yaml
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context_handoff:
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source_agent: desirecore
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target_agent: legal-assistant
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user_intent: '帮我审查这份合同的风险点'
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```
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### Collaboration with other Skills
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| Collaboration Skill | Collaboration method |
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| ------------------- | -------------------- |
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| create-agent | When there is no match, suggest creating a new Agent and pass the user need as initial information |
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| task-management | After a successful match, tasks can be created automatically and assigned to the target Agent |
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### Error handling
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| Error scenario | Handling method |
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| -------------- | --------------- |
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| Tool call failure | Prompt the error message and suggest trying again later |
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| Agent list is empty | Guide the user to create the first Agent |
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| User description is too vague | Ask follow-up questions and provide domain options as guidance |
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| Target Agent does not exist | When `get` returns "智能体不存在: <id>", fall back to `list` to re-confirm available Agents |
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| Recommended Agent has an abnormal status | Mark the status and suggest choosing another online Agent |
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### Permission requirements
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- Complete Agent retrieval and detail lookup via the built-in tool `ManageAgent`
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- Both `list` and `get` are read-only queries; no user confirmation is required, they are approval-free, and carry no risk
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### Dependencies
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- Built-in tool `ManageAgent` (`action='list'` to retrieve the list, `action='get'` to query details)
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- Collaboration: on no match, hand off to create-agent (pass the need as initial info); on a successful match, optionally create a task and assign it to the target Agent.
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- Errors: tool call fails → report error and suggest retry; empty Agent list → guide the user to create the first Agent; overly vague need → ask follow-ups and offer domain options; `get` returns "Agent not found: <id>" → fall back to `list` to reconfirm available Agents; recommended Agent in an abnormal state → mark the status and suggest picking an online one.
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- `list` / `get` are read-only, approval-free, and risk-free; always done via `ManageAgent`.
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@@ -6,241 +6,45 @@
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根据用户需求描述，在已注册的智能体中匹配并推荐最合适的 Agent。
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## L1：概述与使用场景
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## L1：概述
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### 能力描述
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discover-agent 是一个**流程型技能（Procedural Skill）**，赋予 DesireCore 为用户发现和推荐合适智能体的能力。它通过理解用户需求描述，在已注册的 Agent 列表中进行多维度匹配，展示候选列表供用户选择。
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### 使用场景
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- 用户描述了一个需求，但不知道该找哪个智能体帮忙
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- 用户想浏览当前可用的智能体及其能力
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||||
- 用户需要为特定任务找到最合适的专业助手
|
||||
- 新用户初次使用系统，需要了解有哪些智能体可用
|
||||
- 用户对当前智能体的表现不满意，想寻找更合适的替代者
|
||||
|
||||
### 核心价值
|
||||
|
||||
- **降低门槛**：用户无需记住每个智能体的名称和能力
|
||||
- **精准匹配**：基于需求语义进行智能推荐，而非简单关键词搜索
|
||||
- **流畅衔接**：无匹配时自动建议创建新 Agent（衔接 create-agent 技能）
|
||||
流程型技能：理解需求 → `ManageAgent(action='list')` 检索 → 语义匹配评分 → 排序展示 → 引导选择；无匹配时自动衔接 create-agent。价值在工具给不了的部分：需求语义匹配、候选排序与呈现、无匹配时的创建衔接。`list` / `get` 只读免审批。
|
||||
|
||||
## L2：详细规范
|
||||
|
||||
### 执行流程
|
||||
|
||||
```
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ 需求理解 │ ──→ │ Agent 检索 │ ──→ │ 匹配评分 │
|
||||
└──────────────┘ └──────────────┘ └──────────────┘
|
||||
│
|
||||
↓
|
||||
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
|
||||
│ 引导选择 │ ←── │ 结果展示 │ ←── │ 候选排序 │
|
||||
└──────────────┘ └──────────────┘ └──────────────┘
|
||||
```
|
||||
流程：需求理解 → 检索 → 匹配评估 → 排序 → 展示 → 引导选择。
|
||||
|
||||
### 阶段 1：需求理解
|
||||
|
||||
**触发条件**（任一满足）：
|
||||
触发（任一）：用户说"帮我找一个… / 有没有… / 谁能帮我…"、描述任务但未指定智能体、"有哪些智能体"，或系统检测到需求与当前 Agent 能力不匹配。从描述提取维度：`domain`（领域，如法律/财务/技术/教育）、`task_type`（咨询/审查/分析/创作）、`keywords`（合同/报表/代码/论文…）、`urgency`（日常/紧急）。
|
||||
|
||||
- 用户说"帮我找一个..."、"有没有..."、"谁能帮我..."
|
||||
- 用户描述了一个任务但未指定具体智能体
|
||||
- 用户说"有哪些智能体"、"看看都有谁"
|
||||
- 系统检测到用户需求与当前 Agent 能力不匹配
|
||||
### 阶段 2：检索
|
||||
|
||||
**需求解析**：
|
||||
|
||||
从用户描述中提取以下维度：
|
||||
|
||||
| 维度 | 说明 | 示例 |
|
||||
| ----------- | -------- | ---------------------- |
|
||||
| `domain` | 专业领域 | 法律、财务、技术、教育 |
|
||||
| `task_type` | 任务类型 | 咨询、审查、分析、创作 |
|
||||
| `keywords` | 关键词 | 合同、报表、代码、论文 |
|
||||
| `urgency` | 紧急程度 | 日常 / 紧急 |
|
||||
|
||||
### 阶段 2：Agent 检索
|
||||
|
||||
**数据源**：调用 `ManageAgent(action='list')` 获取所有已注册的智能体列表。
|
||||
|
||||
**工具调用**：
|
||||
|
||||
```
|
||||
ManageAgent(action='list')
|
||||
```
|
||||
|
||||
**返回内容**：一份紧凑列表，每行包含以下关键字段：
|
||||
|
||||
- `name` — 智能体名称
|
||||
- `id` — 智能体唯一标识
|
||||
- `status` — 当前状态（online/busy/idle/offline）
|
||||
- `description` — 智能体描述
|
||||
|
||||
> `list` 为只读查询，无需用户确认、免审批。
|
||||
|
||||
**过滤规则**：
|
||||
|
||||
- 默认展示除 offline 之外的智能体，offline 智能体仅在无更优候选时作为补充展示
|
||||
- 排除系统内部智能体（如 DesireCore 自身，除非用户显式要求）
|
||||
`ManageAgent(action='list')` 取全部已注册智能体（返回 name / id / status / description 的紧凑列表）。过滤：默认展示 offline 之外的，offline 仅在无更优候选时补充；排除系统内部智能体（如 DesireCore 自身，除非用户显式要求）。
|
||||
|
||||
### 阶段 3：匹配评估
|
||||
|
||||
根据以下维度综合判断匹配度（使用 LLM 语义理解，非公式计算）：
|
||||
用 LLM 语义理解综合判断匹配度（非公式计算）：description / persona 与需求的相关性、skills 与任务类型的关联、领域契合度、状态可用性（online 优先）。展示分级：高度匹配 → 标"推荐"，部分匹配 → 标"可能相关"，无明显关联 → 不展示。
|
||||
|
||||
| 维度 | 说明 |
|
||||
| ---------- | --------------------------------------------------- |
|
||||
| 描述相关性 | 智能体 description / persona 与用户需求的语义相关度 |
|
||||
| 技能匹配度 | 智能体拥有的 skills 与任务类型的关联度 |
|
||||
| 领域契合度 | 智能体专业领域与用户需求领域的契合程度 |
|
||||
| 状态可用性 | 智能体当前状态（online 优先于 offline） |
|
||||
### 阶段 4：排序
|
||||
|
||||
**展示规则**：
|
||||
|
||||
- 高度匹配（明确适合该任务）→ 标为"推荐"
|
||||
- 部分匹配（可能有帮助）→ 标为"可能相关"
|
||||
- 无明显关联 → 不展示
|
||||
|
||||
### 阶段 4：候选排序
|
||||
|
||||
**排序规则**：
|
||||
|
||||
1. 按综合得分降序排列
|
||||
2. 同分时 online 状态优先
|
||||
3. 最多展示 5 个候选
|
||||
综合得分降序；同分 online 优先；最多展示 5 个候选。
|
||||
|
||||
### 阶段 5：结果展示
|
||||
|
||||
**有匹配结果时**：
|
||||
|
||||
```
|
||||
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。
|
||||
```
|
||||
|
||||
**无匹配结果时**：
|
||||
|
||||
```
|
||||
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？
|
||||
```
|
||||
|
||||
**浏览模式**（用户要求查看所有）：
|
||||
|
||||
```
|
||||
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？
|
||||
```
|
||||
- **有匹配**：列出候选，每个含名称、描述、关键技能、状态、匹配度，请用户选择或进一步细化需求。例如「1. 法律顾问助手（92%）——合同审查与法律风险评估；技能：合同审查 / 风险评估 / 法律研究；在线」。
|
||||
- **无匹配**：告知未找到，给三选项——用更具体描述重试 / 创建新专业智能体（衔接 create-agent）/ 浏览全部。
|
||||
- **浏览模式**（用户要看全部）：按在线 / 离线分组列出名称 + 描述，问是否要看某个的详情。
|
||||
|
||||
### 阶段 6：引导选择
|
||||
|
||||
**用户选择后的操作**：
|
||||
- 选中某智能体 → 切换到该智能体的对话，传递用户需求上下文（source / target / user_intent）。
|
||||
- 要求了解更多 → `ManageAgent(action='get', id)` 取详情（名称 / 描述 / 状态 / 版本 / 技能数 / 工具数 / Git 状态），以自然语言或表格呈现关键信息，问是否对话。
|
||||
- 不满意候选 → 引导细化需求或建议创建新 Agent。
|
||||
- 选"创建新的" → 调 create-agent 技能，传递已收集的需求信息。
|
||||
|
||||
| 用户选择 | 后续操作 |
|
||||
| ---------------- | ----------------------------------------------------------------- |
|
||||
| 选择了某个智能体 | 切换到该智能体的对话，传递用户需求上下文 |
|
||||
| 要求了解更多 | 调用 `ManageAgent(action='get', id='<agent-id>')` 获取详情，展示结构化信息（见下方） |
|
||||
| 不满意候选 | 引导用户细化需求或建议创建新 Agent |
|
||||
| 选择"创建新的" | 调用 create-agent 技能，传递已收集的需求信息 |
|
||||
### 协作与错误处理
|
||||
|
||||
**"了解更多"的实现**：
|
||||
|
||||
调用 `ManageAgent(action='get', id='<agent-id>')` 获取指定智能体的详情：
|
||||
|
||||
```
|
||||
ManageAgent(action='get', id='legal-assistant')
|
||||
```
|
||||
|
||||
**返回内容中的关键字段**：
|
||||
|
||||
- 名称、描述、状态
|
||||
- 版本
|
||||
- 技能数 / 工具数
|
||||
- Git 仓库状态
|
||||
|
||||
> `get` 为只读查询，无需用户确认、免审批。目标不存在时返回错误「智能体不存在: <id>」。
|
||||
|
||||
向用户展示时，以自然语言/表格形式呈现关键信息：
|
||||
|
||||
```
|
||||
「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） |
|
||||
|
||||
需要与这个智能体对话吗？
|
||||
```
|
||||
|
||||
**切换上下文传递**：
|
||||
|
||||
```yaml
|
||||
context_handoff:
|
||||
source_agent: desirecore
|
||||
target_agent: legal-assistant
|
||||
user_intent: '帮我审查这份合同的风险点'
|
||||
```
|
||||
|
||||
### 与其他技能的协作
|
||||
|
||||
| 协作技能 | 协作方式 |
|
||||
| --------------- | -------------------------------------------------- |
|
||||
| create-agent | 无匹配时建议创建新 Agent，传递用户需求作为初始信息 |
|
||||
| task-management | 匹配成功后可自动创建任务并分配给目标 Agent |
|
||||
|
||||
### 错误处理
|
||||
|
||||
| 错误场景 | 处理方式 |
|
||||
| --------------------- | -------------------------------- |
|
||||
| 工具调用失败 | 提示错误信息，建议稍后重试 |
|
||||
| Agent 列表为空 | 引导用户创建第一个智能体 |
|
||||
| 用户描述过于模糊 | 追问具体需求，提供领域选项引导 |
|
||||
| 目标智能体不存在 | `get` 返回「智能体不存在: <id>」时，回退到 `list` 重新确认可用智能体 |
|
||||
| 推荐的 Agent 状态异常 | 标注状态，建议选择其他在线 Agent |
|
||||
|
||||
### 权限要求
|
||||
|
||||
- 通过内置工具 `ManageAgent` 完成智能体检索与详情查询
|
||||
- `list` / `get` 均为只读查询，无需用户确认、免审批，无风险
|
||||
|
||||
### 依赖
|
||||
|
||||
- 内置工具 `ManageAgent`（`action='list'` 检索列表、`action='get'` 查询详情）
|
||||
- 协作：无匹配衔接 create-agent（传需求作初始信息）；匹配成功后可创建任务并分配给目标 Agent。
|
||||
- 错误：工具调用失败 → 提示错误、建议重试；Agent 列表为空 → 引导创建第一个智能体；需求过于模糊 → 追问并给领域选项引导；`get` 返回「智能体不存在: <id>」→ 回退 `list` 重新确认可用智能体；推荐 Agent 状态异常 → 标注状态并建议选在线的。
|
||||
- `list` / `get` 均为只读、免审批、无风险，一律经 `ManageAgent` 完成。
|
||||
|
||||
Reference in New Issue
Block a user