Agent-Governance
Agent-Governance是一款code方向的AI技能,核心价值是|,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
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mkdir -p ./skills/agent-governance && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/agent-governance/SKILL.md -o ./skills/agent-governance/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Agent Governance Patterns
Patterns for adding safety, trust, and policy enforcement to AI agent systems.
Overview
Governance patterns ensure AI agents operate within defined boundaries — controlling which tools they can call, what content they can process, how much they can do, and maintaining accountability through audit trails.
User Request → Intent Classification → Policy Check → Tool Execution → Audit Log
↓ ↓ ↓
Threat Detection Allow/Deny Trust UpdateWhen to Use
- **Agents with tool access**: Any agent that calls external tools (APIs, databases, shell commands)
- **Multi-agent systems**: Agents delegating to other agents need trust boundaries
- **Production deployments**: Compliance, audit, and safety requirements
- **Sensitive operations**: Financial transactions, data access, infrastructure management
---
Pattern 1: Governance Policy
Define what an agent is allowed to do as a composable, serializable policy object.
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
import re
class PolicyAction(Enum):
ALLOW = "allow"
DENY = "deny"
REVIEW = "review" # flag for human review
@dataclass
class GovernancePolicy:
"""Declarative policy controlling agent behavior."""
name: str
allowed_tools: list[str] = field(default_factory=list) # allowlist
blocked_tools: list[str] = field(default_factory=list) # blocklist
blocked_patterns: list[str] = field(default_factory=list) # content filters
max_calls_per_request: int = 100 # rate limit
require_human_approval: list[str] = field(default_factory=list) # tools needing approval
def check_tool(self, tool_name: str) -> PolicyAction:
"""Check if a tool is allowed by this policy."""
if tool_name in self.blocked_tools:
return PolicyAction.DENY
if tool_name in self.require_human_approval:
return PolicyAction.REVIEW
if self.allowed_tools and tool_name not in self.allowed_tools:
return PolicyAction.DENY
return PolicyAction.ALLOW
def check_content(self, content: str) -> Optional[str]:
"""Check content against blocked patterns. Returns matched pattern or None."""
for pattern in self.blocked_patterns:
if re.search(pattern, content, re.IGNORECASE):
return pattern
return NonePolicy Composition
Combine multiple policies (e.g., org-wide + team + agent-specific):
def compose_policies(*policies: GovernancePolicy) -> GovernancePolicy:
"""Merge policies with most-restrictive-wins semantics."""
combined = GovernancePolicy(name="composed")
for policy in policies:
combined.blocked_tools.extend(policy.blocked_tools)
combined.blocked_patterns.extend(policy.blocked_patterns)
combined.require_human_approval.extend(policy.require_human_approval)
combined.max_calls_per_request = min(
combined.max_calls_per_request,
policy.max_calls_per_request
)
if policy.allowed_tools:
if combined.allowed_tools:
combined.allowed_tools = [
t for t in combined.allowed_tools if t in policy.allowed_tools
]
else:
combined.allowed_tools = list(policy.allowed_tools)
return combined
# Usage: layer policies from broad to specific
org_policy = GovernancePolicy(
name="org-wide",
blocked_tools=["shell_exec", "delete_database"],
blocked_patterns=[r"(?i)(api[_-]?key|secret|password)\s*[:=]"],
max_calls_per_request=50
)
team_policy = GovernancePolicy(
name="data-team",
allowed_tools=["query_db", "read_file", "write_report"],
require_human_approval=["write_report"]
)
agent_policy = compose_policies(org_policy, team_policy)Policy as
🎯 Best For
- Claude users
- GitHub Copilot users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Agent-Governance to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Agent-Governance compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Agent-Governance?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Agent-Governance?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/agent-governance/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.