How do I install the Compliance Audit skill?

Run npx skillmds@latest add johnalbertini14-glitch/compliance-audit 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 Compliance Audit 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, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Compliance Audit?

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 Compliance Audit free to use?

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

Who published Compliance Audit?

johnalbertini14-glitch (@johnalbertini14-glitch) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Compliance Audit by johnalbertini14-glitch

Name: Compliance Audit
Rating: 4.5 (4 reviews)
Author: johnalbertini14-glitch

Compliance Audit

Immutable audit trail for autonomous agent operations. Log skill executions, data access, decisions, and budget changes with tamper-evident hashes. Essential for enterprise governance, incident response, and trust verification.

File contents

Compliance Audit Trail

Immutable, tamper-evident audit logging for autonomous agents. Every action gets a hash-chained entry that can be verified for integrity.

Why This Exists

Autonomous agents make decisions, execute skills, access data, and spend money without human oversight. When something goes wrong, you need to know exactly what happened. Current agent frameworks have no standard audit trail — this fills that gap.

Commands

Log an action

python3 {baseDir}/scripts/audit.py log --action "skill_executed" --details '{"skill": "scanner", "target": "some-skill", "result": "clean"}'

Log a decision

python3 {baseDir}/scripts/audit.py log --action "decision" --details '{"choice": "deploy v2", "reason": "all tests passed", "alternatives_considered": ["rollback", "hotfix"]}'

Log data access

python3 {baseDir}/scripts/audit.py log --action "data_access" --details '{"resource": "api_key", "purpose": "moltbook_post", "accessor": "ghost_agent"}'

Log a budget change

python3 {baseDir}/scripts/audit.py log --action "budget_change" --details '{"amount": -10.00, "merchant": "namecheap", "reason": "domain purchase", "balance_after": 190.00}'

View recent entries

python3 {baseDir}/scripts/audit.py view --last 20

View entries by action type

python3 {baseDir}/scripts/audit.py view --action skill_executed

View entries in a time range

python3 {baseDir}/scripts/audit.py view --since "2026-02-15T00:00:00" --until "2026-02-16T00:00:00"

Verify audit trail integrity

python3 {baseDir}/scripts/audit.py verify

Export audit trail

python3 {baseDir}/scripts/audit.py export --format json > audit-export.json
python3 {baseDir}/scripts/audit.py export --format csv > audit-export.csv

Generate compliance summary

python3 {baseDir}/scripts/audit.py summary --period day

Entry Format

Each audit entry contains:

timestamp — ISO 8601, UTC
action — what happened (skill_executed, decision, data_access, budget_change, error, custom)
agent — which agent performed the action
details — structured JSON with action-specific data
hash — SHA-256 hash chaining previous entry's hash + current entry (tamper-evident)
sequence — monotonically increasing sequence number

Integrity Verification

The audit trail is hash-chained: each entry includes a SHA-256 hash of the previous entry's hash concatenated with the current entry's data. If any entry is modified or deleted, the chain breaks and verify will report the exact point of tampering.

Storage

Audit logs are stored in ~/.openclaw/audit/ as daily JSON files (audit-YYYY-MM-DD.json). This keeps individual files small while maintaining the full history.

Use Cases

Incident response: What happened in the 5 minutes before the error?
Budget accountability: Show every dollar spent and why
Trust verification: Prove your agent hasn't been compromised
Enterprise compliance: Meet audit requirements for autonomous systems
Debugging: Trace the decision chain that led to an unexpected outcome

---name: compliance-auditdescription: Immutable audit trail for autonomous agent operations. Log skill executions, data access, decisions, and budget changes with tamper-evident hashes. Essential for enterprise governance, incident response, and trust verification.--- # Compliance Audit Trail Immutable, tamper-evident audit logging for autonomous agents. Every action gets a hash-chained entry that can be verified for integrity. ## Why This Exists Autonomous agents make decisions, execute skills, access data, and spend money without human oversight. When something goes wrong, you need to know exactly what happened. Current agent frameworks have no standard audit trail — this fills that gap. ## Commands ### Log an action ```bash python3 {baseDir}/scripts/audit.py log --action "skill_executed" --details '{"skill": "scanner", "target": "some-skill", "result": "clean"}' ``` ### Log a decision ```bash python3 {baseDir}/scripts/audit.py log --action "decision" --details '{"choice": "deploy v2", "reason": "all tests passed", "alternatives_considered": ["rollback", "hotfix"]}' ``` ### Log data access ```bash python3 {baseDir}/scripts/audit.py log --action "data_access" --details '{"resource": "api_key", "purpose": "moltbook_post", "accessor": "ghost_agent"}' ``` ### Log a budget change ```bash python3 {baseDir}/scripts/audit.py log --action "budget_change" --details '{"amount": -10.00, "merchant": "namecheap", "reason": "domain purchase", "balance_after": 190.00}' ``` ### View recent entries ```bash python3 {baseDir}/scripts/audit.py view --last 20 ``` ### View entries by action type ```bash python3 {baseDir}/scripts/audit.py view --action skill_executed ``` ### View entries in a time range ```bash python3 {baseDir}/scripts/audit.py view --since "2026-02-15T00:00:00" --until "2026-02-16T00:00:00" ``` ### Verify audit trail integrity ```bash python3 {baseDir}/scripts/audit.py verify ``` ### Export audit trail ```bash python3 {baseDir}/scripts/audit.py export --format json > audit-export.json python3 {baseDir}/scripts/audit.py export --format csv > audit-export.csv ``` ### Generate compliance summary ```bash python3 {baseDir}/scripts/audit.py summary --period day ``` ## Entry Format Each audit entry contains: - **timestamp** — ISO 8601, UTC - **action** — what happened (skill_executed, decision, data_access, budget_change, error, custom) - **agent** — which agent performed the action - **details** — structured JSON with action-specific data - **hash** — SHA-256 hash chaining previous entry's hash + current entry (tamper-evident) - **sequence** — monotonically increasing sequence number ## Integrity Verification The audit trail is hash-chained: each entry includes a SHA-256 hash of the previous entry's hash concatenated with the current entry's data. If any entry is modified or deleted, the chain breaks and `verify` will report the exact point of tampering. ## Storage Audit logs are stored in `~/.openclaw/audit/` as daily JSON files (`audit-YYYY-MM-DD.json`). This keeps individual files small while maintaining the full history. ## Use Cases - **Incident response**: What happened in the 5 minutes before the error? - **Budget accountability**: Show every dollar spent and why - **Trust verification**: Prove your agent hasn't been compromised - **Enterprise compliance**: Meet audit requirements for autonomous systems - **Debugging**: Trace the decision chain that led to an unexpected outcome

Frequently asked questions