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ai-content-generation

When the user wants to create AI-generated image or video content: a faceless influencer or persona for TikTok and Instagram, a faceless YouTube channel, an AI clone or talking-head of themselves, or AI ad creative (UGC, product video, static images). Also use when the user mentions 'faceless influencer,' 'faceless channel,' 'AI avatar,' 'AI UGC,' 'talking head,' 'clone myself,' 'AI ad creative,' 'AI video ads,' or tools like Veo, Google Flow, Kling, Higgsfield, Seedance, Runway, ElevenLabs, HeyGen, Arcads, Nano Banana, Midjourney, or Ideogram. Gives step-by-step, copy-paste-prompt playbooks for each use case plus the realism rules that beat AI slop. For paid-ad strategy and targeting, see performance-marketing or paid-ads.

DeepseekModel 官方收录技能 质量 优秀 · 78 v1.0.0

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https://deepseekmodel.com/api/download.php?id=realjaymes-marketingagentskills-skills-ai-content-generation-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name ai-content-generation description When the user wants to create AI-generated image or video content: a faceless influencer or persona for TikTok and Instagram, a faceless YouTube channel, an AI clone or talking-head of themselves, or AI ad creative (UGC, product video, static images). Also use when the user mentions 'faceless influencer,' 'faceless channel,' 'AI avatar,' 'AI UGC,' 'talking head,' 'clone myself,' 'AI ad creative,' 'AI video ads,' or tools like Veo, Google Flow, Kling, Higgsfield, Seedance, Runway, ElevenLabs, HeyGen, Arcads, Nano Banana, Midjourney, or Ideogram. Gives step-by-step, copy-paste-prompt playbooks for each use case plus the realism rules that beat AI slop. For paid-ad strategy and targeting, see performance-marketing or paid-ads. metadata {"version":"1.5.0"} AI Content Generation Help the user actually make AI image and video content, end to end, with copy-paste prompts at every step. This skill covers four use cases, each a self-contained, do-this-then-that playbook. The hard parts (keeping a character consistent and killing the AI-slop look) are baked into the prompts, so the user can follow the steps without prior experience. The core idea The model is the easy part. What makes AI content win is the niche, the script, keeping the character or product consistent, and beating the AI-slop look. Every playbook bakes those in. Roughly 80% of quality is set before the model runs (research, script, consistency, realism); the model is the last 20%. Pick the use case, then open its reference The user wants to... Use this reference Run a recurring AI character on TikTok or Instagram, no face on camera references/faceless-influencer.md Clone themselves into a talking head and generate videos from scripts references/ai-clone-talking-head.md Make paid ad creative at volume (UGC, product video, static) references/ai-ad-creative.md Build a faceless YouTube channel (long-form and shorts) references/faceless-youtube.md Each reference is fully self-contained: a numbered walkthrough split into one-time setup and a repeating loop, with the exact prompts inline. How to run this skill Identify which of the four use cases the user is after (ask one question if unclear). Open the matching reference file and follow it with the user, step by step. At each tool step, give the user the copy-paste prompt from the reference, filled in with their specifics (niche, topic, product, audience). Keep one character, one voice, and one look consistent across a user's content. Consistency is the most common failure point. Apply the realism rules below to every generation prompt (they are already written into the reference prompts; do not strip them out). Before the user publishes, run the reference's pre-publish checklist. How the tools are named (app vs model) The names look chaotic because three different things share the spotlight: apps you chat inside, wrappers that put one interface over many models, and the models that do the generating. Once you know which is which, every new launch slots in. The model generates. The app or wrapper is just the door you walk through to reach it. Three buckets: Assistant / app : where you type. It runs models underneath. ChatGPT, Gemini, Claude. Wrapper : one interface over several models, usually for cheaper volume or shot control. Google Flow, Higgsfield. Model : the thing that actually generates, split by layer (text, image, video, voice). Veo 3.1, Gemini Omni, Nano Banana, Kling. Tool Type Made by What it is / what it runs ChatGPT Assistant OpenAI LLM for scripts and ideas Gemini Assistant Google LLM, and the front door to Google's image (Nano Banana) and video (Omni) models Claude Assistant Anthropic LLM, best for natural voice and writing Google Flow Wrapper Google Runs Veo 3.1 and Gemini Omni with a scene builder and shot controls. Paid (Google AI plan, credit-based). Has a QR face-scan avatar to drop yourself into any scene; work image-first to save credits Higgsfield Wrapper Higgsfield Runs Kling and other video models in one UI, cheap at volume Gemini Omni Video model Google Conversational generation and editing, multi-input, 1080p, ~10s clips. Now the default in the Gemini app, Flow, and YouTube Shorts Veo 3.1 Video model Google Cinematic, 4K, longer shots, stable API. Best for brand films and ads Kling Video model Kuaishou Cheap volume, reached through Higgsfield Seedance Video model ByteDance Lowest-cost video Nano Banana Image model Google Locks a face, edits inside an image. Lives in the Gemini app Midjourney Image model Midjourney Best aesthetic look GPT Image Image model OpenAI Renders the scene and legible in-image text in one pass. Lives in ChatGPT Ideogram Image model Ideogram Legible text inside an image, fallback when GPT Image falls short ElevenLabs Voice model ElevenLabs Voiceover and voice cloning Speechma Voice app Speechma Free library voices, no cloning HeyGen Avatar app HeyGen Talking-head avatars, clone yourself Arcads UGC app Arcads AI UGC actors for ads Google Vids All-in-one app Google Doc to video with stock, AI voiceover, avatars CapCut Editor ByteDance Captions, color, music, assembly The big three, decoded. Each vendor has a company name, an app you chat in, and a family of models underneath. Mixing those three is most of the confusion. Google : Gemini is the assistant. Nano Banana is its image model. Veo 3.1 is its cinematic video model (API, 4K, long shots). Gemini Omni is its conversational video generation-and-editing model, now default in the Gemini app, Flow, and Shorts. Veo 3.1 did not go away. It stayed on the API for high-fidelity work while Omni took over the consumer app. OpenAI : OpenAI is the company. ChatGPT is the app. GPT (GPT-5 and friends) are the text models inside it. Sora is its video model, DALL-E / GPT Image its image model. Anthropic : Anthropic is the company. Claude is the assistant. Opus, Sonnet, and Haiku name the model tiers, from most capable down to fastest (for example Claude Opus 4.x). To place any new model , ask two questions. Is it an app you chat in, a wrapper over other models, or a model itself? And which layer does it work on, text, image, video, or voice? Drop it in the matching row and move on. Tool stack at a glance The user does not need all of these. Each playbook names the few it uses. Start lean (free tiers plus ElevenLabs Starter is enough to ship), scale one tool per layer only when volume demands it. Job Tools Notes Ideas, scripts, prompts ChatGPT, Gemini, Claude Gemini for grounded/current research, Claude for natural voice Voice and voice cloning ElevenLabs Free to test, Starter to ship Free voiceover (no cloning) Speechma Free, no signup, browser-based library voices; ship narration before you clone a voice Video generation Gemini Omni, Veo 3.1, Google Flow (runs Veo and Omni with a scene builder and shot controls), Kling 3.0 via Higgsfield, Seedance Omni for conversational edits and consistent clips (default in the Gemini app and Flow), Veo 3.1 for cinematic 4K hero shots, Kling for cheap volume, Seedance for lowest cost Images and in-image text ChatGPT (GPT Image), Gemini Nano Banana, Midjourney, Ideogram ChatGPT (GPT Image) is the default for headline/CTA/thumbnail text, short or long (scene and text in one pass); Nano Banana locks a face; Ideogram is the fallback when ChatGPT falls short; Midjourney for the best look Avatars and UGC actors HeyGen, Arcads HeyGen to clone yourself, Arcads for ad UGC All-in-one (quick, lower ceiling) Google Vids Free tier does real work: free Veo AI video (~10 generations/month, the only free way to make AI video), free voiceover, and slides-to-video (File → Convert Slides → narrated video). Talking-head avatars/ingredients are Pro/Ultra only. Fast but weaker for faceless or cinematic Editing CapCut Captions, color, music; free The rule that beats AI slop Realism is controlled imperfection. The default settings and generic prompts produce the average, which reads as fake. Add back the texture and flaws real capture has: Prompt for a phone-camera look: natural light, slight grain, real-time pace. Drop the words "8K", "cinematic", "perfect", "studio", "hyperreal". Those trigger the waxy plastic look. Put real pauses, breaths, and a filler before the key line in voice scripts. Flat delivery is the number one AI-audio tell. Keep clips short (3 to 10 seconds) and cut. Faces and physics drift past that. Segment the script before generating video. Break the finished script into ~10-second beats and generate one beat per clip, each with its own scene and only that beat's spoken line. Never hand a video model the whole 30 to 45 second script in one prompt. If a clip glitches, regenerate only that clip, then stitch in CapCut. No on-screen text in a video prompt. Video models (Veo, Flow, Kling, Gemini Omni) garble any text you ask them to render on screen. Add all captions, hook text, and CTAs in CapCut after generating. Baked-in text stays fine only for the still-image models (GPT Image, Ideogram, Nano Banana), which are chosen precisely because they render legible text. Lock the scene and reuse it across clips. Set the environment, outfit, background, camera angle, lighting, and style once, then repeat that same scene description in every beat's prompt. Reusing it verbatim keeps the character and setting from drifting between clips. For video, add negative prompts: no morphing, no warping, no melting, no jelly motion, no slow motion. Mix in real footage (stock or a real product photo) and apply one consistent color grade so generated and real assets read as one piece. These are already written into the reference prompts. The reference "Make It Look Real" sections also list the editing and review moves a prompt cannot do. Run it outside Claude Code (ChatGPT, Claude, Gemini) A ready-to-use project package lives in assets/chatgpt-project/ . It turns this skill into a ChatGPT Project, Claude Project, or Gemini Gem so a non-technical user can run the same steps in a normal chat: project-instructions.md — paste into the Project or Gem instructions. knowledge/ — upload these files as the project knowledge. References references/faceless-influencer.md references/ai-clone-talking-head.md references/ai-ad-creative.md references/faceless-youtube.md
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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