1.1万 件 ・ 関連度順
概要と使いどころ
AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AI SDR Skill workflow skill. Use this skill when the user needs When the user wants to deploy AI sales development reps, automate sales qualification, build signal-to-action routing, or design AI agent architecture for sales. Also use when the user mentions 'AI SDR,' 'AI sales agent,' 'automated qualification,' 'signal routing,' 'sales automation,' '11x,' 'Artisan,' 'AiSDR,' 'AI BDR,' or 'autonomous sales.' This skill covers AI SDR deployment, qualification automation, and agent architecture for sales development. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
国内房产投资(房产投资人视角 — 城市与地段选择 / 政策与调控解读 / 一手房与二手房 / 房产金融与杠杆 / 估值与尽职调查 / 交易流程与税费 / 持有运营与出租 / 退出与流动性 / 风险识别 / 房产在个人资产配置中的位置 / 投资者的决策框架与心态 — 聚焦中国大陆,2021 年以来深度下行期的真实市场) (China domestic real estate investing — 国内房产投资人 / 投资顾问的实践视角(城市与地段选择 / 政策与调控解读 / 一二手房与房产金融 / 估值与尽调 / 交易税费 / 持有运营与退出 / 风险识别 / 资产配置中的房产)) Master OS — automated mastery of China domestic real estate investing — 国内房产投资人 / 投资顾问的实践视角(城市与地段选择 / 政策与调控解读 / 一二手房与房产金融 / 估值与尽调 / 交易税费 / 持有运营与退出 / 风险识别 / 资产配置中的房产): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on China domestic real estate investing — 国内房产投资人 / 投资顾问的实践视角(城市与地段选择 / 政策与调控解读 / 一二手房与房产金融 / 估值与尽调 / 交易税费 / 持有运营与退出 / 风险识别 / 资产配置中的房产) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「房产投资」「买房」「房地产」「real estate」「国内房产」
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。
短视频投流 (short-video paid advertising (China platforms)) Master OS — automated mastery of short-video paid advertising (China platforms): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on short-video paid advertising (China platforms) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「投流」「巨量引擎」「千川」「信息流」「创意素材」
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。
数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N
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。
独立开发者与微型 SaaS — 单人或极小团队 (≤3 人) 构建可持续订阅收入的软件产品商业, 有别于自由职业/咨询、企业级 SaaS 和开源维护: (a) 产品发现与验证 (自己的痒 vs 市场优先 Nugent/Walling; 着陆页冒烟测试; 先接 Stripe 再写代码; JTBD 访谈适配独立开发者场景; Reddit/HN/X/社区痛点挖掘; Mom Test 验证框架; 公开构建作为验证机制); (b) 独立开发者技术架构 (无聊技术论 McKinley; 单体优先; serverless vs VPS 在 ≤$100/月预算下的取舍; Rails/Django/Laravel/Next.js 等框架的出货速度选型; 托管服务优先于自建; Supabase/PlanetScale/Neon 数据库即服务; Clerk/Auth0 认证即服务; Stripe/Paddle/LemonSqueezy 支付; Vercel/Fly.io/Railway 部署; AI 辅助编码 Cursor/Copilot 作为力量倍增器); (c) 无营销团队的分发与增长 (SEO 作为微型 SaaS 护城河; Product Hunt 发布; AppSumo 终身授权利弊; 冷邮件; Twitter/X 公开构建; IndieHackers 社区分发; 集成市场 Shopify/WordPress/Zapier/Slack 应用目录; 联盟计划; 一人内容营销); (d) 定价与变现 (SaaS 定价心理学; freemium vs 免费试用 vs 纯付费; 按席位 vs 按用量 vs 固定费率; 年付折扣; 老用户保价; 微型 SaaS 流失率控制; MRR/ARR/LTV/CAC 在微型规模的含义; $10K MRR 里程碑心理学); (e) 独立创始人心智模型与生活设计 (default alive vs default dead Graham; 拉面盈利 Levels; 生活方式生意 vs 增长生意的光谱; 独立开发者倦怠预防; 时间管理与上下文切换成本; 地理套利与远程优先; 一次构建反复销售的资产思维; 社区作为支持网络 IndieHackers/WIP/MicroConf); (f) 法务与运营基础 (公司注册地选择 LLC/Ltd/GmbH 税效; Stripe Atlas vs Firstbase vs 本地律师; 服务条款与隐私政策; GDPR/CCPA 低成本合规; 知识产权基础; 首次雇人的承包商 vs 员工选择; 独立开发者会计 SaaS — Xero/QuickBooks; 多辖区税务义务)。中国独立开发者生态: 出海作为主导策略 (从中国构建面向全球市场的产品以避开国内竞争/监管); 即刻/V2EX/少数派作为社区枢纽; 微信小程序作为替代分发; 小报童/爱发电作为中文市场变现; 人民币定价心理学; 支付宝/微信支付集成; ICP 备案要求; 中国区 App Store 审核差异; 独立开发者周刊。诚实处理: 生存偏差 (每一个 Pieter Levels 背后是数千个从未达到 $1K MRR 的人), AI 对微型 SaaS 生态位的颠覆 (wrapper 产品脆弱), 独自构建的孤独与心理健康成本, 公开构建的表演性质 vs 真正透明, AppSumo 终身授权上瘾毁坏单位经济, 样板代码销售的伦理边界。不含: 企业级 SaaS (不同 GTM/销售/融资模型), 不含: 自由职业/咨询 (用时间换钱非建资产), 不含: 开源维护 (不同可持续性模型), 不含: 风投支持的创业 (不同增长期望), 不含: 无代码/低代码平台构建 (相邻但不同受众), 不含: 内容创作作为主业。 (Independent Developer and Micro-SaaS — the craft of single-person or tiny-team (≤3 people) software product businesses that generate sustainable recurring revenue without venture capital, distinct from freelancing/consulting, enterprise SaaS, and open-source maintainership: (a) product discovery and validation (scratching your own itch vs market-first Nugent/Walling; landing page smoke tests; Stripe checkout before code; Jobs-to-Be-Done interview adapted to solo context; Reddit/HN/X/community pain-point mining; idea validation frameworks Fitzpatrick Mom Test; build-in-public as validation mechanism); (b) technical architecture for solo operators (boring technology thesis Mckinley; monolith-first; serverless vs VPS tradeoffs for ≤$100/mo infra; framework selection for shipping speed — Rails/Django/Laravel/Next.js/Remix/SvelteKit; managed services over self-hosted whenever possible; database-as-a-service Supabase/PlanetScale/Neon/Turso; auth-as-a-service Clerk/Auth0/Supabase Auth; payment integration Stripe/Paddle/LemonSqueezy; deployment Vercel/Fly.io/Railway/Render; monorepo vs polyrepo for solo; AI-assisted coding Cursor/Copilot as force multiplier); (c) distribution and growth without a marketing team (SEO as moat for micro-SaaS Ahrefs-style content marketing on 1-person budget; Product Hunt launches; AppSumo lifetime deals tradeoffs; cold email outreach; Twitter/X building in public; IndieHackers community distribution; integration marketplaces Shopify/WordPress/Zapier/Slack app directories as channels; affiliate programs; content marketing at scale-of-one); (d) pricing and monetization (SaaS pricing psychology Patel/Campbell; freemium vs free trial vs paid-only; per-seat vs usage-based vs flat-rate; annual discount strategies; grandfathering existing customers; churn reduction for micro-SaaS — Baremetrics/ProfitWell/ChartMogul analytics; MRR/ARR/LTV/CAC at micro scale; the $10K MRR milestone psychology); (e) solo founder mental model and lifestyle design (default alive vs default dead Graham; ramen profitability Levels; lifestyle business vs growth business spectrum; burnout prevention for solo operators — no team to delegate to; time management and context-switching costs; geographic arbitrage and remote-first; the 'build once sell twice' asset mindset; community as support network IndieHackers/WIP/MicroConf); (f) legal and operational basics (incorporation LLC/Ltd/Gm
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。
恋爱高手 (dating and relationship coaching) Master OS — automated mastery of dating and relationship coaching: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on dating and relationship coaching problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「恋爱」「脱单」「约会」「情感」「暧昧」
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。
游戏设计 (Game Design) — 设计游戏的规则 / 系统 / 机制 / 体验 的从业者认知操作系统 (以电子游戏为主, 含桌游 / 卡牌 / 棋盘游戏): 决定「玩家做什么、为什么有意义、每一刻的体验如何」的学科 —— 区别于游戏程序 (引擎 / 代码)、游戏美术 (视觉 / 音频)、游戏制作 (排期 / 预算 / 团队)。覆盖 (a) 核心设计理论与框架 (MDA 机制-动态-美学 Hunicke/LeBlanc/Zubek + Schell 的「透镜」+ Koster「乐趣即学习」理论 + Sid Meier「一连串有趣的决策」+ Salen & Zimmerman「有意义的游玩」/「二阶设计」/「魔法圈」+ Costikyan 论不确定性 + Suits/Huizinga 游戏哲学), (b) 系统与机制设计 (核心循环 + 游戏经济 sink/source/产出-消耗 + 正负反馈环 + 资源与成长系统 + 涌现 vs 脚本 Will Wright「可能性空间」+ Machinations 经济建模 Dormans + 支配 / 退化策略 + 正交设计 Garfield), (c) 关卡与遭遇设计 (灰盒 blockout + 节奏与张力 + 预告 telegraphing + 主线 vs 探索 + 环境叙事 + 锁钥 gating + 难度曲线), (d) 游戏手感与「juice」(Steve Swink《Game Feel》+ 即时响应 + 屏震 / 反馈 / 打磨 + 输入与镜头 + 「手感」是设计出来的不是附带的), (e) 玩家心理与 UX (Celia Hodent《The Gamer's Brain》+ 心流通道 挑战 vs 技能 Csikszentmihalyi/陈星汉 Jenova Chen + Bartle 玩家类型 + 动机与奖励节奏 + 新手引导 FTUE / 教学化 + 认知负荷 + 无障碍), (f) 平衡与调优 (Ian Schreiber《Game Balance》+ 对称 / 非对称 + 动态难度 DDA + 传递 / 非传递平衡 + nerf/buff + 消除支配策略 + 测试驱动调优), (g) 叙事与任务设计 (ludonarrative 失调 / 和谐 + 环境与系统叙事 + 分支 vs 线性 + Amy Hennig 角色驱动 + 任务结构), (h) 以玩家为中心的迭代流程 (Tracy Fullerton《Game Design Workshop》playcentric + 纸面原型 + 垂直切片 + 测试协议「看而不说 / 你不是你的玩家」+ 快速迭代 + game jam), (i) 类型与形态工艺 (roguelike/roguelite 与程序生成 Derek Yu + 平台跳跃 + RPG 系统 + 策略 4X + 解谜设计 Jonathan Blow + 多人 / PvP 平衡 + 桌游 / 卡牌设计 Reiner Knizia/Richard Garfield/Brenda Romero), (j) F2P / 长线运营 / 商业化设计 (核心 / 元 / 经济循环 + 抽卡 gacha / 开箱 loot box / 战令 battle pass 机制 + 留存 D1/D7/D30 + 转化 + 鲸鱼 + live ops 内容节奏 —— 并诚实处理掠夺式暗黑模式 / 强迫循环 / 剥削式商业化的伦理), (k) 设计师角色与工艺职业 (系统 / 关卡 / 叙事 / UX 设计师之分 + 通才 vs 专才 + 独立作者 vs 3A 团队 + 设计文档 + 传达设计意图 + GDC 工艺社区)。跨 3A / 独立 / 手游 F2P / 桌游。不含 游戏程序 / 引擎 (Unity/Unreal 编码 / 图形 / 网络同步) / 游戏美术 / 动画 / 音频 / 游戏制作 / 项目管理 / 排期 / QA / 游戏作为生意 / 发行 / 市场 / 电竞运营为终点 / 泛泛「我想做游戏」的 game-dev 教程 —— 本 skill 是「设计」学科: 规则 / 系统 / 机制 / 体验。 (Game Design — the cognitive operating system of practitioners who design the rules, systems, mechanics, and experiences of games (video games primarily, plus tabletop/board/card games): the discipline of deciding WHAT the player does, WHY it is meaningful, and HOW the moment-to-moment experience feels — distinct from game programming (builds the engine/code), game art (visuals/audio), and game production (schedule/budget/team). Covers (a) core design theory & frameworks (MDA Mechanics-Dynamics-Aesthetics Hunicke/LeBlanc/Zubek, Schell's Lenses, Koster's theory of fun = learning, Sid Meier 'a series of interesting decisions', Salen & Zimmerman 'meaningful play'/'second-order design'/'magic circle', Costikyan on uncertainty, Suits/Huizinga play philosophy), (b) systems & mechanics design (core loops, game economy sink/source/faucet-drain, feedback loops positive/negative, resource & progression systems, emergence vs scripting Will Wright 'possibility space', Machinations economy modeling Dormans, dominant/degenerate strategy, orthogonal design Garfield), (c) level & encounter design (blockout/greybox, pacing & tension, telegraphing, critical path vs exploration, environmental storytelling, gating & lock-and-key, difficulty curve), (d) game feel & juice (Steve Swink 'Game Feel', moment-to-moment responsiveness, screenshake/feedback/polish, input & camera, the 'feel' is designed not incidental), (e) player psychology & UX (Celia Hodent 'The Gamer's Brain', flow channel challenge-vs-skill Csikszentmihalyi/Jenova Chen, Bartle player types, motivation & reward schedules, onboarding/FTUE/tutorialization, cognitive load, accessibility), (f) balancing & tuning (Ian Schreiber 'Game Balance', symmetry/asymmetry, dynamic difficulty adjustment DDA, transitive/intransitive balance, nerf/buff, dominant strategy elimination, playtesting-driven tuning), (g) narrative & quest design (ludonarrative dissonance/harmony, environmental & systemic storytelling, branching vs linear, Amy Hennig character-driven, quest structure), (h) player-centric iterative process (Tracy Fullerton 'Game Design Workshop' playcentric design, paper prototyping, vertical slice, playtesting protocol 'watch don't talk / you are not your play
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。
求职 / 面试辅导(求职教练视角 — 求职定位与职业叙事 / 简历与 ATS 优化 / 求职渠道与内推策略 / 职场社交 / 行为面试与专业面试 / 面试心理与表达 / 谈薪与 offer 决策 / 校招与应届生 / 转行求职 / 特殊群体求职 / 求职教练的执业经营 — 涵盖中国大陆求职生态 + 海外英语区求职体系,两套差异极大) (Job-hunting & interview coaching — 求职教练 / 面试辅导师的实践视角(求职定位与职业叙事 / 简历与 ATS / 渠道与内推策略 / 行为与专业面试 / 谈薪与 offer 决策 / 校招与转行 / 求职教练的执业经营)) Master OS — automated mastery of Job-hunting & interview coaching — 求职教练 / 面试辅导师的实践视角(求职定位与职业叙事 / 简历与 ATS / 渠道与内推策略 / 行为与专业面试 / 谈薪与 offer 决策 / 校招与转行 / 求职教练的执业经营): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Job-hunting & interview coaching — 求职教练 / 面试辅导师的实践视角(求职定位与职业叙事 / 简历与 ATS / 渠道与内推策略 / 行为与专业面试 / 谈薪与 offer 决策 / 校招与转行 / 求职教练的执业经营) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「求职」「找工作」「面试」「面试辅导」「求职辅导」
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。
First-encounter orientation on a repository nobody here has worked in yet. Runs a fixed five-step workflow — venv setup, tarball fetch, tree-sitting structural scan, featuring synthesis, then reasoning over the two — and yields an account of what the repo contains and how it is arranged, optionally written out as _FEATURES.md. Use for "I just cloned this", "what is this repo", "what does this do", "explore this repo", "give me an orientation", "what are the main features", "review what's new in this repo", or before starting work in a codebase you have not seen. This is the divergent what's-here skill. Route elsewhere for: a named symbol, a file's structure or a line range (tree-sitting); all callers of a Python symbol (searching-codebases); teaching a human the codebase through exercises (orienting-codebases); fetching or cloning a repo without analysing it (accessing-github-repos, cloning-project).
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。
Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
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。
This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.
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。
Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.
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。
Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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。
Save the currently active browser page to the link collection at saved.owenyoung.com. Use when explicitly invoked as $jj, or when the user asks to bookmark or collect the current page there. Read the page without navigating, generate a concise Chinese title, SEO slug, and distinctive Chinese Markdown description, attach the complete article or main-page text as Chinese-only Markdown, match collections, create one authenticated link record, and return its clickable URL.
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。
Use when adding character/enemy/game-flow states or NPC behaviour - explicit transition tables instead of flag soup, hierarchical and pushdown machines, behaviour trees per engine, navmesh pathfinding, AI tick budgets, and table-driven tests.
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。
Archive multiple completed changes at once. Use when archiving several parallel changes.
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。
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
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。
Comprehensive skill for building, migrating, and maintaining Astro v7 projects. Covers best practices from the official AGENTS.md, the v6→v7 migration path, validation of breaking/deprecated patterns, AI-enhanced dev server usage (background mode, JSON logging), advanced routing with src/fetch.ts, route caching, Sätteri Markdown, and the Rust compiler. Use when the user mentions 'Astro', '.astro files', 'astro dev', 'astro build', 'islands architecture', 'content collections', 'SSG', 'SSR adapter', 'upgrade to Astro 7', 'migrate Astro', 'Astro v7', 'Astro v6', 'Sätteri', 'route caching', 'Astro.cache', 'astro dev --background', 'src/fetch.ts', 'advanced routing', 'Hono + Astro', 'Rolldown', 'Vite 8', 'queued rendering', 'CDN cache provider', 'Astro AI', 'related content', 'related posts', 'vector embeddings Astro', 'astro-related-content', or asks about static site generation with Astro.
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。
Master Test Architect and Quality Advisor. Use when the user asks to talk to Murat or requests the Test Architect.
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。
Research YouTube topics, analyze competitor videos, deconstruct viral content, and query the YouTube Data API. Use when researching a video topic before planning, analyzing video transcripts for viral patterns, searching competitor channels, or fetching video and channel stats via the YouTube Data API v3.
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。
Comprehensive brand messaging, positioning, and value proposition development using proven frameworks including Peep Laja Message Layers, Osterwalder Value Proposition Canvas, Geoffrey Moore positioning, April Dunford's Five Components, StoryBrand SB7, Andy Raskin Strategic Narrative, and Messaging House. Use when developing brand identity, brand messaging architecture, positioning statements, value propositions, messaging hierarchies, brand pillars, taglines, one-liners, elevator pitches, brand guidelines, visual identity systems, or creating Positioning & Messaging Packs. Triggers include: messaging framework, brand positioning, value prop, messaging architecture, brand pillars, brand identity, StoryBrand, positioning statement, brand guidelines, design system, messaging house, corporate identity, brand voice, visual standards.
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。