indirect-prompt-injection

Harden agent sessions against prompt injection from untrusted content. Use when the agent reads web search results, emails, downloaded files, PDFs, or any external text that could contain adversarial instructions. Provides content scanning, memory write guardrails (scan → lint → accept or quarantine), untrusted content tagging, and canary detection. Also use when setting up new tools that ingest external content (email checkers, RSS readers, web scrapers).

AI video prompt builder for cinematographers. Translates natural language shot descriptions into structured prompts optimized for AI video generators.

将中文创意需求转换为 SDXL 或 Flux 可用的高质量英文图像提示词。当用户要求生成图片、画一张图、出图、AI绘画时触发。

Transform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.

Detect prompt injection attacks and adversarial inputs in user text before passing it to your LLM. Use when you need to validate or screen user-provided text for jailbreak attempts, instruction overrides, role-play escapes, or other prompt manipulation techniques. Returns a safety verdict, risk score (0–1), and threat categories. Ideal for guarding AI pipelines, chatbots, and any application that feeds user input into a language model.

AI视频Prompt构建专家。采用"首尾帧图片+视频"工作流，支持多段5秒视频拼接生成长视频（30秒/60秒）。先生成关键帧图片，再生成视频Prompt，确保段与段之间无缝衔接。针对即梦平台优化，支持全中文Prompt输出。

Nubaby prompt system for prompt augmentation, routers, dictionaries, dataset captions, prompt tags, compact prompts, video/storyboard prompt shaping, and structured visual tension expansion. Use when prompts are too short/vague or need structured upgrade before comfyui-nubaby execution.

Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.

This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.

Analyzes and rewrites prompts for better AI output, creates reusable prompt templates for marketing use cases (ad copy, email campaigns, social media), and structures end-to-end AI content workflows. Use when the user wants to improve prompts for AI-assisted marketing, build prompt templates, or optimize AI content workflows. Also use when the user mentions 'prompt engineering,' 'improve my prompts,' 'AI writing quality,' 'prompt templates,' or 'AI content workflow.'

Token-safe prompt assembly with memory orchestration. Use for any agent that needs to construct LLM prompts with memory retrieval. Guarantees no API failure due to token overflow. Implements two-phase context construction, memory safety valve, and hard limits on memory injection.

Use when creating journal cover images, generating scientific artwork prompts, or designing graphical abstracts. Creates detailed prompts for AI image generators to produce publication-quality scientific visuals.