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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。

摄影 (摄影 (Photography) — 以静态影像为产出的视觉创作 + 服务行业：从看见 → 用光 → 构图 → 拍摄 → 后期 → 交付的认知操作系统，覆盖商业摄影 (commercial: 产品/广告/电商/美食)、人像摄影 (portrait: 影楼/写真/婚礼/时尚/环境人像)、纪实与街头摄影 (documentary/photojournalism/street)，以及与之贯通的风光、新闻、艺术摄影。面向职业摄影师 / 助理 / 修图师 / 想转职业的资深爱好者 / 服务这行的人 (器材厂商、图库、后期外包、影楼运营)。覆盖: (a) 第一性张力 — **光 (light is the craft — 看光、控光、塑光：硬光/柔光、方向、色温、光比、布光是摄影第一性) ⇄ 器材 (gear — 相机/镜头/灯具迷信, 'GAS' gear-acquisition-syndrome, 'the best camera is the one you have with you' — 器材不是摄影)**; **决定性瞬间/抓取 (decisive moment, Cartier-Bresson, 等待与预判, 纪实/街头不可重来) ⇄ 构建/导演 (constructed/directed, 商业与人像是布光摆姿造光的'制造'而非'捕获')**; **前期/拍对 (get it right in camera, 曝光/白平衡/构图当场拍对) ⇄ 后期/暗房 (post/darkroom, Ansel Adams 'negative is the score, print is the performance' — RAW 冲洗/调色/修图是创作一半)**; **作者性/艺术表达 (vision, personal voice, 'photograph what you feel not what you see') ⇄ 服务性/商业交付 (client brief, 影楼标准化出片, 'shoot the brief not your ego', 摄影是生意)**; **真实/未操纵 (truth, photojournalism ethics, 不过度 PS, 新闻摄影伦理) ⇄ 修饰/理想化 (retouch, 商业人像液化磨皮, 广告的'谎言')**; **决定性技术参数 (曝光三角 aperture/shutter/ISO, 景深, 互易律) ⇄ 看见/眼力 (seeing, 'photography is about seeing not cameras', 构图/光感是 tacit 眼力)**; **胶片/慢/不可见 (film, 慢工, 不可即时回看, 暗房) ⇄ 数码/即时/海量 (digital, chimping, 连拍, 后期无限)**; AI 对摄影的冲击 ('是不是摄影'、生成式填充/换天/AI 修图、真实性危机); (b) 核心工作流 / pipeline (最标准、最易蒸高质量+CLI 化) — 通用链: 看光选光 → 曝光三角决策 (光圈定景深/快门定动静/ISO 兜底) → 测光与曝光 (评价/点测光, 向右曝光 ETTR, 直方图, 区域系统) → 构图 (三分法/引导线/框架/负空间/前景) → 对焦 → 拍摄 → 选片 culling → RAW 后期 (Lightroom/Capture One: 曝光/白平衡/色调曲线/HSL/局部) → 精修 retouch (Photoshop: 频率分离/液化/D&B) → 调色 grading → 输出交付 (色彩管理/sRGB/打印/图库上传); 子流派 SOP: 影棚商业布光 (主光/辅光/轮廓光/背景光, 三点布光, 光质修饰器 softbox/beauty-dish/grid)、婚礼/纪实一天流程、街头扫街、产品/电商台面、人像引导摆姿 posing & direction; (c) 工具栈 — 相机系统 (无反主导: Sony A 系 / Canon R / Nikon Z / 富士 X&GFX / 徕卡 M; 中画幅 Phase One/Hasselblad/GFX 商业; 残幅 vs 全幅 vs 中幅)、镜头 (大三元, 定焦 vs 变焦, 35/50/85 人像三剑客, 微距, 移轴)、灯具 (机顶闪 Godox/Profoto, 影室灯, 常亮 LED, 持续光 vs 闪光)、修饰器 (柔光箱/反光伞/束光筒/雷达罩/旗板)、测光/附件 (测光表, 灰卡, 三脚架, 滤镜 ND/CPL/渐变)、后期软件 (Adobe Lightroom Classic/CC 主导, Capture One 商业/影室, Photoshop 精修, DxO PhotoLab/PureRAW 降噪, Capture One vs Lightroom 之争, 国产: 醒图/泼辣 Polarr/Snapseed 移动端, RawTherapee/Darktable 开源, AI: Lightroom 生成式/Photoshop Generative Fill/Topaz Photo AI/Gigapixel/Luminar Neo)、色彩管理 (X-Rite/Calibrite 校色仪, ICC, 显示器)、图库/交付 (Pixieset/图虫/500px/视觉中国/Getty/Adobe Stock, 客片在线选片); (d) 知识正典 — Ansel Adams 'The Camera / The Negative / The Print' 三部曲 + 区域系统 (Zone System)、Henri Cartier-Bresson 'The Decisive Moment'、Michael Freeman 'The Photographer's Eye' (构图) 'The Photographer's Mind'、Bryan Peterson 'Understanding Exposure' (曝光入门圣经)、Scott Kelby 'Digital Photography Book' 系列 + Lightroom/Photoshop 书、Joe McNally 'The Hot Shoe Diaries / Sketching Light' (闪光灯)、Light Science & Magic (布光科学经典教材)、Roland Barthes 'Camera Lucida' (摄影哲学)、Susan Sontag 'On Photography'、John Szarkowski 'The Photographer's Eye'、National Geographic 摄影指南、Robert Capa / Sebastião Salgado / Steve McCurry (纪实)、Annie Leibovitz / Richard Avedon / Peter Lindbergh (人像/时尚)、Galen Rowell / Ansel Adams (风光); (e) figures/流派 — 风光/系统派 (Ansel Adams 区域系统奠基、Galen Rowell 探险风光、Marc Adamus); 决定性瞬间/纪实街头派 (Henri Cartier-Bresson 奠基、Robert Capa 战地、Sebastião Salgado 社会纪实、Steve McCurry、Vivian Maier、Daido Moriyama、森山大道/荒木经惟 日本派、Vivian Maier); 人像/时尚派 (Richard Avedon、Annie Leibovitz、Peter Lindbergh、Platon、Joey L、Lindsay Adler 时尚布光); 布光/教育派 (Joe McNally、Zack Arias 'one light'、Profoto/strobist David Hobby 'Strobist' 闪光灯启蒙博客、Karl Taylor 商业布光教学); 商业/广告派 (Chase Jarvis 'CreativeLive'创办+'best camera'、Joel Grimes、Tim Wallace 汽车); 后期/修图派 (Pratik Naik、Aaron Nace 'Phlearn'); 街头当代派 (Eric Kim 街头博客、Joel Meyerowitz、Saul Leiter 色彩街头); 中文圈 (李涛 'photoshop教学'、宁思潇潇 器材评测、thomas 'Thomas看看世界' 风光后期、阿刘 风光、668 人像); 哲学/批评 (Susan Sontag、Roland Barthes、John Szarkowski MoMA); (f) 行业话术/黑话 — 曝光三角 exposure triangle / 光圈 aperture f-stop / 快门 shutter speed / 感光度 ISO / 景深 DoF depth of field / 焦段 focal length / 等效 / 全画幅 full-frame vs 残幅 crop / 大三元 / 定焦 prime / 互易律 reciprocity / 向右曝光 ETTR / 直方图 histogram / 高光溢出 clipping / 动态范围 dynamic range / 宽容度 / 白平衡 WB

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。

GitHub 解封 — 账号封禁/限制/恢复的领域操作系统。覆盖：(a) GitHub 官方政策体系 (TOS / 可接受使用政策 / Trust & Safety 执行模式 / 公开申诉路径 / 受制裁地区解读)；(b) 封号原因诊断学 (spam / abuse / 制裁误标 / 2FA 丢失 / ToS 违规 / 账号劫持误判 / ban evasion 等子类型 + 症状→原因映射 + 自我分诊压测)；(c) 申诉实操 craft (写一封能让 T&S 快速判误伤的英文申诉信 + 升级阶梯 + 法务介入边界 + Don'ts)；(d) 制裁与合规硬边界 (OFAC SDN 名单 / 哪些国家或地区真不可达 / VPN 误判路径 / 中国大陆不是美国制裁地区的反复重申)；(e) 中国大陆开发者特别处境 (sanctioned region 误标 + SMS 验证不支持 +86/+852 + Gitee/GitLab/Codeberg/JihuLab 备用迁移 + 私有自托管 Gitea/Forgejo)；(f) 真实案例库 (~30+ 公开案例分类拆解：误伤 / 真违规 / 制裁误标三类各自赔率与申诉成功路径)。伦理锚绝不软化：不教 ban evasion / 不教撒谎式申诉 / 真违规承认改进 / 丢 2FA 无恢复因子则诚实告知 / 中国制裁误标靠申诉不靠换 VPN / 申诉 6 个月窗口必算清 / 约 3% (业内估) 真违规恢复 vs 约 79% (业内估) 误伤申诉成功率分层标。 (GitHub Account Reinstatement — the domain operating system for GitHub (and analogous platform) account suspension, restriction, appeal, and recovery. Covers: (a) GitHub official policy architecture (TOS / Acceptable Use Policies / Trust & Safety enforcement patterns / public appeals pathways / sanctioned-region interpretation); (b) suspension-cause diagnostics (spam / abuse / sanctions mis-flag / 2FA loss / ToS violation / account hijack false-positive / ban evasion taxonomy + symptom→cause mapping + self-triage pressure test); (c) appeal craft (writing an English appeal letter that gets T&S to fast-track a false-positive ruling + escalation ladder + legal-engagement boundary + Don'ts); (d) sanctions and compliance hard boundaries (OFAC SDN list / which countries/regions are truly unreachable / VPN false-positive paths / China is NOT a US-sanctioned region — repeated emphasis); (e) mainland China developer special circumstances (sanctioned-region mis-flag + SMS verification does not support +86/+852 + Gitee/GitLab/Codeberg/JihuLab fallback migration + self-hosted Gitea/Forgejo); (f) real-case library (~30+ public cases with categorized analysis: false-positive / genuine violation / sanctions mis-flag, each with win-rate and appeal success path). Ethical anchors absolutely not softened: do NOT teach ban evasion (creating new accounts turns a temp ban into a permanent one — the single biggest mistake); do NOT teach social engineering or lying in appeals (misuse + actually confirms violation); genuine violations: admit + improve beats denial; lost 2FA + no recovery factor → do not pretend recovery is possible; China sanctioned-region mis-flag → fix via appeal, not repeated VPN switching; appeal form 6-month window must be counted carefully; historical 约 3% (业内估) genuine-violation recovery vs 约 79% (业内估) false-positive appeal success rate stratification must be honestly labeled. Anti-samples (explicitly describe harm, never endorse): developer permabanned for ban evasion / open-source team org-banned for personal attacks in public issues / cases where repeated VPN switching escalated risk-control severity.) Master OS — automated mastery of GitHub Account Reinstatement — the domain operating system for GitHub (and analogous platform) account suspension, restriction, appeal, and recovery. Covers: (a) GitHub official policy architecture (TOS / Acceptable Use Policies / Trust & Safety enforcement patterns / public appeals pathways / sanctioned-region interpretation); (b) suspension-cause diagnostics (spam / abuse / sanctions mis-flag / 2FA loss / ToS violation / account hijack false-positive / ban evasion taxonomy + symptom→cause mapping + self-triage pressure test); (c) appeal craft (writing an English appeal letter that gets T&S to fast-track a false-positive ruling + escalation ladder + legal-engagement boundary + Don'ts); (d) sanctions and compliance hard boundaries (OFAC SDN list / which countries/regions are truly unreachable / VPN false-positive paths / China is NOT a US-sanctioned region — repeated emphasis); (e) mainland China developer special circumstances (sanctioned-region mis-flag + SMS verification does not support +86/+852 + Gitee/GitLab/Codeberg/JihuLab fallback migration + self-hosted Gitea/Forgejo); (f) real-case library (~30+ public cases with categorized analysis: fa

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。

Use when a task involves continuing a project, restoring project context, maintaining file-based project memory, updating current-state summaries, or recording meaningful progress across sessions. Works best for long-horizon, file-based projects and supports research writing, product document collaboration, software project coordination, and broader cross-functional project continuity.

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。

Draws images, PDF/SVG vector files and animation frames with pycairo, and supplies what cairo lacks: shaped text for Arabic, Indic and other scripts, text along curves, blur/glow/drop shadows, path booleans, stroke-to-outline, and Gouraud-shaded 3D. Use when writing pycairo or cairo drawing code, generative art, custom diagrams or posters rendered in Python, or when cairo output needs non-Latin text, soft effects, merged shapes or shaded surfaces.

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。

Bing Webmaster Tools + IndexNow extension. Microsoft Copilot citations are fed by the Bing index; this skill makes Bing visibility, link data, and IndexNow URL submission first-class.

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。

Optimize resumes for software engineering, PM, and technical roles

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。

When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth ideas,' 'how to market,' 'marketing strategies,' 'marketing tactics,' 'ways to promote,' 'ideas to grow,' 'what else can I try,' 'I don't know how to market this,' 'brainstorm marketing,' or 'what marketing should I do.' Use this as a starting point whenever someone is stuck or looking for inspiration on how to grow. For specific channel execution, see the relevant skill (ads, social, emails, etc.).

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。

Generate cross-platform install scripts for any software or library — a standalone install.sh runnable via a curl/wget one-liner with OS, arch, and package manager detection. Don't use for Dockerfiles, CI/CD pipelines, or one-off shell scripts.

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 product philosophy and leadership frameworks of Steve Jobs, co-founder and CEO of Apple Inc. Reach for this skill whenever the user is navigating product design, customer experience, innovation, hiring top-tier talent, or defining corporate culture. Trigger this skill for questions about finding product-market fit, making career trade-offs, building hardware/software ecosystems, or establishing brand values. Use this to push back against feature creep, committee-driven decisions, and technology-first thinking. Apply Jobs's mental models to help users simplify complex systems, focus on craftsmanship, and build products that sit at the intersection of technology and the liberal arts.

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Applies the reasoning, principles, and mental models of Sebastian Thrun (robotics and self-driving cars pioneer, founder of Google X, Waymo, Udacity, Stanford University). Reach for this skill whenever Claude is asked to advise on hardware/software systems engineering, autonomous vehicles, moonshot ideation, probabilistic robotics (SLAM), or leading high-stakes engineering teams. Trigger this skill for discussions on democratizing education, regulating AI, transitioning from academic research to product development, or managing technical teams with empathy. Use it to shift focus from incremental component debates to end-to-end execution and audacious goals.

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Applies the reasoning style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.

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Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Suede-owned launch packaging and install verification. Use when finished software work needs a clean public package (README, docs page, install command, release note, GitHub Pages update, skill-pack release, MCP server, or launch/social copy) or when a release needs handoff notes, an install fails, a public user cannot add a skill, a raw-vs-blob URL returns HTML, or `@personal` or a local plugin alias leaks into public docs. Verifies the live URL and runs the exact install command from a clean temporary directory before anything is called live. NOT FOR: product, course, or artist campaign launches (use suede-campaign-in-a-box or suede-marketing-plan); writing announcement copy from scratch (use suede-copy); MCP tool-catalog QA (use suede-mcp-qa); landing-page conversion work (use suede-site-alchemy).

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Suede-owned artist campaign rollout. Use when an artist, song, release, era, show, or catalog moment needs a complete campaign across surfaces: hooks, rituals, visuals, merch, rollout calendar, email, site copy, fan actions, setlists, and collaborator outreach. NOT FOR: single-surface conversion copy (use suede-copy); sync licensing packaging (use suede-sync-packaging); software, repo, or skill-pack releases (use suede-launch-packaging).

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 for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

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Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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High-end visual design like a premium agency. Defines exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks common defaults that make AI designs look cheap or generic. Based on taste-skill by Leon Lin & blueemi, improved by shokunin.

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。

Apply senior software engineering standards when writing, editing, or refactoring any code. Use this skill whenever the user asks to write a function, build a feature, fix a bug, design a module, or improve existing code — even if they don't mention quality explicitly. The goal is production-grade code: readable, correct, maintainable, secure, and failure-aware. Always active for any coding task, at any scale.

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Encode Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great. From the creator of Sonner (13M+ weekly npm downloads), Vaul, animations.dev, and Linear's web team. Use when user wants to polish UI, audit animations, review component interactions, add micro-feedback, or elevate motion quality.

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 this skill on every programming task — writing code, fixing bugs, refactoring, explaining code, running tools, or answering technical questions. Apply token-saving and quality-preserving practices throughout. Activate even when the user doesn't explicitly ask for efficiency — this skill is always relevant for software development work. Do not skip this skill for "simple" tasks; the rules apply at every scale.

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。

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

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。