Klingai Compliance Review

What it does

When your agent uses it

• Preparing for audits
• Reviewing security posture
• With phrases like klingai compliance
• Kling ai security review

Example prompts

• “klingai compliance”
• “kling ai security review”
• “klingai audit prep”
• “/klingai-compliance-review”

Requirements

• Python 3
• Compatibility (from SKILL.md): Designed for Claude Code
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Grep

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Review a sandbox canary against content policy, rights/attribution, access scope, budget, destination, and retention requirements.
2. 2Record only aggregate and redacted evidence; reject artifacts containing private information, unlicensed material, restricted content, or…
3. 3Halt release and remove drafts when any mandatory check fails; assign the issue to an owner and verify the remediation in a new canary.
4. 4Approve promotion only after all required checks pass and retain the redacted review receipt for the authorized window.

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash(npm:*)
  • Grep

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • owasp.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Designed for Claude Code

  From compatibility in the SKILL.md frontmatter.

Context cost

Klingai Compliance Review loads about 1.8k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 432 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~71

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 432 words, ~1,818 tokens.

name

klingai-compliance-review

description

Security and compliance review framework for Kling AI integrations. Use when preparing for audits or reviewing security posture. Trigger with phrases like 'klingai compliance', 'kling ai security review', 'klingai audit prep', 'video generation compliance'.

allowed-tools

Read, Write, Edit, Bash(npm:*), Grep

compatibility

Designed for Claude Code

version

1.18.0

license

MIT

author

Jeremy Longshore <jeremy@intentsolutions.io>

tags

saas, kling-ai, compliance, security

User Prompt → [Your App] → [Kling AI API] → [Kling GPU Cluster]
                                                     ↓
[Your CDN] ← download ← [Kling CDN (temporary URL)] ← Generated Video

# Safe logging pattern
def safe_log_key(access_key: str) -> str:
    return access_key[:8] + "..." + access_key[-4:]

class GDPRCompliantClient:
    """Kling client with GDPR data handling."""

    def __init__(self, base_client, audit_logger):
        self.client = base_client
        self.audit = audit_logger

    def text_to_video(self, prompt: str, data_subject_id: str = None, **kwargs):
        # Log processing activity (GDPR Art. 30)
        self.audit.log("processing_activity", "system", {
            "purpose": "video_generation",
            "data_subject": data_subject_id,
            "legal_basis": "legitimate_interest",
            "data_categories": ["text_prompt"],
            "recipients": ["klingai_api"],
        })

        return self.client.text_to_video(prompt, **kwargs)

    def handle_deletion_request(self, data_subject_id: str):
        """Handle GDPR right to erasure (Art. 17)."""
        # Delete stored videos associated with the data subject
        # Delete audit logs referencing the data subject
        # Note: cannot delete data already sent to Kling API
        self.audit.log("deletion_request", "system", {
            "data_subject": data_subject_id,
            "action": "processed",
        })

def run_compliance_check(config: dict) -> dict:
    """Run automated compliance checks against configuration."""
    checks = []

    # Check credential storage
    if config.get("key_source") == "environment":
        checks.append(("WARN", "credentials", "Using env vars; prefer secrets manager"))
    elif config.get("key_source") == "secrets_manager":
        checks.append(("PASS", "credentials", "Using secrets manager"))

    # Check TLS
    if config.get("base_url", "").startswith("https://"):
        checks.append(("PASS", "tls", "HTTPS enforced"))
    else:
        checks.append(("FAIL", "tls", "Not using HTTPS"))

    # Check content filtering
    if config.get("content_filter_enabled"):
        checks.append(("PASS", "content_filter", "Pre-submission filtering active"))
    else:
        checks.append(("WARN", "content_filter", "No pre-submission content filtering"))

    # Check audit logging
    if config.get("audit_logging"):
        checks.append(("PASS", "audit", "Audit logging enabled"))
    else:
        checks.append(("FAIL", "audit", "No audit logging"))

    # Print report
    for status, area, message in checks:
        icon = {"PASS": "OK", "WARN": "!!", "FAIL": "XX"}[status]
        print(f"  [{icon}] {area}: {message}")

    return {
        "passed": sum(1 for s, _, _ in checks if s == "PASS"),
        "warnings": sum(1 for s, _, _ in checks if s == "WARN"),
        "failed": sum(1 for s, _, _ in checks if s == "FAIL"),
    }

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/.curated/klingai-compliance-review of jeremylongshore/tons-of-skills-marketplace.

• SKILL.md
• references/compliance-checker.md
• references/compliance-report-template.md
• references/errors.md
• references/examples.md

Open the folder on GitHubat commit cfae287