「explainer」の検索結果

Design and build expressive 3D scenes, explainers and interactive models with grounded subject knowledge. Use for 3D creation or edits, including scientific visualization and spatial puzzles.

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Use when the user mentions the Higgsfield CLI (binaries `higgsfield` / `higgs` / `hf`, `higgsfield auth login`, `higgsfield generate create`, `higgsfield workflow`, the `@higgsfield/cli` npm package), the Higgsfield MCP custom connector (`mcp.higgsfield.ai/mcp`) or its bundled workflows (`get_workflow_instructions` — character-sheet, thumbnail-generation, ugc-* videos, faceless-video, narrator, subtitles, product-photoshoot, brand-asset-creation, ad-multiplier, video-editing / Higgsedit, website-builder-flow), Higgsfield's bundled skills repo (`higgsfield-ai/skills` — `higgsfield-generate`, `higgsfield-soul-id`, `higgsfield-product-photoshoot`, `higgsfield-brandkit`, `higgsfield-marketplace-cards`, `higgsfield-websites`, `higgsfield-video-explainer`, `higgsfield-youtube-thumbnail`, invoked as `/higgsfield:generate` etc.), or asks how this skill coexists with those tools (`do I need both`, `how does this work with the CLI/MCP/skills`, `should Higgsfield's workflow do this or you`).

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Make a clean 45–90 second product demo video for any software product, rendered from code to MP4 with music. Airy light canvas, short centred lines the camera pushes into, the product's own UI rebuilt and shown up close as it is used (typing, results, tables, clicks), integrations, and a logo close. Use when someone wants a product demo, launch video, feature walkthrough or "how it works" video for an app, SaaS or tool.

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Make a 12–20 second high-energy motion-graphics promo for any product, rendered from code to MP4 with synthesised music. Fast beat-locked scene changes, kinetic typography, a shape that carries across every transition, a HUD frame. Use when someone wants a hype reel, teaser, launch sting or showreel-style promo for a product, app or brand.

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Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

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Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser session.

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Use when the user wants a GitHub PR (URL, owner/repo#N, or 'this PR') made into a code-change explainer video from its diff and commits: changelog, feature reveal, fix, or refactor. Not a product promo; unclear goes to /hyperframes.

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Generate self-contained HTML visual explanations for systems, code changes, plans, data, and technical concepts. Use for diagrams, architecture overviews, diff or plan reviews, project recaps, comparison tables, slide decks, and other visual explanations.

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Generate HeyGen presenter videos via the v3 Video Agent pipeline — handles Frame Check (aspect ratio correction), prompt engineering, avatar resolution, and voice selection. Required for any HeyGen video generation. Replaces deprecated endpoints with v3. Use when: (1) generating any HeyGen video (via API or otherwise), (2) sending a personalized video message (outreach, update, announcement, pitch, knowledge), (3) creating a HeyGen presenter-led explainer, tutorial, or product demo with a human face, (4) "make a video of me saying...", "send a video to my leads", "record an update for my team", "create a video pitch", "make a loom-style message", "I want to appear in this video", "generate a HeyGen video", "make a talking head video". Accepts avatar_id from heygen-avatar for identity-first HeyGen videos, or uses a stock presenter. Returns video share URL + HeyGen session URL for iteration. Chain signal: when the user wants to create/design an avatar AND make a video in the same request, run heygen-avatar first, then return here. Conjunctions to watch: "and then", "and immediately", "first...then", "X and make a video", "design [presenter] and record" = always CHAIN. If the user provides a photo AND wants a video, route to heygen-avatar first. NOT for: avatar creation or identity setup (use heygen-avatar first), cinematic footage or b-roll without a presenter, translating videos, TTS-only, or streaming avatars.

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Explain analysis methodology to diverse audiences. Use when documenting 'how we did this' sections, building trust through transparency, or teaching analytical approaches to stakeholders.

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创建包含 section、problem、solution 和 explainer 的练习目录结构，且能通过 lint 检查。当用户想要搭建练习框架、创建练习模板或设置新的课程章节时使用。

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Direct and implement programmatic videos from a natural-language sentence: brief, shots, style, timeline, assets, engine-specific coding prompts, draft, review and repair. Use when asked to make a video, product launch, explainer, Reel/Short, kinetic typography or docs-to-video with HyperFrames, Remotion or Motion Canvas, or to improve an existing code-video project. 一句话视频制作与导演工作流。

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Create AI avatar and talking head videos via inference.sh CLI. Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS). Also: OmniHuman, Fabric, PixVerse. Audio: Inworld TTS-2 (100+ languages, emotion steering for characters), ElevenLabs, Kokoro. Capabilities: audio-driven avatars, text-to-avatar, lipsync videos, talking head generation, virtual presenters, UGC content. Use for: AI presenters, explainer videos, virtual influencers, dubbing, marketing videos, UGC ads, gaming avatars, NPC dialogue. Triggers: ai avatar, talking head, lipsync, avatar video, virtual presenter, ai spokesperson, audio driven video, heygen alternative, synthesia alternative, talking avatar, lip sync, video avatar, ai presenter, digital human, ugc, ugc video, ugc ad, avatar ugc

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Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, reference-to-video, video editing, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative, seedance, happyhorse

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Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

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Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.

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把一句话需求转成可复现、可验收视频工程的乔木智能剪辑导演。Use when the user asks to create, plan, edit, remix, explain, narrate, subtitle, animate, composite, or render a video—including one-line requests such as “制作一个科普视频：介绍 LLM 中的 RL”. Builds English-learning movie mixes, person profiles, explainers, code-drawn motion graphics (UI morph loops, showreels, kinetic-type promos, line-art/pixel/Three.js animation, lyric MVs), product launch videos, cinematic shorts, social clips, course/PPT videos, AI image/video/TTS/music-assisted stories, stock-footage stories, or multi-source montage. Routes across 33台词, ClipSeek/open media, local files, ListenHub, image generation, HTML/SVG/Canvas motion graphics with beat-grid storyboards and determinism probes, Manim, ffmpeg-full, captions, sound design, frame-0 video covers with YouTube thumbnails, and release checks.

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Use IBM AI Fairness 360 for tabular fairness datasets, metrics, bias mitigation algorithms, sklearn-compatible workflows, subgroup detectors, and explainers.

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Turn a narration line, knowledge point, opinion, story beat, or abstract concept into a Seedance 2.0 editorial paper-collage clip with a clear visual metaphor, approved final composition, tactile stop-motion assembly, controlled paper texture, purposeful depth, and synchronized collage sound effects. Use for 4–15 second explainer or B-roll clips; not for realistic presenter ads or editable-layer delivery.

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Write engaging video scripts for YouTube, TikTok, and other platforms. Creates complete scripts with hooks, main content, and CTAs. Supports various formats including tutorials, vlogs, reviews, explainers, and storytelling. Use when creating video scripts, writing YouTube content, or planning video structure.

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Turn ONE topic, talking-head video, or photo into a finished Vox-style paper-collage explainer / ad video on the MuAPI platform (api.muapi.ai) + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode).

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Explain GRACE 4 methodology, .grace artifacts, semantic anchors, change lifecycle, verification, and migration boundaries.

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Explains runtime errors and compilation failures with actionable debugging guidance. Use when Python or Java code throws runtime exceptions (NullPointerException, TypeError, AttributeError, etc.), compilation errors (syntax errors, type mismatches, import failures), or dependency issues. Analyzes error messages, stack traces, and code context to identify root causes and provide concrete fixes with examples. Distinct from test-related errors - focuses on errors during normal code execution and build processes.

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Analyze and explain why Isabelle or Coq proofs fail, identifying the root cause such as type mismatches, missing assumptions, incorrect goals, unification failures, or inapplicable tactics. Use when the user encounters proof failures, error messages in formal verification, stuck proof states, or asks why their Isabelle/Coq proof doesn't work.

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