「training」の検索結果

Produce presenter-led AI avatar videos with Synthesia Studio and Synthesia API. Use for Synthesia-specific avatar video planning, avatar/voice/language selection, scripted scenes, template personalization, API job lifecycle, localization/dubbing, brand/training/product workflows, consent/rights/safety checks, artifact custody, troubleshooting, and final QA.

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Select, integrate, and operate multi-model image-generation gateways with model-specific schema discovery, version policy, asynchronous jobs, webhooks, spend approval, safe inputs and artifacts, data-governance review, and billing observability. Use for comparing or building against fal.ai, Replicate, or Together AI image APIs, including controlled failover; do not use for direct model-provider APIs, local inference, training, dedicated endpoint provisioning, video generation, or general image editing.

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Provider-independent production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation assets. Use when planning, scripting, storyboarding, generating, reviewing, localizing, or QAing animation whose primary purpose is learning.

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Provider-independent production workflow for AI avatar spokesperson videos, including presenter briefs, consent and likeness rights, disclosure and platform labeling, casting, localization, performance direction, brand fit, claims review, lip-sync/audio QA, accessibility, approvals, and delivery packages. Use when creating synthetic presenter videos, corporate explainers, training modules, sales/support avatars, localization spokespeople, product announcements, compliance videos, or social avatar clips.

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Use this skill for provider-independent reality capture that turns photographed or video-derived real places, objects, aerial sites, interiors, or people into photogrammetric meshes, NeRFs, or 3D Gaussian splats. It guides representation choice, capture planning, calibration and scale, reconstruction or training, cleanup, compression and LOD, coordinate and interchange handoff, visual/geometric QA, archival records, and property, location, people, and cultural rights checks. Do not use it for text-to-3D generation, synthetic asset invention, guaranteed metrology, or broad downstream finishing after handoff.

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Provider-independent explainer video production for AI agents creating educational, product, concept, nonprofit/public-service, onboarding, training, animated, mixed-media, and short social explainers. Use when an agent must turn a topic, brief, research corpus, product, process, policy, dataset, or complex idea into a factual, accessible, narrated, captioned, storyboarded, generated-media-ready explainer video with claim review, localization, pacing, variants, and QA.

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Provider-independent governance workflow for verifying and correcting human- or model-generated video descriptions. Use for pre-caption critique and post-caption revision, aspect-by-aspect factual review, critique precision/recall/constructiveness, second-stage peer review, calibration, appeals, versioned triplets, provenance, and acceptance reporting; not for pixel/audio QA, accessibility captioning, model training, or creative shot direction.

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Provider-independent localization and dubbing production direction for AI agents producing translated videos, dubbed ads, social clips, explainers, avatar videos, documentaries, training media, and multi-market campaigns. Use when planning or reviewing subtitle-versus-dub strategy, translation adaptation, glossary/style-guide setup, casting or voice matching, lip-sync or phrase-sync direction, synthetic dubbing boundaries, captions/subtitles handoff, accessibility, platform delivery, client review, or localization QA.

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Provider-independent captions and media accessibility direction for AI agents producing or finishing generated videos, ads, social clips, explainers, avatar videos, documentaries, podcasts/video recuts, training media, and localized content. Use when planning, authoring, reviewing, localizing, burning in, exporting, or QAing captions, subtitles, SDH, transcripts, audio description, flashing/motion safety, caption readability, or accessible media handoff files.

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Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, classifier training with nested CV, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.

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Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models. Transfers cell type labels without retraining on combined data. Use when annotating new single-cell datasets using pre-trained reference models.

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End-to-end EHR predictive modeling pipeline with PyHealth, covering dataset loading, task definition, model training, evaluation, calibration, and clinical interpretation.

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CesiumJS camera control - Camera, flyTo, lookAt, setView, ScreenSpaceCameraController, composable Controller camera controllers (1.144), CameraEventAggregator, flight animation. Use when positioning the camera, creating flyTo animations, constraining user navigation, adding custom or asset-inspection camera controllers, tracking entities, or converting between screen and world coordinates.

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You are an expert in Crawl4AI, the open-source web crawler built for AI applications. You help developers extract clean, structured data from websites for LLM training, RAG pipelines, and content analysis — with automatic markdown conversion, JavaScript rendering, CSS-based extraction, LLM-powered structured extraction, and session management for multi-page crawling.

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ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

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Context7 fetches current, version-specific documentation and code examples for software libraries and hands them to an AI coding agent, so generated code matches the library version in use instead of stale training data. Use when someone says "use context7", "get the latest docs for this library", "look up the current API", "the agent keeps using a deprecated method", "set up Context7 in Claude Code or Cursor", or asks how to call a framework whose API changed recently. Covers the ctx7 CLI, the MCP server and its two tools, library IDs and version pinning, API keys and rate limits, and the REST API.

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World-class explainer video expertise combining the visual storytelling of Kurzgesagt, the simplification genius of Common Craft, and the conversion science of high-performing SaaS landing pages. Explainer videos transform complex ideas into clear, compelling stories. Great explainers don't just inform—they illuminate. They take abstract concepts and make them feel obvious. The best explainer videos understand that simplicity is the ultimate sophistication, that showing beats telling, and that the goal isn't to impress with complexity—it's to make the viewer feel smart. Use when "explainer, explainer video, how it works, product demo, onboarding video, training video, educational video, whiteboard, animated explainer, explain this, simplify, concept video, SaaS video, explainer, video, animation, educational, product, demo, onboarding, SaaS" mentioned.

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The art and science of creating AI-powered digital presenters, avatars, and synthetic spokespersons. This skill covers HeyGen, Synthesia, D-ID, Tavus, and the emerging landscape of photorealistic AI humans that can speak any script in any language. Digital humans aren't replacing human presenters—they're enabling scale that humans can't achieve. A product demo in 50 languages. Personalized video messages for thousands of customers. 24/7 customer support with a friendly face. Training videos that can be updated without reshoots. The practitioners of this skill understand both the power and the responsibility. They know when digital humans enhance experiences and when they feel uncanny. They navigate the ethics of synthetic media thoughtfully. They create AI presenters that feel helpful, not deceptive. Use when "digital human, AI avatar, AI presenter, HeyGen, Synthesia, D-ID, Tavus, synthetic, talking head AI, AI spokesperson, personalized video, video at scale, multilingual video, AI actor, digital-humans, avatar, ai-presenter, synthesia, heygen, d-id, synthetic-media, personalization, multilingual" mentioned.

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World-class character and art style consistency for AI-generated images and videos - ensures visual coherence across series, maintains character identity, and provides rigorous QA before deliveryUse when "character consistency, art style, same character, consistent character, visual continuity, series, turnaround sheet, character sheet, reference image, character bible, style guide, anime character, consistent look, face consistency, outfit consistency, lora training, ip-adapter, flux kontext, visual qa, art quality, generation review, style drift, character drift, character-consistency, art-style, visual-qa, ai-art, image-generation, video-generation, anime, illustration, lora, ip-adapter, flux, midjourney, stable-diffusion" mentioned.

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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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产品营销 (PMM, Product Marketing) — 拥有「产品如何被定位 / 表达 / 发布 / 卖进市场」的从业者认知操作系统: 是产品 / 销售 / 市场 / 客户之间的连接组织, 负责把产品能力翻译成市场价值, 并把市场声音带回产品。覆盖 (a) 定位与差异化 (April Dunford 「刻意选择语境」框架 + 竞争性替代方案 + 卖价值不卖功能 + 选择「让你优势成立的市场参照系」, 对照 Ries & Trout 经典「定位是占领心智」), (b) 信息传递与叙事 (messaging 层级 / 信息屋 + 价值主张 + 收益导向 vs 战略叙事「换框架」Andy Raskin + 表达清晰度 Emma Stratton / Punchy + 初创 homepage 定位 FletchPMM), (c) 进入市场 (GTM) 策略与产品发布 (发布分级 T1/T2/T3 + 发布流程与跨职能编排 + GTM 打法 PLG vs SLG vs 混合 + 滩头细分选择), (d) 市场与竞争情报 (竞品分析 + battlecard 战卡 + 输赢分析 win/loss + 竞争赋能 Klue/Crayon/Clozd), (e) 买家与客户研究 (ICP 理想客户画像 + 买家 / 用户 persona + Jobs-to-be-Done 任务理论 Christensen/Moesta/Ulwick ODI + 客户之声 VoC + 用真实买家测信息 Wynter/Peep Laja), (f) 销售赋能 (战卡 + pitch / 销售 deck + 一页纸 + demo 叙事 + 异议处理 + 销售培训 + 内容采用率 — 你的产出只值销售实际用到的那部分), (g) 定价与打包 (价值定价 + 打包分层 + 《Monetizing Innovation》Ramanujam/Simon-Kucher + PLG 定价 — 常与产品 / 财务共担), (h) 细分与品类 (市场细分 + TAM/SAM/SOM + 技术采用生命周期与跨越鸿沟 Geoffrey Moore + 品类设计与叙事 Play Bigger / Christopher Lochhead — 稀有且昂贵, 不是默认选项), (i) 需求生成与内容协同 (campaign 信息 + 思想领导力 + 漏斗内容 TOFU/MOFU/BOFU + ABM 基于客户营销), (j) 分析师与影响者关系 (Gartner 魔力象限 + Forrester Wave + 分析师 briefing — To B 场景, 对 pay-to-play 影响诚实标注), (k) 客户营销与倡导 (参考客户 + case study + 评测站 G2/TrustRadius/Capterra 运营 + 社区), (l) PMM 运营与度量 (影响管道 + 赢率 + 发布采用率 + 信息穿透率 + 角色汇报线「向产品还是向市场」之争 + PMM 作为战略职能 vs 接单工具人陷阱); 跨 B2B SaaS (该学科重心) / B2C 消费 / 开发者 PLG / 平台产品。不含 产品管理 (PM 造产品 / 拥有 roadmap, PMM 拥有市场 — 相邻且极易混淆) / 增长 / 效果营销 / 付费投放 (平行学科) / 品牌 / 企业传播 / 公关为终点 / 需求生成为终点 (PMM 与之协同但不等同) / 平面设计 /「做 slide 和周边的团队」(PMM 抗争的接单工具人窄化) / 泛泛「市场营销」。 (Product Marketing (PMM) — the cognitive operating system of practitioners who own how a product is positioned, messaged, launched, and sold into a market: the connective tissue between product, sales, marketing, and customers, responsible for translating product capability into market value and bringing the voice of the market back into product. Covers (a) positioning & differentiation (April Dunford's deliberate-context framing, competitive alternatives, value-not-features, framing the market in which your strengths matter — vs Ries & Trout classic perception-in-the-mind positioning), (b) messaging & narrative (messaging hierarchy / house, value proposition, benefit-led vs strategic-narrative / change-the-frame messaging Andy Raskin, clarity craft Emma Stratton/Punchy, homepage & startup positioning FletchPMM), (c) go-to-market (GTM) strategy & product launches (launch tiers T1/T2/T3, launch process & cross-functional orchestration, GTM motion PLG vs SLG vs hybrid, beachhead segment selection), (d) market & competitive intelligence (competitive analysis, battlecards, win/loss analysis, competitive enablement Klue/Crayon/Clozd), (e) buyer & customer research (ICP ideal customer profile, buyer & user personas, Jobs-to-be-Done Christensen/Moesta/Ulwick ODI, voice of customer, message testing with real buyers Wynter/Peep Laja), (f) sales enablement (battlecards, pitch & sales decks, one-pagers, demo narratives, objection handling, sales training, content adoption — output is only as good as what sales actually uses), (g) pricing & packaging (value-based pricing, packaging & tiering, Monetizing Innovation Ramanujam/Simon-Kucher, PLG pricing — often shared with product/finance), (h) segmentation & category (market segmentation, TAM/SAM/SOM, technology adoption lifecycle & crossing the chasm Geoffrey Moore, category design & narrative Play Bigger / Christopher Lochhead — rare and expensive, not a default), (i) demand-gen & content partnership (campaign messaging, thought leadership, funnel content TOFU/MOFU/BOFU, ABM account-based marketing), (j) analyst & influencer relations (Gartner Magic Quadrant, Forrester Wave, analyst briefings — for B2B, with honest read on pay-to-play perception), (k) customer marketing & advocacy (references, case studies, reviews G2/TrustRadius/Capterra presence, community), (l) PMM operations & metrics (influenced pipeline, win rate, launch adoption, message pull-through, the role's reporting line product-vs-mark

日本語の概要は準備中です。原文の説明を表示しています。

K12 体育培训 (K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。) Master OS — automated mastery of K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「K12 体育培训」「青少年体育培训」「少儿体适能」「中考体育」「体育中考」

日本語の概要は準備中です。原文の説明を表示しています。