AgentMesh Governance
AI agent governance, trust scoring, and policy enforcement powered by AgentMesh. Activate when: (1) user wants to enforce token limits, tool restrictions, or...
技能说明
name: agentmesh-governance description: > AI agent governance, trust scoring, and policy enforcement powered by AgentMesh. Activate when: (1) user wants to enforce token limits, tool restrictions, or content policies on agent actions, (2) checking an agent's trust score before delegation or collaboration, (3) verifying agent identity with Ed25519 cryptographic DIDs, (4) auditing agent actions with tamper-evident Merkle chain logs, (5) user asks about agent safety, governance, compliance, or trust. Enterprise-grade: 1,600+ tests, merged into Dify (65K★), LlamaIndex (47K★), Microsoft Agent-Lightning (15K★). version: 1.0.0 metadata: openclaw: requires: bins: - python3 - pip emoji: "🛡️" homepage: https://github.com/imran-siddique/agentmesh-integrations/tree/master/openclaw-skill
AgentMesh Governance — Trust & Policy for OpenClaw Agents
Zero-trust governance layer for OpenClaw agents. Enforce policies, verify identities, score trust, and maintain tamper-evident audit logs — all from your agent's command line.
Setup
Install the AgentMesh governance CLI:
pip install agentmesh-governance
If
agentmesh-governanceis not yet on PyPI, install directly from source:pip install "agentmesh @ git+https://github.com/imran-siddique/agent-mesh.git"
Scripts
All scripts are in scripts/. They wrap the governance engine and output JSON results.
Check Policy Compliance
Evaluate an action against a governance policy before execution:
scripts/check-policy.sh --action "web_search" --tokens 1500 --policy policy.yaml
Returns JSON with allowed: true/false, any violations, and recommendations.
Use this before executing any tool call to enforce limits.
Get Trust Score
Check an agent's current trust score (0.0 – 1.0):
scripts/trust-score.sh --agent "research-agent"
Returns the composite trust score with breakdown across 5 dimensions: policy compliance, resource efficiency, output quality, security posture, collaboration health.
Verify Agent Identity
Verify an agent's Ed25519 cryptographic identity before trusting its output:
scripts/verify-identity.sh --did "did:agentmesh:abc123" --message "hello" --signature "base64sig"
Returns verified: true/false. Use when receiving data from another agent.
Record Interaction
Update trust scores after collaborating with another agent:
scripts/record-interaction.sh --agent "writer-agent" --outcome success
scripts/record-interaction.sh --agent "writer-agent" --outcome failure --severity 0.1
Success adds +0.01 to trust score. Failure subtracts the severity value. Agents dropping below the minimum threshold (default 0.5) are auto-blocked.
Audit Log
View tamper-evident audit trail with Merkle chain verification:
scripts/audit-log.sh --last 20
scripts/audit-log.sh --agent "research-agent" --verify
The --verify flag checks Merkle chain integrity — any tampering is detected.
Generate Identity
Create a new Ed25519 cryptographic identity (DID) for your agent:
scripts/generate-identity.sh --name "my-agent" --capabilities "search,summarize,write"
Returns your agent's DID, public key, and capability manifest.
Policy File Format
Create a policy.yaml to define governance rules:
name: production-policy
max_tokens: 4096
max_tool_calls: 10
allowed_tools:
- web_search
- file_read
- summarize
blocked_tools:
- shell_exec
- file_delete
blocked_patterns:
- "rm -rf"
- "DROP TABLE"
- "BEGIN CERTIFICATE"
confidence_threshold: 0.7
require_human_approval: false
When to Use This Skill
- Before tool execution: Run
check-policy.shto enforce limits - Before trusting another agent's output: Run
verify-identity.sh - After collaboration: Run
record-interaction.shto update trust - Before delegation: Check
trust-score.sh— don't delegate to agents below 0.5 - For compliance: Run
audit-log.sh --verifyto prove execution integrity - On setup: Run
generate-identity.shto create your agent's DID
What It Enforces
| Policy | Description |
|---|---|
| Token limits | Cap per-action and per-session token usage |
| Tool allowlists | Only explicitly permitted tools can execute |
| Tool blocklists | Dangerous tools are blocked regardless |
| Content patterns | Block regex patterns (secrets, destructive commands, PII) |
| Trust thresholds | Minimum trust score required for delegation |
| Human approval | Gate critical actions behind human confirmation |
Architecture
This skill bridges the OpenClaw agent runtime with the AgentMesh governance engine:
OpenClaw Agent → SKILL.md scripts → AgentMesh Engine
├── GovernancePolicy (enforcement)
├── TrustEngine (5-dimension scoring)
├── AgentIdentity (Ed25519 DIDs)
└── MerkleAuditChain (tamper-evident logs)
Part of the Agent Ecosystem: AgentMesh · Agent OS · Agent SRE
如何使用「AgentMesh Governance」?
- 打开小龙虾AI(Web 或 iOS App)
- 点击上方「立即使用」按钮,或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「AgentMesh Governance」技能完成任务
- 结果即时呈现,支持继续对话优化