How do I install the Agentmesh Governance skill?

Run npx skillmds@latest add dvcrn/agentmesh-governance in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agentmesh Governance safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agentmesh Governance?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agentmesh Governance free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agentmesh Governance?

dvcrn (@dvcrn) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agentmesh Governance by dvcrn — Agent Skill

Name: Agentmesh Governance
Rating: 4.2 (5 reviews)
Author: dvcrn

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 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★).

dvcrn Updated Sep 8, 2026 32 repo stars

File contents

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-governance is 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.sh to enforce limits
Before trusting another agent's output: Run verify-identity.sh
After collaboration: Run record-interaction.sh to update trust
Before delegation: Check trust-score.sh — don't delegate to agents below 0.5
For compliance: Run audit-log.sh --verify to prove execution integrity
On setup: Run generate-identity.sh to 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

---name: agentmesh-governancedescription: 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★).---# AgentMesh Governance — Trust & Policy for OpenClaw AgentsZero-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.## SetupInstall the AgentMesh governance CLI:```bashpip install agentmesh-governance```> If `agentmesh-governance` is not yet on PyPI, install directly from source:> ```bash> pip install "agentmesh @ git+https://github.com/imran-siddique/agent-mesh.git"> ```## ScriptsAll scripts are in `scripts/`. They wrap the governance engine and output JSON results.### Check Policy ComplianceEvaluate an action against a governance policy before execution:```bashscripts/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 ScoreCheck an agent's current trust score (0.0 – 1.0):```bashscripts/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 IdentityVerify an agent's Ed25519 cryptographic identity before trusting its output:```bashscripts/verify-identity.sh --did "did:agentmesh:abc123" --message "hello" --signature "base64sig"```Returns `verified: true/false`. Use when receiving data from another agent.### Record InteractionUpdate trust scores after collaborating with another agent:```bashscripts/record-interaction.sh --agent "writer-agent" --outcome successscripts/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 LogView tamper-evident audit trail with Merkle chain verification:```bashscripts/audit-log.sh --last 20scripts/audit-log.sh --agent "research-agent" --verify```The `--verify` flag checks Merkle chain integrity — any tampering is detected.### Generate IdentityCreate a new Ed25519 cryptographic identity (DID) for your agent:```bashscripts/generate-identity.sh --name "my-agent" --capabilities "search,summarize,write"```Returns your agent's DID, public key, and capability manifest.## Policy File FormatCreate a `policy.yaml` to define governance rules:```yamlname: production-policymax_tokens: 4096max_tool_calls: 10allowed_tools: - web_search - file_read - summarizeblocked_tools: - shell_exec - file_deleteblocked_patterns: - "rm -rf" - "DROP TABLE" - "BEGIN CERTIFICATE"confidence_threshold: 0.7require_human_approval: false```## When to Use This Skill- **Before tool execution**: Run `check-policy.sh` to enforce limits- **Before trusting another agent's output**: Run `verify-identity.sh`- **After collaboration**: Run `record-interaction.sh` to update trust- **Before delegation**: Check `trust-score.sh` — don't delegate to agents below 0.5- **For compliance**: Run `audit-log.sh --verify` to prove execution integrity- **On setup**: Run `generate-identity.sh` to 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 |## ArchitectureThis skill bridges the OpenClaw agent runtime with the [AgentMesh](https://github.com/imran-siddique/agent-mesh)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](https://imran-siddique.github.io):[AgentMesh](https://github.com/imran-siddique/agent-mesh) ·[Agent OS](https://github.com/imran-siddique/agent-os) ·[Agent SRE](https://github.com/imran-siddique/agent-sre)

Frequently asked questions