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在 OpenClaw 中安装
/install agents
功能描述
Design, build, and deploy AI agents with architecture patterns, framework selection, memory systems, and production safety.
使用说明 (SKILL.md)
When to Use
Use when designing agent systems, choosing frameworks, implementing memory/tools, specifying agent behavior for teams, or reviewing agent security.
Quick Reference
| Topic | File |
|---|---|
| Architecture patterns & memory | architecture.md |
| Framework comparison | frameworks.md |
| Use cases by role | use-cases.md |
| Implementation patterns & code | implementation.md |
| Security boundaries & risks | security.md |
| Evaluation & debugging | evaluation.md |
Before Building — Decision Checklist
- Single purpose defined? If you can't say it in one sentence, split into multiple agents
- User identified? Internal team, end customer, or another system?
- Interaction modality? Chat, voice, API, scheduled tasks?
- Single vs multi-agent? Start simple — only add agents when roles genuinely differ
- Memory strategy? What persists within session vs across sessions vs forever?
- Tool access tiers? Which actions are read-only vs write vs destructive?
- Escalation rules? When MUST a human step in?
- Cost ceiling? Budget per task, per user, per month?
Critical Rules
- Start with one agent — Multi-agent adds coordination overhead. Prove single-agent insufficient first.
- Define escalation triggers — Angry users, legal mentions, confidence drops, repeated failures → human
- Separate read from write tools — Read tools need less approval than write tools
- Log everything — Tool calls, decisions, user interactions. You'll need the audit trail.
- Test adversarially — Assume users will try to break or manipulate the agent
- Budget by task type — Use cheaper models for simple tasks, expensive for complex
The Agent Loop (Mental Model)
OBSERVE → THINK → ACT → OBSERVE → ...
Every agent is this loop. The differences are:
- What it observes (context window, memory, tool results)
- How it thinks (direct, chain-of-thought, planning)
- What it can act on (tools, APIs, communication channels)
安全使用建议
This skill is documentation-only and internally coherent with its aim to teach how to design, implement, and secure agents. Before installing or relying on it: (1) note the publisher has no public homepage—if provenance matters, prefer skills with identifiable authors or an organization; (2) the skill contains code snippets and operational advice but will not run code or access secrets by itself—if you implement the patterns, follow the security.md checklist (sandbox tools, avoid putting secrets in prompts, require approvals for destructive actions); (3) the prompt-injection pattern found is part of the security discussion and not an active exploit, but remain cautious: never paste sensitive keys into any prompt or untrusted context and sandbox any code you copy from the implementation examples.
功能分析
Type: OpenClaw Skill
Name: agents
Version: 1.0.0
The OpenClaw AgentSkills skill bundle is a comprehensive educational resource focused on designing, building, deploying, and securing AI agents. All files, including SKILL.md, architecture.md, evaluation.md, frameworks.md, implementation.md, security.md, and use-cases.md, contain conceptual information, best practices, and illustrative code snippets for agent development. There is no evidence of prompt injection attempts against the OpenClaw agent, data exfiltration, malicious execution, persistence mechanisms, or obfuscation. On the contrary, the 'security.md' file explicitly details agent-specific attack vectors and robust mitigation strategies, demonstrating a strong emphasis on security awareness and responsible AI development.
能力评估
Purpose & Capability
Name/description match the contents: the package is documentation and checklists for designing, implementing, evaluating, and securing agents. It requests no binaries, env vars, or installs—appropriate for an instructional skill. Note: the skill metadata has no homepage and owner is an opaque ID; that reduces provenance but does not create an internal inconsistency.
Instruction Scope
SKILL.md and the included markdown files are guidance only and do not instruct the agent to read arbitrary system files, exfiltrate data, or call external endpoints. The instructions focus on architecture, testing, and security practices (including avoiding prompt injection). There is no vague open-ended instruction that would grant broad discretionary access.
Install Mechanism
No install spec and no code files — lowest-risk model. Nothing is downloaded or written to disk by the skill itself.
Credentials
The skill declares no required environment variables, credentials, or config paths. The guidance even warns against putting secrets in prompts and advises retrieving secrets from environment variables without exposing them. No disproportionate credential requests are present.
Persistence & Privilege
Defaults for invocation/persistence are normal (always:false, agent can invoke autonomously). The skill does not request persistent system presence or modification of other skills or global agent config. Nothing indicates privilege escalation.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install agents - 安装完成后，直接呼叫该 Skill 的名称或使用
/agents触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release: architecture patterns, frameworks comparison, use cases, implementation, security, evaluation
元数据
常见问题
Agents 是什么？
Design, build, and deploy AI agents with architecture patterns, framework selection, memory systems, and production safety. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1252 次。
如何安装 Agents？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install agents」即可一键安装，无需额外配置。
Agents 是免费的吗？
是的，Agents 完全免费（开源免费），可自由下载、安装和使用。
Agents 支持哪些平台？
Agents 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Agents？
由 Iván（@ivangdavila）开发并维护，当前版本 v1.0.0。
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