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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.
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GitHub
@@ -2,7 +2,7 @@
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name: create-agent
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description: >-
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通过多轮对话收集需求，调用 ManageAgent 内置工具创建新的 AgentFS v2 智能体，支持自定义 persona 和 principles。Use when 用户要求创建新智能体、培养某领域助手、或快速基于模板生成可治理 Agent。
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version: 2.5.0
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version: 2.5.1
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type: meta
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risk_level: low
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status: enabled
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@@ -13,7 +13,7 @@ tags:
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- meta
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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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@@ -26,7 +26,7 @@ metadata:
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description: >-
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通过多轮对话收集需求，调用 ManageAgent 内置工具创建新的 AgentFS v2 智能体，支持自定义 persona 和 principles。Use when 用户要求创建新智能体、培养某领域助手、或快速基于模板生成可治理 Agent。
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body: ./SKILL.zh-CN.md
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source_hash: sha256:43425a25973ca933
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source_hash: sha256:a119427bed3bcd72
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translated_by: human
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en-US:
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name: Create Agent
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@@ -34,9 +34,9 @@ metadata:
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description: >-
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Collect requirements through multi-turn conversation and call the ManageAgent builtin tool to create a new AgentFS v2 Agent, with customizable persona and principles. Use when the user asks to create a new Agent, raise a domain assistant, or quickly produce a governable Agent from a template.
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body: ./SKILL.md
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source_hash: sha256:43425a25973ca933
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source_hash: sha256:a119427bed3bcd72
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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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@@ -66,235 +66,74 @@ market:
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Collect requirements through natural-language conversation and call the ManageAgent builtin tool to create a specialized digital Agent.
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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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Meta-skill: gather requirements over multi-turn conversation → generate persona/principles → land via `ManageAgent(action='create')`. Use it to raise a domain specialist for the user (legal advisor, financial analyst, etc.). Its value is what the tool can't give: domain-tailored persona/principles generation + a pre-create preview confirmation.
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create-agent is a **Meta-Skill** that gives DesireCore the ability to create other Agents. It collects user requirements through multi-turn conversation, generates persona and principles content, and calls the `ManageAgent` builtin tool to complete the creation.
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## L2: Detailed Spec
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### Use Cases
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- The user wants a digital assistant for a specialty domain (e.g. legal advisor, financial analyst)
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- An enterprise needs to quickly deploy a customized business Agent
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- A developer needs to quickly produce an Agent prototype from a template
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### Core Value
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- **Lower the barrier**: No programming knowledge required; create an Agent through conversation
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- **Specialization**: Generate appropriate persona and principles based on a domain template
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- **Governable**: The created repository conforms to the AgentFS v2 spec and supports version management
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## L2: Detailed Specification
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### Conversation Flow
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```
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ 意图识别 │ ──→ │ 需求收集 │ ──→ │ 内容生成 │
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└──────────────┘ └──────────────┘ └──────────────┘
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│
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↓
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ 回执生成 │ ←── │ 工具创建 │ ←── │ 用户确认 │
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└──────────────┘ └──────────────┘ └──────────────┘
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```
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Flow: intent recognition → requirement gathering → content generation → user confirmation → creation → receipt.
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### Stage 1: Intent Recognition
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**Trigger conditions** (any one is sufficient):
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Trigger (any): user explicitly says "create an Agent / make me an assistant"; describes a domain-specific need the current Agent lacks; asks "can you raise a …". Confirm intent, then move to requirement gathering.
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- The user explicitly says "create an Agent" or "build me an assistant"
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- The user describes needing professional help in some domain that the current Agent doesn't have
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- The user asks "can you help me grow a..."
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### Stage 2: Requirement Gathering
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**Output**: Confirm the user's create intent and proceed to requirement collection.
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### Stage 2: Requirement Collection
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**Required information**:
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| Field | Description | Example Probing Question |
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| -------------- | -------------- | ---------------------------------------------- |
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| `name` | Agent name | "What name would you like to give this Agent?" |
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| `role` | Core duty | "What's its main responsibility?" |
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| `target_users` | Target users | "Who will use this Agent?" |
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| `domain` | Specialty area | "What expertise does it need?" |
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**Optional information**:
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| Field | Description | Default |
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| ------------ | --------------------- | -------------------------------- |
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| `style` | Communication style | Determined by domain template |
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| `boundaries` | Off-limits / red lines| Determined by domain template |
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| `language` | Primary language | Chinese |
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**Collection strategy**:
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- Prefer to infer information from the user's natural description
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- Only probe for required fields the user hasn't mentioned
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- Ask at most 2 follow-up questions per turn
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- Required: `name`, `role` (core responsibility), `target_users`, `domain` (specialty).
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- Optional: `style` (communication tone), `boundaries` (red lines), `language` (default Chinese) — defaults derived from the domain template.
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- Strategy: prefer inferring from the user's natural description; only ask for missing required fields; at most 2 questions per turn.
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### Stage 3: Content Generation
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Based on the collected requirements, assemble structured persona and principles data. **Do not output raw markdown**; instead, organize by field and present to the user.
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Organize persona and principles as structured fields (do not emit raw markdown; present field-by-field). Leave uncollected fields empty for the system to fill with defaults:
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**Persona fields** (all fields optional; missing ones are auto-filled by the system):
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- **persona**: L0 one-sentence core identity; L1 `role` / `personality` (array of trait tags) / `communication_style`; L2 specialty, values, decision preferences (free-form).
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- **principles**: L0 one-sentence top principle; L1 `must_do` / `must_not` (safety red lines) / `priority`; L2 governance principles, escalation rules (free-form).
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| Layer | Field | Description |
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| ----- | --------------------- | ------------------------------------------------------------ |
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| L0 | — | One-sentence core identity |
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| L1 | `role` | Role positioning |
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| L1 | `personality` | Personality trait tags |
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| L1 | `communication_style` | Communication style |
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| L2 | — | Specialty domain, core values, decision preferences (free-form) |
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**Domain matching reference** (recommend personality and red lines by domain):
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**Principles fields** (all also optional):
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| Layer | Field | Description |
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| ----- | ---------- | -------------------------------------------- |
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| L0 | — | One-sentence top-level principle |
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| L1 | `must_do` | Things the Agent must do |
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| L1 | `must_not` | Things the Agent must never do (safety red lines) |
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| L1 | `priority` | Priority ordering |
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| L2 | — | Governance principles, escalation rules (free-form) |
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**Domain matching reference**:
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| Domain Keywords | Recommended personality | Default must_not |
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| -------------------------------- | -------------------------------- | ----------------------------------------------- |
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| Legal, contract, legal affairs | Professional, rigorous, prudent | No litigation representation; not a substitute for formal legal advice |
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| Finance, accounting, investment | Precise, analytical, conservative | No investment advice; do not handle real transactions |
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| Code, development, architecture | Logical, pragmatic, direct | No direct production access; do not store credentials |
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| General / other | Friendly, helpful | Standard safety norms |
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| Domain | Recommended personality | Default must_not |
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| --- | --- | --- |
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| Legal / contract | Professional, rigorous, prudent | No litigation representation; not a substitute for formal legal advice |
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| Finance / accounting / investment | Precise, analytical, conservative | No investment advice; no real transactions |
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| Code / development / architecture | Logical, pragmatic, direct | No direct production access; no credential storage |
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| General / other | Friendly, helpful | Standard safety norms |
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### Stage 4: User Confirmation
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When showing the preview to the user, present each field in natural language / table form. **Do not show raw markdown source**:
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Present the preview in natural language / tables (name, description, persona, principles; **no raw markdown source**), e.g.:
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> About to create Agent:
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>
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> **Name**: Legal Advisor Assistant
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> **Description**: A digital Agent focused on contract review and legal risk assessment
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>
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> ---
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>
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> **Persona**
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>
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> | Field | Content |
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> | --------------------- | ------------------------------------------------------------------------ |
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> | Core identity | You are Legal Advisor Assistant, focused on contract review and legal risk assessment |
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> | Role positioning | A digital legal advisor focused on contract review and legal risk assessment |
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> | Personality | Professional, rigorous, prudent |
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> | Communication style | Use legal terminology accurately while also providing plain-language explanations |
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>
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> **Principles**
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>
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> | Field | Content |
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> | ----------------- | -------------------------------------------------------------------- |
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> | Top principle | User interest is the highest priority; not a substitute for formal legal advice |
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> | Must do | Cite statutes accurately, mark uncertainty, recommend consulting a professional lawyer |
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> | Must not | Provide litigation representation, substitute for formal legal advice, leak user consultations |
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> | Priority | User safety > Accuracy > Efficiency |
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>
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> ---
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>
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> About to create "Legal Advisor Assistant" — focused on contract review and legal risk assessment.
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> **Persona**: digital legal advisor; professional, rigorous, prudent; uses legal terms accurately with plain-language explanations.
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> **Principles**: user interest first, not a substitute for formal legal advice; must — cite statutes / mark uncertainty / recommend consulting a lawyer; must not — litigation representation / leaking consultations; priority: user safety > accuracy > efficiency.
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> Confirm creation? (Confirm / Modify / Cancel)
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**"Modify" branch handling**:
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When the user chooses "Modify":
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1. Ask the user which field to modify (e.g. "Which one do you want to modify?")
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2. The user identifies the field to modify (e.g. "change personality to something more lively")
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3. The Agent re-collects content for that field
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4. Update the corresponding field in the preview
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5. Show the full preview again → re-enter the confirmation flow
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If the user picks "Modify": ask which field → re-collect that field → update the preview → show and confirm again.
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### Stage 5: Create via ManageAgent
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**Tool call** (structured format):
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Structured call (persona/principles also accept markdown strings; unprovided fields are auto-filled):
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```
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ManageAgent({
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"action": "create",
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"name": "Legal Advisor Assistant",
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"description": "A digital Agent focused on contract review and legal risk assessment",
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"persona": {
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"L0": "You are Legal Advisor Assistant, a digital Agent focused on contract review and legal risk assessment.",
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"L1": {
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"role": "A digital legal advisor focused on contract review and legal risk assessment",
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"personality": ["Professional", "Rigorous", "Prudent"],
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"communication_style": "Use legal terminology accurately while also providing plain-language explanations"
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}
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},
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"principles": {
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"L0": "User interest is the highest priority; not a substitute for formal legal advice.",
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"L1": {
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"must_do": ["Cite statutes accurately", "Mark uncertainty", "Recommend consulting a professional lawyer"],
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"must_not": ["Provide litigation representation", "Substitute for formal legal advice", "Leak user consultations"],
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"priority": "User safety > Accuracy > Efficiency"
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}
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}
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"persona": { "L0": "…", "L1": { "role": "…", "personality": ["professional","rigorous","prudent"], "communication_style": "…" } },
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"principles": { "L0": "…", "L1": { "must_do": ["…"], "must_not": ["…"], "priority": "user safety > accuracy > efficiency" } }
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})
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```
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**Minimal create** (only `name`; the rest is auto-generated):
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- Minimal create needs only `{ "action": "create", "name": "My Assistant" }` (everything else auto-generated).
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- Optional `id` (kebab-case slug; `core` / `desirecore` are reserved core identifiers and cannot be used, including when the slug auto-generated from `name` collides), `config.llm` (llm delta only; sensitive fields like mcp_servers are rejected and must be adjusted via the UI after creation).
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- The Agent is registered and usable immediately; the returned ID works directly with ManageTeam / Delegate. If the tool errors (already exists / reserved identifier / non-whitelisted config field, etc.), explain the reason and retry after adjusting per the hint.
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```
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ManageAgent({ "action": "create", "name": "My Assistant" })
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```
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### Stage 6: Receipt
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**Basic create** (`name` + `description`; `description` auto-fills persona L0):
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Present in a user-friendly way (no internal paths / technical details): confirm success and suggest next steps — start chatting, add skills, adjust persona or rules.
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```
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ManageAgent({ "action": "create", "name": "Legal Advisor", "description": "Focused on contract review" })
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```
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### Background and Constraints
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Any unprovided fields are auto-filled with sensible defaults by the system. `persona` and `principles` also accept raw markdown strings (backward compatible).
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**Optional parameters**:
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- `id`: specify a kebab-case slug ID (e.g. "Legal Advisor" → "legal-advisor"). If not specified, the system auto-generates one from `name`. Note that `core` / `desirecore` are reserved core-agent identifiers and cannot be used (including when the slug auto-generated from `name` collides with them).
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- `config`: an agent.json config delta. Only the `llm` field is allowed (model, temperature, etc.); sensitive fields such as `mcp_servers` / `tool_permissions` are rejected and must be adjusted via the settings UI after creation.
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**Successful result**:
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> Agent "Legal Advisor Assistant" created (ID: legal-advisor-assistant), registered and ready. It can be used directly with ManageTeam or Delegate.
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The new Agent is registered online immediately after creation—no waiting or refresh needed; the returned ID can be used directly in subsequent ManageTeam / Delegate calls.
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### Stage 6: Receipt Generation
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**Receipt report**:
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After successful creation, present the receipt in a user-friendly way (do not expose internal paths or technical details):
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> Agent "Legal Advisor Assistant" has been created!
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>
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> **Next steps**:
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>
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> - Start a conversation right away
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> - Add skills to make it more powerful
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> - Adjust its personality or behavior rules
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### Background Knowledge
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> AgentFS repository structure, troubleshooting points, and protected paths are detailed in `_agentfs-background.md` and `_protected-paths.yaml`.
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### Error Handling
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| Error Scenario | Handling |
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| ------------------------------------------- | ------------------------------------------- |
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| Missing `name` or invalid ID format | Ask user to check input |
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| Agent ID already exists | Suggest another name or an explicit `id` |
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| ID hits a reserved core-agent identifier | Change the name or provide another `id` |
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| `config` contains non-whitelisted fields | Retry with only the `llm` field |
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### Permission Requirements
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- Always create via the `ManageAgent` builtin tool. **Never** call the local HTTP API, curl, or write AgentFS directories directly
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- The `create` action is exempt from system approval, but this skill's conversation flow still requires showing the preview and getting user confirmation first (Stage 4)
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### Dependencies
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- `ManageAgent` builtin tool (client ≥ 10.0.90)
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- AgentFS structure, troubleshooting, and protected paths: see `_agentfs-background.md` and `_protected-paths.yaml`.
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- Always create via `ManageAgent`; never use curl / HTTP or write AgentFS directories directly. Requires client ≥ 10.0.90.
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