414 件 ・ 関連度順
概要と使いどころ
Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.
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。
Structure a dev-tool homepage that converts developers into champions. Use when the landing page is written for the buyer instead of the developer, reads as salesy, buries what the product does, or makes it hard to start. Pairs with the ShipReady homepage audit.
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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, 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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This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.
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 of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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 style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, 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。
Think and work like an expert Microbiome Scientist. Use when a task calls for Microbiome Scientist judgment. Reasons from compositional and longitudinal stats (MaAsLin2, ANCOM-BC2), STORMS pre-analytics, FMT/LBP and diet trials, and multi-omics integration; treats PPI/antibiotic confounders, kitome contamination, host-DNA swamping, and HMA causality overclaim as first-class failure modes.
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。
Think and work like an expert Electrical Engineer. Use when a task calls for Electrical Engineer judgment. Reasons from Kirchhoff's laws, Maxwell's quasi-static limit, energy conservation, and LTI superposition through SPICE loop-gain and corner analysis, Bode gain/phase-margin checks, impedance-controlled layout, and IEC 62368/CISPR compliance, while treating unmodeled PCB parasitics, ground-return loops, protection let-through energy versus semiconductor SOA, and probe-artifact confounders as first-class failure modes.
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Provider-independent production workflow for recutting long talking-head, podcast, interview, webinar, panel, lecture, livestream, founder call, or customer-call footage into short clips, highlight reels, trailers, audiograms, captioned vertical videos, and social cutdowns. Use when an agent must ingest source recordings, transcribe and diarize speakers, select quotes without misrepresentation, preserve context, handle consent/rights/disclosures, clean jump cuts and audio, add captions, graphics, lower thirds, b-roll, platform variants, synthetic-media boundaries, review packets, delivery specs, and recut QA.
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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
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Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
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Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.
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。
Core marketing principles for startups. Covers positioning, messaging, channels, and growth. Focus on what works for resource-constrained teams. Battle scars included: I've watched startups burn $1M on Instagram ads because "that's where competitors are", seen "growth hacks" destroy brand trust overnight, and advised founders who scaled marketing before finding product-market fit. Contrarian take: Most startup marketing advice is backwards. The best marketing doesn't feel like marketing. Distribution comes before product features. And your first 100 customers should never come from paid ads. Use when "marketing strategy, positioning, messaging, customer acquisition, growth channels, brand, go-to-market, " mentioned.
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。
Your first 10 hires determine your company culture. Your first 50 determine your execution capacity. Hiring is the most leveraged activity a founder does, and the most common source of expensive mistakes. This skill covers when to hire, who to hire first, how to sequence roles, compensation and equity, interviewing and closing, and the many ways hiring goes wrong at startups. Use when "hiring, first hire, when to hire, who to hire, compensation, equity, salary, recruiting, interview process, offer negotiation, team building, engineer hire, sales hire, founding team, hiring, team, compensation, equity, recruiting, interviews, offers, startups, founding-team" mentioned.
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Most startup ideas are bad. Not because founders lack creativity, but because they're solving invented problems instead of lived ones. Paul Graham's essays reveal a pattern: the best ideas come from living in the future and noticing what's missing. Schlep blindness hides gold. Organic ideas beat brainstormed ones. Frighteningly ambitious ideas are often easier. This skill synthesizes PG's idea-finding wisdom into actionable guidance. Use when "keywords, contexts, " mentioned.
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Causal inference specialist for causal discovery, counterfactual reasoning, and effect estimationUse when "causal inference, causal discovery, counterfactual, intervention effect, confounder, structural causal model, SCM, dowhy, causal graph, causal, dowhy, scm, dag, counterfactual, intervention, causalnex, confounding, ml-memory" mentioned.
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。
Pieter Levels (@levelsio) 视角. 海外 indie hacker / solo builder 极致代表 — Nomad List + Photo AI + Interior AI + Remote OK 多产品 portfolio, 单人 $250K+/月 MRR, 零员工. 把"用 AI agent 赚钱"从"融资 + 招人 + scale 到 unicorn"的 VC 操作系统, 翻成"audience first + ship daily + stay solo + $10K MRR = 自由"的 indie 操作系统. 用途: 当用户问"我应该融资吗" / "AI 产品怎么 0→$1M ARR" / "1 个人能做多大" / "build in public 还有用吗" / "我应该招人吗" 时, 用 Pieter 视角先反问"你要的到底是 freedom 还是 valuation" — 如果是前者, 那 path 跟 VC 派完全相反. 当用户提到 "Pieter Levels" / "@levelsio" / "indie hacker" / "solo founder" / "Nomad List" / "Photo AI" / "build in public" / "ship daily" / "$10K MRR" 时使用. 即使用户只是说 "我想做个 SaaS 副业" / "怎么 bootstrap" 也应触发.
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。
Write personalized cold outreach emails to hiring managers and founders — specific, human, not a pitch deck
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 strategic thinking of Walt Disney, co-founder of The Walt Disney Company and animation pioneer. Use this skill whenever you are advising users on creative control, intellectual property, brand building, storytelling, or balancing quality with profit. It is highly effective for situations involving product diversification, media synergy, adapting classic works for mainstream audiences, or designing ambitious, future-proof prototypes (like urban planning or platform ecosystems). Trigger this skill when users face pressure to repeat past successes (sequels), deal with middlemen, or need to elevate a team's creative output through constructive feedback.
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Applies the practical, systems-level, and commercially-driven reasoning of Thomas Edison (founder of General Electric, inventor and industrialist). Use this skill whenever the user is navigating product commercialization, systems engineering, R&D strategy, scaling new technologies, evaluating patent decisions, or struggling with repeated iterative failures. Edison's approach shifts the focus from isolated 'genius' inventions to building entire profitable ecosystems, treating failure as a necessary process of elimination, and prioritizing relentless execution over pure theory.
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 of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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 philosophical frameworks of Plato (ancient Greek philosopher, Theory of Forms, founder of the Academy) to structural, ethical, and epistemological problems. Use this skill whenever the user is grappling with organizational design, leadership ethics, defining abstract concepts, aligning incentives, or uncovering the root essence of a problem. Trigger this when discussing justice, governance, the nature of knowledge, structural harmony, or when a user is trapped by superficial metrics (shadows) and needs to find fundamental truths (Forms). Do not wait for the user to name Plato; proactively apply his dialectical reasoning to cut through illusions.
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。