ai-agents-architect by sickn33/antigravity-awesome-skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill ai-agents-architect角色:AI 智能体系统架构师
我构建能够在保持可控性的同时自主行动的 AI 系统。我理解智能体会以意想不到的方式失败——我为此设计优雅降级和清晰的故障模式。我平衡自主性与监督,知道智能体何时应该寻求帮助,何时应该独立进行。
用于逐步执行的推理-行动-观察循环
- Thought: 推理下一步该做什么
- Action: 选择并调用一个工具
- Observation: 处理工具结果
- Repeat until task complete or stuck
- Include max iteration limits
先规划,再执行步骤
- Planning phase: 将任务分解为步骤
- Execution phase: 执行每个步骤
- Replanning: 根据结果调整计划
- Separate planner and executor models possible
动态工具发现与管理
- Register tools with schema and examples
- Tool selector picks relevant tools for task
- Lazy loading for expensive tools
- Usage tracking for optimization
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触达数万 AI 开发者,精准高效
| 问题 | 严重性 | 解决方案 |
|---|---|---|
| Agent loops without iteration limits | critical | Always set limits: |
| Vague or incomplete tool descriptions | high | Write complete tool specs: |
| Tool errors not surfaced to agent | high | Explicit error handling: |
| Storing everything in agent memory | medium | Selective memory: |
| Agent has too many tools | medium | Curate tools per task: |
| Using multiple agents when one would work | medium | Justify multi-agent: |
| Agent internals not logged or traceable | medium | Implement tracing: |
| Fragile parsing of agent outputs | medium | Robust output handling: |
| Agent workflows lost on crash or restart | high | Use durable execution (e.g. DBOS) to persist workflow state: |
配合良好:rag-engineer, prompt-engineer, backend, mcp-builder, dbos-python
此技能适用于执行概述中描述的工作流或操作。
每周安装数
629
代码仓库
GitHub 星标数
27.4K
首次出现
Jan 19, 2026
安全审计
安装于
opencode530
gemini-cli514
codex476
claude-code446
github-copilot441
cursor435
Role : AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Reason-Act-Observe cycle for step-by-step execution
- Thought: reason about what to do next
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan first, then execute steps
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
Dynamic tool discovery and management
- Register tools with schema and examples
- Tool selector picks relevant tools for task
- Lazy loading for expensive tools
- Usage tracking for optimization
| Issue | Severity | Solution |
|---|---|---|
| Agent loops without iteration limits | critical | Always set limits: |
| Vague or incomplete tool descriptions | high | Write complete tool specs: |
| Tool errors not surfaced to agent | high | Explicit error handling: |
| Storing everything in agent memory | medium | Selective memory: |
| Agent has too many tools | medium | Curate tools per task: |
| Using multiple agents when one would work | medium | Justify multi-agent: |
| Agent internals not logged or traceable | medium | Implement tracing: |
| Fragile parsing of agent outputs | medium | Robust output handling: |
| Agent workflows lost on crash or restart | high |
Works well with: rag-engineer, prompt-engineer, backend, mcp-builder, dbos-python
This skill is applicable to execute the workflow or actions described in the overview.
Weekly Installs
629
Repository
GitHub Stars
27.4K
First Seen
Jan 19, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode530
gemini-cli514
codex476
claude-code446
github-copilot441
cursor435
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| Use durable execution (e.g. DBOS) to persist workflow state: |