Trust Infrastructure for AI Agents - Like SSL/TLS for agent-to-agent communication. 77 security tests, cryptographic certificates, and Trust Handshake Protoc...
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install agentshield-audit或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install agentshield-audit⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/agentshield-audit/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
--- name: agentshield version: 1.0.23 description: Trust Infrastructure for AI Agents - Like SSL/TLS for agent-to-agent communication. 77 security tests, cryptographic certificates, and Trust Handshake Protocol for establishing secure channels between agents. triggers: ["audit my agent", "get security certificate", "verify agent", "activate AgentShield", "security audit", "trust handshake", "verify peer agent"] ---
The trust layer for the agent economy. Like SSL/TLS, but for AI agents.
🔐 Cryptographic Identity - Ed25519 signing keys 🤝 Trust Handshake Protocol - Mutual verification before communication 📋 Public Trust Registry - Reputation scores & track records ✅ 77 Security Tests - Comprehensive vulnerability assessment
🔒 Privacy Disclosure: See PRIVACY.md for detailed data handling information.
---
Agents need to communicate with other agents (API calls, data sharing, task delegation). But how do you know if another agent is trustworthy?
Without a trust layer, agent-to-agent communication is like HTTP without SSL - unsafe and unverifiable.
---
AgentShield provides the trust layer for agent-to-agent communication:
52 Live Attack Vectors:
25 Static Security Checks:
Result: Security score (0-100) + Tier (VULNERABLE → HARDENED)
Agent A wants to communicate with Agent B:
# Step 1: Both agents get certified
python3 initiate_audit.py --auto
# Step 2: Agent A initiates handshake with Agent B
python3 handshake.py --target agent_B_id
# Step 3: Both agents sign challenges
# (Automatic in v1.0.13+)
# Step 4: Receive shared session key
# → Now you can communicate securely!
What you get:
---
clawhub install agentshield
# Install Python dependencies (required!)
pip3 install -r requirements.txt
cd ~/.openclaw/workspace/skills/agentshield*/
# Auto-detect agent name from IDENTITY.md/SOUL.md
python3 initiate_audit.py --auto
# Or manual:
python3 initiate_audit.py --name "MyAgent" --platform telegram
Output:
agent_xxxxxpython3 verify_peer.py agent_yyyyy
# Initiate handshake
python3 handshake.py --target agent_yyyyy
# Result: Shared session key for encrypted communication
---
Before: Agent A calls Agent B's API - no way to verify B's integrity With AgentShield: Agent A checks Agent B's certificate + handshake → Verified communication
Before: Orchestrator spawns sub-agents - can't verify they're safe With AgentShield: All sub-agents certified → Orchestrator knows they're trusted
Before: Download random agents from the internet - no trust guarantees With AgentShield: Browse Trust Registry → Only hire VERIFIED agents
Before: Share sensitive data with another agent - hope it doesn't leak With AgentShield: Handshake → Encrypted session key → Secure data transfer
---
✅ All 77 tests run locally - Your system prompts NEVER leave your device ✅ Private keys stay local - Only public keys transmitted ✅ Human-in-the-Loop - Explicit consent before reading IDENTITY.md/SOUL.md ✅ No environment scanning - Doesn't scan for API tokens
What goes to the server:
What stays local:
AGENTSHIELD_API=https://agentshield.live # API endpoint
AGENT_NAME=MyAgent # Override auto-detection
OPENCLAW_AGENT_NAME=MyAgent # OpenClaw standard
---
{
"agent_id": "agent_xxxxx",
"public_key": "...",
"security_score": 85,
"tier": "PATTERNS_CLEAN",
"issued_at": "2026-03-10",
"expires_at": "2026-06-08"
}
agentshield.live/verify/agent_xxxxx- Age (longer = more trust) - Verification count - Handshake success rate - Days active
{
"handshake_id": "hs_xxxxx",
"requester": "agent_A",
"target": "agent_B",
"status": "completed",
"session_key": "...",
"completed_at": "2026-03-10T20:00:00Z"
}
---
| Script | Purpose | |--------|---------| | initiate_audit.py | Run 77 security tests & get certified | | handshake.py | Trust handshake with another agent | | verify_peer.py | Check another agent's certificate | | show_certificate.py | Display your certificate | | agentshield_tester.py | Standalone test suite (advanced) |
---
---
Current (v1.0.13):
Coming Soon:
---
---
AgentShield is SSL/TLS for AI agents.
Get certified → Verify others → Establish trust handshakes → Communicate securely.
# 1. Get certified
python3 initiate_audit.py --auto
# 2. Handshake with another agent
python3 handshake.py --target agent_xxxxx
...安装 Agentshield Audit 后,可以对 AI 说这些话来触发它
Help me get started with Agentshield Audit
Explains what Agentshield Audit does, walks through the setup, and runs a quick demo based on your current project
Use Agentshield Audit to trust Infrastructure for AI Agents - Like SSL/TLS for agent-to-agen...
Invokes Agentshield Audit with the right parameters and returns the result directly in the conversation
What can I do with Agentshield Audit in my developer & devops workflow?
Lists the top use cases for Agentshield Audit, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/agentshield-audit/ 目录(个人级,所有项目可用),或 .claude/skills/agentshield-audit/(项目级)。重启 AI 客户端后,用 /agentshield-audit 主动调用,或让 AI 根据上下文自动发现并使用。
Agentshield Audit 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Agentshield Audit 可免费安装使用。请查阅仓库了解许可证信息。
Trust Infrastructure for AI Agents - Like SSL/TLS for agent-to-agent communication. 77 security tests, cryptographic certificates, and Trust Handshake Protoc...
Agentshield Audit 属于「Developer & DevOps」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my developer & devops tasks using Agentshield Audit
Identifies repetitive steps in your workflow and sets up Agentshield Audit to handle them automatically