Agent Audit Trail — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Agent Audit Trail (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Compliance infrastructure for agentic AI.
An MCP server that provides immutable audit logging, policy enforcement, and compliance reporting for AI agent workflows. Built for SMBs deploying AI agents who need to demonstrate regulatory compliance (Colorado SB 205, EU AI Act, and emerging state AI legislation).
This is the strongest repo to review for AI governance, agent safety, and implementation reliability. It shows the part of AI delivery that usually gets ignored in demos: what happened, which tool was called, what data was touched, which policy fired, and whether the record can be trusted later.
Proof points:
Agent Audit Trail sits between your AI agents and their tools, providing:
npm install -g agent-audit-trailAdd to your claude_desktop_config.json:
{
"mcpServers": {
"agent-audit-trail": {
"command": "agent-audit-trail"
}
}
}claude mcp add agent-audit-trail -- npx agent-audit-trail| Tool | Description |
|---|---|
log_action | Log an agent action to the immutable audit trail |
query_logs | Search and filter audit entries |
get_log_detail | Get full details for a specific log entry |
get_summary | Dashboard-style activity summary |
list_agents | Show all registered agents |
register_agent | Explicitly register a new agent |
list_policy_rules | Show configured policy rules |
add_policy_rule | Create a new policy rule |
toggle_policy_rule | Enable or disable a rule |
verify_chain | Verify the cryptographic integrity of the audit trail |
export_audit_log | Export logs for compliance reporting |
Three rules are active out of the box:
Every log entry is chained to the previous one using SHA-256. Each entry stores:
previous_hash — the hash of the entry before it (null for the first entry)hash — SHA-256 of: previous_hash + all entry fieldsThis means:
verify_chain and get a definitive yes/no answerRun verify_chain before exporting compliance reports to confirm the chain is intact. The export output includes the verification result.
Use the log_action tool:
- agent_name: "customer-support-bot"
- tool_name: "database_query"
- tool_action: "SELECT"
- parameters: {"query": "SELECT email, phone FROM customers WHERE id = 123"}
- data_fields_accessed: "email,phone"The server will log the action and return any policy violations (in this case, the PII Field Access rule would flag the email and phone access).
Use the query_logs tool:
- has_violations: true
- limit: 10Use the add_policy_rule tool:
- name: "Block External API Calls"
- description: "Block agents from calling external APIs without approval"
- condition_type: "tool_match"
- condition_operator: "contains"
- condition_value: "external_api"
- action: "block"
- severity: "high"Audit logs are stored locally at ~/.agent-audit-trail/audit-logs.json by default with full hash chaining. Configure a custom path with the AUDIT_DATA_DIR environment variable.
Production note: Local JSON storage works for single-instance deployments. For multi-agent or distributed setups, swap to Supabase/PostgreSQL for scalable, cloud-hosted storage with the same hash chain guarantees (documentation coming soon).
This tool helps businesses demonstrate compliance with:
Note: Agent Audit Trail provides logging and reporting infrastructure. It does not constitute legal compliance certification. Consult legal counsel for your compliance program.
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.