Implement audit logging for Kling AI operations for compliance and security. Use when tracking API usage or preparing for audits. Trigger with phrases like 'klingai audit', 'kling ai audit log', 'klingai compliance log', 'video generation audit trail'.
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-audit-logging/SKILL.mdCompliance-grade audit logging for Kling AI API operations. Every task submission, status change, and credential usage is captured in tamper-evident structured logs.
import json
import hashlib
import time
from datetime import datetime
from pathlib import Path
class AuditLogger:
"""Append-only audit log with integrity checksums."""
def __init__(self, log_dir: str = "audit"):
self.log_dir = Path(log_dir)
self.log_dir.mkdir(exist_ok=True)
self._prev_hash = "genesis"
def _compute_hash(self, entry: dict) -> str:
raw = json.dumps(entry, sort_keys=True) + self._prev_hash
return hashlib.sha256(raw.encode()).hexdigest()[:16]
def log(self, event_type: str, actor: str, details: dict):
"""Write a tamper-evident audit entry."""
entry = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"event_type": event_type,
"actor": actor,
"details": details,
"prev_hash": self._prev_hash,
}
entry["hash"] = self._compute_hash(entry)
self._prev_hash = entry["hash"]
date = datetime.utcnow().strftime("%Y-%m-%d")
filepath = self.log_dir / f"audit-{date}.jsonl"
with open(filepath, "a") as f:
f.write(json.dumps(entry) + "\n")
return entry["hash"]class KlingAuditClient:
"""Kling client with full audit trail."""
def __init__(self, base_client, audit: AuditLogger, actor: str = "system"):
self.client = base_client
self.audit = audit
self.actor = actor
def text_to_video(self, prompt: str, **kwargs):
# Log submission
self.audit.log("task_submitted", self.actor, {
"action": "text_to_video",
"model": kwargs.get("model", "kling-v2-master"),
"duration": kwargs.get("duration", 5),
"mode": kwargs.get("mode", "standard"),
"prompt_hash": hashlib.sha256(prompt.encode()).hexdigest()[:16],
"prompt_length": len(prompt),
})
result = self.client.text_to_video(prompt, **kwargs)
# Log completion
self.audit.log("task_completed", self.actor, {
"action": "text_to_video",
"status": "succeed",
"video_count": len(result.get("videos", [])),
})
return result
def log_auth_event(self, event: str, success: bool):
self.audit.log("auth_event", self.actor, {
"event": event,
"success": success,
"access_key_prefix": self.client.config.access_key[:8] + "...",
})def verify_audit_chain(log_file: str) -> bool:
"""Verify tamper-evidence of audit log chain."""
prev_hash = "genesis"
entries = []
with open(log_file) as f:
for line_num, line in enumerate(f, 1):
entry = json.loads(line)
entries.append(entry)
if entry["prev_hash"] != prev_hash:
print(f"Chain broken at line {line_num}: "
f"expected prev_hash={prev_hash}, got {entry['prev_hash']}")
return False
# Recompute hash
check_entry = {k: v for k, v in entry.items() if k != "hash"}
raw = json.dumps(check_entry, sort_keys=True) + prev_hash
expected_hash = hashlib.sha256(raw.encode()).hexdigest()[:16]
if entry["hash"] != expected_hash:
print(f"Hash mismatch at line {line_num}")
return False
prev_hash = entry["hash"]
print(f"Verified {len(entries)} entries -- chain intact")
return Truedef generate_audit_report(log_dir: str = "audit", days: int = 30) -> dict:
"""Generate compliance audit report."""
from collections import Counter
from datetime import timedelta
log_path = Path(log_dir)
events = []
cutoff = datetime.utcnow() - timedelta(days=days)
for filepath in sorted(log_path.glob("audit-*.jsonl")):
with open(filepath) as f:
for line in f:
entry = json.loads(line)
if entry["timestamp"] >= cutoff.isoformat():
events.append(entry)
event_types = Counter(e["event_type"] for e in events)
actors = Counter(e["actor"] for e in events)
report = {
"period_days": days,
"total_events": len(events),
"event_types": dict(event_types),
"unique_actors": len(actors),
"actors": dict(actors),
"first_event": events[0]["timestamp"] if events else None,
"last_event": events[-1]["timestamp"] if events else None,
}
print(f"\n=== Audit Report ({days} days) ===")
print(f"Total events: {report['total_events']}")
for event_type, count in event_types.most_common():
print(f" {event_type}: {count}")
print(f"Actors: {', '.join(actors.keys())}")
return report3e83543
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.