From klingai-pack
Filters Kling AI prompts for content policy compliance using Python regex to block violence, adult/sexual content, hate, illegal activities, deepfakes, and more. Prevents failed generations and wasted credits.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-content-policyThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Kling AI enforces content policies server-side. Tasks with policy-violating prompts return `task_status: "failed"` with a content policy message. This skill covers pre-submission filtering to avoid wasted credits and API calls.
Kling AI enforces content policies server-side. Tasks with policy-violating prompts return task_status: "failed" with a content policy message. This skill covers pre-submission filtering to avoid wasted credits and API calls.
Kling AI prohibits prompts that generate:
| Category | Examples |
|---|---|
| Violence/gore | Graphic injuries, torture, weapons used violently |
| Adult/sexual | Explicit nudity, sexual acts, suggestive content |
| Hate/discrimination | Slurs, targeted harassment, supremacist imagery |
| Illegal activity | Drug manufacturing, terrorism, fraud instructions |
| Real people | Deepfakes of identifiable individuals without consent |
| Copyrighted characters | Trademarked characters (Mickey Mouse, Spider-Man) |
| Misinformation | Fake news, fabricated events presented as real |
| Self-harm | Suicide, eating disorders, self-injury instructions |
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()
Always include safety-related negative prompts:
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}
When building apps with user-submitted prompts:
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packReviews security and compliance for Kling AI video generation API integrations using checklists for credentials, data flow, input validation, privacy, and GDPR prep.
Generates videos from text prompts or images, animates still images, and creates talking avatars from photos with audio using Kling AI models (VIDEO 3.0, Avatar 2.0, etc.). Handles multi-shot storyboards, character consistency, and prompt engineering.
Generates videos from text prompts via fal.ai models like Kling 2.6 Pro, Sora 2, LTX-2 Pro, Runway Gen-3 Turbo, Luma Dream Machine; supplies endpoints, durations, aspect ratios, prompt structures, TypeScript/Python code.