「gym」の検索結果

健身私教 — 为客户设计、指导和推进个性化训练方案的专业工艺, 有别于团课教练、物理治疗师或运动队教练: (a) 训练方案设计与周期化 (线性 / 波动 / 板块周期化 Bompa & Haff; NSCA《力量训练与体能训练精要》行业圣经; Schoenfeld 肌肥大机制; Helms/Valdez/Morgan 肌肉与力量金字塔; 共轭训练法 Simmons/Westside; Starting Strength 线性渐进 Rippetoe; Renaissance Periodization 训练量地标 Israetel); (b) 动作评估与筛查 (FMS 功能性动作筛查 Gray Cook; NASM CEx 纠正性训练连续体 抑制-拉伸-激活-整合; 关节逐级方法 Cook/Boyle; 姿势与代偿模式识别; PAR-Q+ 与 ACSM 运动前筛查指南); (c) 教学与指令 (外部 vs 内部注意焦点 Wulf; 差异化学习 Schöllhorn; 约束导向方法 Davids/Renshaw; RIR/RPE 自动调节 Zourdos/Helms; 动机访谈在健身中的应用 Rollnick/Miller); (d) 营养指导的执业边界 (宏量营养素周期化; Precision Nutrition / ISSN 立场声明的循证建议; 边界: 私教提供一般性指导, 医学营养治疗需 RD/RDN 资质; 能量可用性与 RED-S Mountjoy IOC 共识); (e) 商业与客户管理 (客户留存与方案依从; 咨询与销售伦理; 线上教练平台与规模化 Goodman PTDC; 方案交付工具 TrueCoach/Trainerize/TrainHeroic; 课时定价模型; 独立教练 vs 健身房雇员); (f) 专项方向 (运动表现 CSCS; 纠正性训练 CES; 老年健身 SFN; 产前产后; 青少年 YSCA; 适应性 / 无障碍健身; 健美 / 形体; 力量举; 耐力运动 S&C)。诚实处理: 兄弟科学 vs 循证之争, 补剂行业利益冲突, 合成代谢激素使用现状 (诚实但不推广: 匿名调查约 30% 有经验男性健身者使用过 — 业内估, Kanayama/Pope/Hudson 记录健康风险, WADA 禁用清单用于竞技语境), 体型焦虑与进食障碍风险 (男性私教肌肉变形症, 正食症倾向, NEDA 负责任语言指南), 健身房掠夺性销售 (高压推销、误导性对比照、长约锁定), 执业边界违规 (私教诊断伤病或超范围开饮食处方), 线上教练认证造假 (周末速成、未经认可的认证)。不含: 物理治疗 / 康复治疗 (持照医疗职业), 不含: 团课教练 (Les Mills/Zumba/CrossFit 分馆教练 — 相邻但不同), 不含: 精英竞技运动教练 (团队项目周期化, 不同职业路径), 不含: 营养作为主业 (临床营养学需单独资质)。 (Personal Trainer — the craft of designing, coaching, and progressing individualized exercise programs for clients, distinct from group fitness instruction, physiotherapy, or sports coaching: (a) exercise programming and periodization (linear / undulating / block periodization per Bompa & Haff; NSCA Essentials of Strength Training and Conditioning as industry bible; Schoenfeld on hypertrophy mechanisms; Helms/Valdez/Morgan Muscle & Strength Pyramids; conjugate method Simmons/Westside; Starting Strength linear progression Rippetoe; Renaissance Periodization volume landmarks Israetel); (b) movement assessment and screening (FMS Gray Cook; NASM CEx corrective exercise continuum inhibit-lengthen-activate-integrate; joint-by-joint approach Cook/Boyle; posture and compensation pattern identification; PAR-Q+ and pre-exercise screening ACSM guidelines); (c) coaching and cueing (external vs internal cueing Wulf attentional focus research; differential learning Schöllhorn; constraints-led approach Davids/Renshaw; RIR/RPE autoregulation Zourdos/Helms; motivational interviewing for adherence Rollnick/Miller adapted to fitness); (d) nutrition guidance within scope of practice (macronutrient periodization; evidence-based recommendations per Precision Nutrition / ISSN position stands; scope boundary: PT gives general guidance NOT medical nutrition therapy which requires RD/RDN credential; energy availability and RED-S awareness Mountjoy IOC consensus); (e) business and client management (client retention and programming adherence; consultation and sales ethics; online coaching platforms and scaling Goodman PTDC; programming delivery tools like TrueCoach/Trainerize/TrainHeroic; session pricing models; independent contractor vs gym employee dynamics); (f) specialization tracks (sports performance CSCS; corrective exercise CES; senior fitness SFN; pre/postnatal; youth YSCA; adaptive/inclusive fitness; bodybuilding/physique coaching; powerlifting/strength sports; endurance athlete S&C). Evidence-based practice as the core ethos: Schoenfeld/Krieger meta-analyses on volume-response, Helms systematic reviews on protein/resistance training, Brad Schoenfeld's MASS journal bridging research to practice, Menno Henselmans and Eric Helms as practitioner-researchers, Greg Nuckols Stronger By Science as translation layer. Chinese fitness industry context: rapid growth of personal training in commercial gyms (Keep/Lefit/Megafit chains), NSCA-CPT and ACE-CPT as dominant certifications alongside local CSFA (China Sport and Fitness Association) credential, '体适能' (physical fitness) as formal term, online coaching boom post-COVID, PT business models in tier 1-3 cities diverge sharply. Honest treatment of: bro science vs evidence-based d

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Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.

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Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Covers the native 5.0 build and environment API, published 3.0.0 Gymnasium/PettingZoo adaptation, and a pinned historical 4.0 profile.

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This skill is designed to help users automatically extract business data from Google Maps search results. The Agent should proactively apply this skill when the user makes the following requests searching for coffee shops in a specific city, finding dentists or medical clinics nearby, tracking competitors' locations in a certain area, extracting business leads from Google Maps lists, gathering restaurant data for market research, finding hotels or accommodation options in a region, locating specific services like coworking spaces or gyms, monitoring new business openings in a neighborhood, collecting contact information and addresses for sales prospecting, analyzing price ranges and cuisines of local eateries, getting ratings and review counts for a list of businesses, exporting local business data into a CRM or database.

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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

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Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger. Do NOT use for: bug fixes, code review, documentation, refactoring, dependency updates, or single-file changes.

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Use when building a Unity 6.3 level or open world: terrain from code or heightmaps, procedural islands, erosion, splat and foliage rules, trees and grass, ProBuilder graybox, level gym, modular kit and prefab placement, set dressing, landmarks and player guidance, the island's edge, spline roads, WFC, fog, additive scene streaming, or a world that hitches, pops in, leaks memory or runs slow on mobile. Keywords: Terrain, TerrainData, ProBuilder, blockout, graybox, modular kit, NavMesh, LoadSceneAsync, open world.

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Scrape Google Maps for local businesses by category and location, output CSV ready for cold email enrichment. Best for SMB campaigns targeting restaurants, clinics, gyms, salons, contractors, etc. Uses RapidAPI Maps Data API. Output feeds directly into /blitz-list-builder (to find owner contacts) or //list-builder Phase 5 (if you have names already).

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TypeScript and JavaScript expert with deep knowledge of type-level programming, performance optimization, monorepo management, migration strategies, and modern tooling. Use PROACTIVELY for any TypeScript/JavaScript issues including complex type gymnastics, build performance, debugging, and architectural decisions. If a specialized expert is a better fit, I will recommend switching and stop.

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Custom training plans by goal (strength, cardio, flexibility). Progressive overload programming, rest day optimization, home vs gym adaptations, deload weeks.

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Build promo videos for local businesses in HyperFrames -- the new-menu drop, the seasonal offer, the event announcement, the "we're hiring" post, the Google Business Profile clip. Restaurant, salon, gym, trades: 15-30 seconds, brand-clean, captioned, sized for Instagram, Facebook, and the profile.

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Use DreamerV2 2.2.0 for TensorFlow world-model reinforcement-learning training, custom Gym integration, typed experiment configuration, replay/checkpoint management, and JSONL/TensorBoard evaluation.

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Guide agents through AgiBot X1 humanoid reinforcement-learning training, checkpoint playback, policy export, and MuJoCo sim2sim workflows with verified configuration contracts and explicit Isaac Gym limits.

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Think and work like an expert Computer Security Researcher. Use when a task calls for Computer Security Researcher judgment. Reasons from explicit threat models and CIA/STRIDE through AFL++/libFuzzer triage, ASan/KASAN oracles, ProVerif/Tamarin proofs, CVE/CWE/CAPEC taxonomies, CyberGym dual-execution benchmarks, Menlo/CVD ethics, and USENIX open-science artifact norms.

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Suede-owned recovery discipline for recurring charges billed outside Amazon: App Store, Google Play, PayPal, direct-bill streaming, gyms, news, and SaaS. Use when the user wants to find, audit, cancel, or dispute a subscription they may have forgotten, is being charged for twice, or no longer uses. Every cancellation and dispute requires the user to name the service first. Never enters payment credentials and never promises a refund. Requires Claude in Chrome for browser actions. NOT FOR: Amazon returns, restocking fees, or Amazon-billed Prime Video Channels, Audible, Kindle Unlimited, or Prime (use amazon-returns-recovery); merchant-side dunning and cancel-flow design (use suede-churn-prevention).

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Machine learning environment for automated theorem proving with Coq.

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สร้าง workout program 4 สัปดาห์ + exercise library + progression + tracking sheet สำหรับ home/gym

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Gym workout planner and nutrition tracker. Search 690+ exercises by muscle, equipment, or category via wger. Look up macros and calories for 380,000+ foods via USDA FoodData Central. Compute BMI, TDEE, one-rep max, macro splits, and body fat — pure Python, no pip installs. Built for anyone chasing gains, cutting weight, or just trying to eat better.

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TypeScript and JavaScript expert with deep knowledge of type-level programming, performance optimization, monorepo management, migration strategies, and modern tooling. Use PROACTIVELY for any TypeScript/JavaScript issues including complex type gymnastics, build performance, debugging, and architectural decisions.

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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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