market/skills/create-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
create-agent	通过多轮对话收集需求，调用 HTTP API 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。	2.4.2	meta	low	enabled	true	agent creation meta	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 创建智能体 通过自然语言对话收集需求，一键创建专业化数字智能体 通过多轮对话收集需求，调用 HTTP API 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。 ./SKILL.zh-CN.md sha256:cf21692224334fce human name short_desc description body source_hash translated_by translated_at Create Agent Collect requirements through natural-language conversation and create a specialized digital Agent in one step Collect requirements through multi-turn conversation and call the HTTP API 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. ./SKILL.md sha256:cf21692224334fce 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="ca-a" x1="2" y1="7" x2="16" y2="21" gradientUnits="userSpaceOnUse"><stop stop-color="#34C759"/><stop offset="1" stop-color="#007AFF"/></linearGradient></defs><circle cx="9" cy="7" r="4" fill="url(#ca-a)" fill-opacity="0.15" stroke="url(#ca-a)" stroke-width="1.5"/><path d="M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2" fill="url(#ca-a)" fill-opacity="0.1" stroke="url(#ca-a)" stroke-width="1.5"/><circle cx="19" cy="11" r="4" fill="#34C759" fill-opacity="0.15"/><line x1="19" y1="8.5" x2="19" y2="13.5" stroke="#34C759" stroke-width="2" stroke-linecap="round"/><line x1="16.5" y1="11" x2="21.5" y2="11" stroke="#34C759" stroke-width="2" stroke-linecap="round"/></svg> productivity name verified DesireCore Official true latest

create-agent skill

L0: One-Sentence Summary

Collect requirements through natural-language conversation and call the HTTP API to create a specialized digital Agent.

L1: Overview and Use Cases

Capability Description

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 POST /api/agents to complete the creation.

Use Cases

• The user wants a digital assistant for a specialty domain (e.g. legal advisor, financial analyst)
• An enterprise needs to quickly deploy a customized business Agent
• A developer needs to quickly produce an Agent prototype from a template

Core Value

• Lower the barrier: No programming knowledge required; create an Agent through conversation
• Specialization: Generate appropriate persona and principles based on a domain template
• Governable: The created repository conforms to the AgentFS v2 spec and supports version management

L2: Detailed Specification

Conversation Flow

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   意图识别    │ ──→ │   需求收集    │ ──→ │   内容生成    │
└──────────────┘     └──────────────┘     └──────────────┘
                                                  │
                                                  ↓
┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   回执生成    │ ←── │   API 创建   │ ←── │   用户确认    │
└──────────────┘     └──────────────┘     └──────────────┘

Stage 1: Intent Recognition

Trigger conditions (any one is sufficient):

• The user explicitly says "create an Agent" or "build me an assistant"
• The user describes needing professional help in some domain that the current Agent doesn't have
• The user asks "can you help me grow a..."

Output: Confirm the user's create intent and proceed to requirement collection.

Stage 2: Requirement Collection

Required information:

Field	Description	Example Probing Question
name	Agent name	"What name would you like to give this Agent?"
role	Core duty	"What's its main responsibility?"
target_users	Target users	"Who will use this Agent?"
domain	Specialty area	"What expertise does it need?"

Optional information:

Field	Description	Default
style	Communication style	Determined by domain template
boundaries	Off-limits / red lines	Determined by domain template
language	Primary language	Chinese

Collection strategy:

• Prefer to infer information from the user's natural description
• Only probe for required fields the user hasn't mentioned
• Ask at most 2 follow-up questions per turn

Stage 3: Content Generation

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.

Persona fields (all fields optional; missing ones are auto-filled by the system):

Layer	Field	Description
L0	—	One-sentence core identity
L1	role	Role positioning
L1	personality	Personality trait tags
L1	communication_style	Communication style
L2	—	Specialty domain, core values, decision preferences (free-form)

Principles fields (all also optional):

Layer	Field	Description
L0	—	One-sentence top-level principle
L1	must_do	Things the Agent must do
L1	must_not	Things the Agent must never do (safety red lines)
L1	priority	Priority ordering
L2	—	Governance principles, escalation rules (free-form)

Domain matching reference:

Domain Keywords	Recommended personality	Default must_not
Legal, contract, legal affairs	Professional, rigorous, prudent	No litigation representation; not a substitute for formal legal advice
Finance, accounting, investment	Precise, analytical, conservative	No investment advice; do not handle real transactions
Code, development, architecture	Logical, pragmatic, direct	No direct production access; do not store credentials
General / other	Friendly, helpful	Standard safety norms

Stage 4: User Confirmation

When showing the preview to the user, present each field in natural language / table form. Do not show raw markdown source:

About to create Agent:

Name: Legal Advisor Assistant Description: A digital Agent focused on contract review and legal risk assessment

---

Persona

Field	Content
Core identity	You are Legal Advisor Assistant, focused on contract review and legal risk assessment
Role positioning	A digital legal advisor focused on contract review and legal risk assessment
Personality	Professional, rigorous, prudent
Communication style	Use legal terminology accurately while also providing plain-language explanations

Principles

Field	Content
Top principle	User interest is the highest priority; not a substitute for formal legal advice
Must do	Cite statutes accurately, mark uncertainty, recommend consulting a professional lawyer
Must not	Provide litigation representation, substitute for formal legal advice, leak user consultations
Priority	User safety > Accuracy > Efficiency

---

Confirm creation? (Confirm / Modify / Cancel)

"Modify" branch handling:

When the user chooses "Modify":

1. Ask the user which field to modify (e.g. "Which one do you want to modify?")
2. The user identifies the field to modify (e.g. "change personality to something more lively")
3. The Agent re-collects content for that field
4. Update the corresponding field in the preview
5. Show the full preview again → re-enter the confirmation flow

Stage 5: Call API to Create

API endpoint: POST /api/agents

Request body (structured format):

{
  "name": "法律顾问小助手",
  "description": "专注于合同审查和法律风险评估的数字智能体",
  "persona": {
    "L0": "你是法律顾问小助手，专注于合同审查和法律风险评估的数字智能体。",
    "L1": {
      "role": "专注于合同审查和法律风险评估的数字法律顾问",
      "personality": ["专业", "严谨", "审慎"],
      "communication_style": "准确使用法律术语，同时提供通俗解释"
    }
  },
  "principles": {
    "L0": "以用户利益为最高优先级，不替代正式法律意见。",
    "L1": {
      "must_do": ["准确引用法律条文", "标注不确定性", "建议咨询专业律师"],
      "must_not": ["提供诉讼代理", "替代正式法律意见", "泄露用户法律咨询内容"],
      "priority": "用户安全 > 准确性 > 效率"
    }
  }
}

Minimal create (only name; the rest is auto-generated):

{ "name": "我的助手" }

Basic create (name + description; description auto-fills persona L0):

{ "name": "法律顾问", "description": "专注合同审查" }

Any unprovided fields are auto-filled with sensible defaults by the system. persona and principles also accept raw markdown strings (backward compatible).

Optional: To specify a slug ID, generate a sensible kebab-case slug from name (e.g. "Legal Advisor" → "legal-advisor") and include "id": "<slug>" in the request body. If not specified, the system auto-generates one from name.

Successful response (201 Created):

{
  "success": true,
  "agentId": "fa-lv-gu-wen-xiao-zhu-shou",
  "agent": {
    "id": "fa-lv-gu-wen-xiao-zhu-shou",
    "name": "法律顾问小助手",
    "description": "专注于合同审查和法律风险评估的数字智能体",
    "skillsCount": 0,
    "toolsCount": 0,
    "status": "offline"
  }
}

The agent field in the response contains the full info of the newly created Agent and can be used directly in the receipt.

Stage 6: Receipt Generation

Receipt report:

After successful creation, present the receipt in a user-friendly way (do not expose internal paths or technical details):

Agent "Legal Advisor Assistant" has been created!

Next steps:

• Start a conversation right away
• Add skills to make it more powerful
• Adjust its personality or behavior rules

Background Knowledge

AgentFS repository structure, troubleshooting points, and protected paths are detailed in _agentfs-background.md and _protected-paths.yaml.

Error Handling

Error Code	Scenario	Handling
400	Missing name or invalid ID format	Ask user to check input
409	Agent ID already exists	Suggest using another name
500	Internal server error	Ask user to try again later

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
• Creation requires user confirmation

Dependencies

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