데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9천건)
Acquire, clean, analyze, verify, visualize, and explain data for journalism. Use for reproducible data reporting, statistical analysis, maps, or public methodology.
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Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
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Connect to a hospital's FHIR R4 server (Epic, Oracle Health/Cerner, MEDITECH, athenahealth, or any SMART-on-FHIR endpoint), pull a patient's clinical data and notes, and extract structured findings. Use when users say "connect to the EHR", "connect to Epic/Cerner", "pull notes for patient X", "what do the last 6 months of notes say about Y", or any task that starts from a live EHR rather than pasted text.
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Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.
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Use when the user wants to analyze dataset bias, create stratified samples, evaluate fairness, or plan dataset collection. Triggers on phrases like "dataset bias", "stratified sample", "class imbalance", "data distribution", "fairness analysis", or "ethical review".
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End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to reproduce, implement, replicate, or re-run a paper from scratch, or pastes an arxiv URL with reproduction intent.
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把一条 AI 视频提示词从源模型(如 Sora 2)的写法风格转换为目标模型(如 Kling 3.0 / Wan 2.7 / Veo 3.1 等)的最佳实践写法。基于 110 条 10 场景 × 11 模型对照基准数据(prompts/data/cross-model-matrix.json),不是凭直觉重写,而是查表式 in-context learning。10 场景:产品 / 双人对话 / 物理动作 / 图生视频 / 多人会议 / 恐怖 / 自然延时 / 抽象 / 武侠 / 萌宠。用于"Sora 已 EOL 帮我把这条提示词改成 Veo"、"我有 Kling 提示词想跑 Wan"、"跨模型 A/B 测试"、"把英文 prompt 优化成 Kling 中文版"等触发场景。
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生成符合 HappyHorse 1.0 严格规则的紧凑提示词（30-55 词），主体先行 + 明确镜头技术 + 音频激活路径（with X audible / speaking English at natural pace）。可选加入"8s 时序节拍"结构。用于"用 HappyHorse 生成视频"、"做个 3-15 秒短片"、"要原生带音频的视频"、"ASMR 视频提示词"等触发场景。
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生成符合 Kling 3.0（可灵 3.0，快手）规则的视频提示词。三种写法自适应：4 部分基础公式（短视频）/ 5 层进阶公式（剧情+音频）/ 图生视频专用（只描述运动）。Kling 是 2026 年中文理解最强、原生音画同步、最长 2 分钟、支持角色定向发声、Motion Brush 的电影级模型。用于"用 Kling 生成视频"、"可灵 AI 提示词"、"中文视频生成"、"带原生音频的剧情视频"、"图生视频"、"角色对话视频"等触发场景。
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把用户的自然语言视频需求转换为符合 Doubao Seedance 2.0 进阶公式的提示词（8 要素：精准主体+动作细节+场景环境+光影色调+镜头运镜+视觉风格+画质+约束条件）。用于"帮我写一个 Seedance 提示词"、"生成视频提示词"、"做个产品广告视频"、"用 Seedance 生成 XX"等触发场景。如果用户没明确说 Seedance，但描述的是单一镜头的复杂叙事/多主体/电影感视频，也优先用此 skill。
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Generate luxury and premium aesthetic video prompts for Seedance 2.0 on Higgsfield. Use for luxury brand content, premium product showcases, high-end lifestyle, minimalist aesthetic, elegant brand videos, or any content requiring sophisticated visual treatment. Triggers on luxury, premium, high-end, elegant, minimalist, sophisticated, exclusive, refined, designer, couture, bespoke.
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Generate SaaS product launch and software demo video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants a product launch video, app demo, software walkthrough, feature showcase, startup promo, tech product reveal, or any SaaS/software marketing video. Triggers on SaaS, app, software, product launch, demo, feature, startup, tech, UI, dashboard, landing page video.
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Generate before-and-after transformation video prompts for Seedance 2.0 on Higgsfield. Use for transformation reveals, glow-ups, makeovers, renovation reveals, fitness transformations, design before-after, business growth visuals, or any content showing dramatic change. Triggers on before after, transformation, reveal, glow up, makeover, renovation, redesign, progress, results.
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Generate faceless content video prompts for Seedance 2.0 on Higgsfield. Use for faceless YouTube channels, TikTok content without showing face, anonymous creator content, narration-driven videos, stock-footage-style AI content, or any video where the creator doesn't appear on camera. Triggers on faceless, no face, anonymous, narration, stock footage, b-roll, background video, voiceover video.
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Generate online course and coaching program promotional video prompts for Seedance 2.0 on Higgsfield. Use for course trailers, coaching ads, educational content promos, masterclass teasers, webinar promotions, or any educational product video. Triggers on course, coaching, masterclass, webinar, tutorial, education, teaching, training, class, program, academy.
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Generate scroll-stopping viral hook video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants viral content, TikTok hooks, Instagram Reels openers, YouTube Shorts, attention-grabbing video, scroll-stopper, pattern interrupt, or any short-form video designed to maximize retention and views. Triggers on any mention of viral, hook, scroll-stop, retention, views, engagement, short-form, TikTok, Reels, Shorts.
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Generate AI avatar and digital persona video prompts for Seedance 2.0 on Higgsfield. Use for virtual spokesperson content, digital twin videos, AI presenter clips, avatar-based marketing, virtual influencer content, or any video featuring a digital/AI-generated character as the main subject. Triggers on avatar, digital persona, virtual presenter, AI character, digital twin, virtual influencer, synthetic media, animated spokesperson.
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Review a Chinese mathematical-modeling problem-analysis section against the original problem, checking task classification, data reasoning, cross-problem linkage, model-choice rationale, validation planning, chapter boundaries, and the overall solution framework. Use for diagnosis, not automatic full rewriting.
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Query the bundled BZD mathematical-modeling dictionary for a user-selected model, explain its assumptions, inputs, outputs, limitations and validation, then judge whether it fits the current competition problem and data. Use when a user asks whether a proposed model is appropriate, requests model feasibility review, or wants dictionary-based alternatives for a modeling task.
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Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.
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Learn what the user cares about, prepare useful work privately, and prompt them with clear visual decisions. Use to start or continue Agency or handle its tickets.
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Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
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Consult PostgreSQL's pg_dump implementation for guidance on system catalog queries and schema extraction when implementing pgschema features
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Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
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