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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)

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## 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:
yi-ge
2026-07-19 15:16:43 +08:00
committed by GitHub
parent 64d295a489
commit d52e61e041
13 changed files with 302 additions and 1804 deletions
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262
skills/discover-agent/SKILL.md
View File

@@ -1,7 +1,7 @@
---
name: discover-agent
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。
version: 2.6.0
version: 2.6.1
type: procedural
risk_level: low
status: enabled
@@ -12,7 +12,7 @@ tags:
- recommendation
metadata:
author: desirecore
updated_at: '2026-07-18'
updated_at: '2026-07-19'
i18n:
default_locale: en-US
source_locale: zh-CN
@@ -24,7 +24,7 @@ metadata:
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
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。
body: ./SKILL.zh-CN.md
source_hash: sha256:99bddbbaea15b194
source_hash: sha256:671b1b159567a5a3
translated_by: human
en-US:
name: Discover Agent
@@ -32,9 +32,9 @@ metadata:
description: >-
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.
body: ./SKILL.md
source_hash: sha256:99bddbbaea15b194
source_hash: sha256:671b1b159567a5a3
translated_by: ai:claude-fable-5
translated_at: '2026-07-18'
translated_at: '2026-07-19'
market:
icon: >-
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0
@@ -56,247 +56,51 @@ market:
required_client_version: 10.0.90
---
# discover-agent Skill
# discover-agent skill
## L0: One-sentence summary
## L0: One-Sentence Summary
Match and recommend the most suitable Agent from the registered Agents based on the user’s needs.
Match and recommend the most suitable Agent among registered Agents based on the user's need description.
## L1: Overview and use cases
## L1: Overview
### Capability description
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.
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.
## L2: Detailed Spec
### Use cases
Flow: need understanding → retrieval → match evaluation → ranking → presentation → guided selection.
- 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
### Stage 1: Need Understanding
### Core value
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).
- **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)
### Stage 2: Retrieval
## L2: Detailed specification
`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.
### Execution flow
### Stage 3: Match Evaluation
```
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ 需求理解 │ ──→ │ Agent 检索 │ ──→ │ 匹配评分 │
└──────────────┘ └──────────────┘ └──────────────┘
│
↓
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ 引导选择 │ ←── │ 结果展示 │ ←── │ 候选排序 │
└──────────────┘ └──────────────┘ └──────────────┘
```
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.
### Stage 1: Needs understanding
### Stage 4: Ranking
**Trigger conditions** (any one of the following):
Descending by overall score; ties broken by online status; show at most 5 candidates.
- 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
### Stage 5: Presentation
**Need parsing**:
- **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".
- **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.
- **Browse mode** (user wants to see all): list name + description grouped by online / offline, and ask whether to view details of any.
Extract the following dimensions from the user’s description:
### Stage 6: Guided Selection
| 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 |
- Chose an Agent → switch to that Agent's conversation, passing the user's need context (source / target / user_intent).
- 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.
- Unhappy 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 gathered requirements.
### Stage 2: Agent retrieval
### Collaboration and Error Handling
**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**:
1. Sort by overall score in descending order
2. If scores are tied, prefer online status
3. 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: <id>".
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**:
```yaml
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: <id>", 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)
- 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.
- 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.
- `list` / `get` are read-only, approval-free, and risk-free; always done via `ManageAgent`.