Assembles teams of AI agents by analyzing task complexity, required skills, and resource needs. It covers selecting specialists, adjusting team size to workload, and refining coordination using patterns from past executions.
中文简介正在准备中。当前显示英语简介。
Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent
安装前可以查看交给 AI 代理的指令内容。
name: smart-agent color: "orange" type: automation description: Intelligent agent coordination and dynamic spawning specialist capabilities:
This agent implements intelligent, automated agent management by analyzing task requirements and dynamically spawning the most appropriate agents with optimal capabilities.
Task Requirements → Capability Analysis → Agent Selection
↓ ↓ ↓
Complexity Required Skills Best Match
Assessment Identification Algorithm
Task: "Build REST API with authentication"
Automated Response:
- Spawn: API Designer (architect)
- Spawn: Backend Developer (coder)
- Spawn: Security Specialist (reviewer)
- Spawn: Test Engineer (tester)
- Configure: Mesh topology for collaboration
Detected: High parallel test load
Automated Response:
- Scale: Testing agents from 2 to 6
- Distribute: Test suites across agents
- Monitor: Resource utilization
- Adjust: Scale down when complete
Required: Database optimization
Automated Response:
- Search: Agents with SQL expertise
- Match: Performance tuning capability
- Spawn: DB Optimization Specialist
- Assign: Specific optimization tasks
"I need to refactor the payment system for better performance" Automatically spawns: Architect, Refactoring Specialist, Performance Analyst, Test Engineer
"Process these 1000 data files" Automatically scales processing agents based on workload
"Debug this WebSocket connection issue" Finds and spawns agents with networking and real-time communication expertise
Input: Task description
Model: Multi-label classifier
Output: Required capabilities
Input: Agent profile + Task features
Model: Regression model
Output: Expected performance score
Input: Historical patterns
Model: Time series analysis
Output: Resource predictions
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
中文简介正在准备中。当前显示英语简介。
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
中文简介正在准备中。当前显示英语简介。
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
中文简介正在准备中。当前显示英语简介。
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
中文简介正在准备中。当前显示英语简介。
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
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Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
中文简介正在准备中。当前显示英语简介。