Klingai Content Policy

What it does

When your agent uses it

• Filtering user prompts
• Handling moderation
• With phrases like klingai content policy
• Kling ai moderation

Example prompts

• “klingai content policy”
• “kling ai moderation”
• “safe video generation”
• “/klingai-content-policy”

Requirements

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

Workflow steps

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

1. 1Filter before API call -- saves credits on obvious violations
2. 2Explain rejections clearly -- tell users what to change
3. 3Log violations -- track patterns for filter improvement
4. 4Rate limit prompt submissions -- prevent abuse
5. 5Review flagged content -- human review for edge cases

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

  No URLs in SKILL.md.

  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 Content Policy loads about 2.2k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 69 tokens; SKILL.md has 559 words of instructions outside code blocks.

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

~69

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

~2.2k

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

~5.3k

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). 559 words, ~2,166 tokens.

name

klingai-content-policy

description

Implement content policy compliance for Kling AI prompts and outputs. Use when filtering user prompts or handling moderation. Trigger with phrases like 'klingai content policy', 'kling ai moderation', 'safe video generation', 'klingai content filter'.

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, content-policy, moderation

import re

class PromptFilter:
    """Filter prompts before sending to Kling AI to save credits."""

    BLOCKED_PATTERNS = [
        r"\b(nude|naked|explicit|nsfw|porn)\b",
        r"\b(gore|dismember|torture|mutilat)\b",
        r"\b(bomb|terroris|weapon|firearm)\b",
        r"\b(suicide|self.harm|kill.yourself)\b",
        r"\b(deepfake|impersonat)\b",
    ]

    BLOCKED_TERMS = {
        "blood splatter", "graphic violence", "child abuse",
        "drug manufacturing", "hate speech",
    }

    def __init__(self):
        self._patterns = [re.compile(p, re.IGNORECASE) for p in self.BLOCKED_PATTERNS]

    def check(self, prompt: str) -> tuple[bool, str]:
        """Returns (is_safe, reason)."""
        lower = prompt.lower()

        for term in self.BLOCKED_TERMS:
            if term in lower:
                return False, f"Blocked term: '{term}'"

        for pattern in self._patterns:
            match = pattern.search(prompt)
            if match:
                return False, f"Blocked pattern: '{match.group()}'"

        if len(prompt) > 2500:
            return False, "Prompt exceeds 2500 character limit"

        if len(prompt.strip()) < 5:
            return False, "Prompt too short"

        return True, "OK"

    def sanitize(self, prompt: str) -> str:
        """Remove problematic terms and return cleaned prompt."""
        for pattern in self._patterns:
            prompt = pattern.sub("[removed]", prompt)
        return prompt.strip()

DEFAULT_NEGATIVE_PROMPT = (
    "violence, gore, blood, nudity, sexual content, "
    "weapons, drugs, hate symbols, distorted faces, "
    "watermark, text overlay, low quality, blurry"
)

def safe_request(prompt: str, negative_prompt: str = ""):
    """Build request with safety defaults."""
    combined_negative = f"{DEFAULT_NEGATIVE_PROMPT}, {negative_prompt}".strip(", ")
    return {
        "model_name": "kling-v2-master",
        "prompt": prompt,
        "negative_prompt": combined_negative,
        "duration": "5",
        "mode": "standard",
    }

class SafeKlingClient:
    """Kling client with pre-submission content filtering."""

    def __init__(self, base_client):
        self.client = base_client
        self.filter = PromptFilter()

    def text_to_video(self, prompt: str, **kwargs):
        is_safe, reason = self.filter.check(prompt)
        if not is_safe:
            raise ValueError(f"Content policy violation: {reason}")

        # Add safety negative prompt
        kwargs.setdefault("negative_prompt", "")
        kwargs["negative_prompt"] = (
            f"{DEFAULT_NEGATIVE_PROMPT}, {kwargs['negative_prompt']}".strip(", ")
        )

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

def handle_policy_rejection(task_id: str, result: dict):
    """Handle content policy rejections gracefully."""
    status_msg = result["data"].get("task_status_msg", "")

    if "content policy" in status_msg.lower() or "policy violation" in status_msg.lower():
        return {
            "error": "content_policy_violation",
            "message": "Your prompt was rejected by Kling AI's content policy. "
                      "Please revise to remove restricted content.",
            "task_id": task_id,
            "credits_consumed": False,  # policy rejections typically don't consume credits
        }
    return {"error": "generation_failed", "message": status_msg, "task_id": task_id}

fixture=synthetic-product-v4; rights=cleared; likeness=none;
policy=pass-v3; destination=staging-private; canary=watermarked;
budget=within-limit; approval=pending; decision=approved_for_draft

© 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 5 other files (references) in skills/.curated/klingai-content-policy of jeremylongshore/tons-of-skills-marketplace.

• SKILL.md
• references/content-filter-implementation.md
• references/content-moderation-service.md
• references/errors.md
• references/examples.md
• references/policy-violation-logger.md

Open the folder on GitHubat commit cfae287