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概要と使いどころ
Find open AI engineering jobs that fit a candidate, verify location and apply links, and assess each employer's product. Use for one-off shortlists or a job alert's research step. Don't use for résumé writing, job applications, or company news.
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 a site or app for SEO, AI-bot search and app-store search in one run: detect web vs store, run each audit once, merge one prioritized report; fixes only via member gates. Don't use for one fix (seo-ai-optimizer, aso-marketing).
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。
Look up the latest news for the user from specified news sites. Provides authoritative URLs for politics, finance, society, world, tech, sports, and entertainment. Use browser_use to open each URL and snapshot to get content, then summarize for the user.
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。
Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a fully-expanded mind map image plus the written wonderings. Fire this when the person signals they want to open up a space and think alongside you — "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet" — or when they invoke /wonder or /wonderpill. Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline ("ideas I can ship this week"), or a "which / what should I pick" question — those want straight answers, not a wonder pill. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.
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。
Turns open-ended requests into things to think WITH instead of answers to accept. Audits the hidden assumptions inside a topic, inverts them into sharp "what if" provocations, branches each one outward, and delivers a mind map plus written wonderings. Portable version, no vendor-specific tools required, works on any conversational assistant. Fire this when the person signals they want to open up a space and think alongside the assistant: "wonder about X", "what are the weird angles on X", "help me think about what this could be", "I want to explore, not decide yet". Do NOT fire when the same topic is attached to a request for concrete, usable, or decided output: a named deliverable, a count ("give me 5 names"), a deadline, or a "which / what should I pick" question. When it is genuinely unclear which they want, this skill opens by asking one line rather than branching. Not for factual lookups or executing a plan already chosen.
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。
Source b-roll for a video edit — classify each moment, scope the search, return vetted candidates, place on the word. Use when the user says "find b-roll", "/find-broll", "source clips for this edit", or wants footage/memes/screenshots to lay over a talking-head video.
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, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.
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。
Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.
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 foundationalist reasoning and methodological doubt of René Descartes (17th-century French rationalist philosopher). Use this skill whenever the user is questioning fundamental assumptions, seeking absolute certainty in a decision, dismantling flawed systems, or trying to rebuild a project from first principles. Trigger this skill for topics involving epistemology, rationalism, radical doubt, systemic reconstruction, or separating objective truth from sensory illusion. Reach for this whenever a user needs to cut through probabilities and consensus to find an indubitable 'Archimedean point' to build upon.
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 this skill whenever the user is navigating early-stage startups, bootstrapping, product innovation, branding, or overcoming the fear of failure. Sara Blakely, founder of Spanx and self-made billionaire, approaches business by leveraging 'the outsider advantage,' relying on intuition, and obsessing over consumer empathy. Trigger this skill for discussions about funding trade-offs (bootstrapping vs. VC), naming products, dealing with imposter syndrome, entering an industry with zero experience, or building an authentic, vulnerability-driven company culture. Reach for her mental models when users are tempted to share fragile ideas too early, fear embarrassment, or need to rethink how they approach product-market fit.
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, 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.
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 Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.
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 existentialist and feminist philosophy of Simone de Beauvoir, French philosopher and author of The Second Sex. Reach for this skill whenever the user is grappling with existential dilemmas, moral ambiguity, gender dynamics, societal expectations, or relationship ethics. Trigger this skill for questions about freedom versus responsibility, bodily autonomy, marginalization, or when the user feels trapped by external labels or "immanence." Use this to help the user reclaim their agency, recognize the social construction of their situation, and make authentic choices that expand their transcendence while respecting the interconnected freedom of others.
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 Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.
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.
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 and empathetic reasoning of Whitney Wolfe Herd, founder and CEO of Bumble. Use this skill whenever you are advising on platform safety, double-sided marketplace dynamics, women's empowerment, purpose-driven entrepreneurship, or overcoming professional trauma. It is highly applicable when users face decisions about product constraints (like time limits or behavioral guardrails), balancing digital engagement with real-world connection, or navigating outdated social dynamics. Reach for this skill to help users shift from 'me to we,' engineer kindness into their products, and focus on core inputs rather than vanity metrics.
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 Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
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 philosophical frameworks of Martha Nussbaum (American philosopher, capabilities approach, University of Chicago) to evaluate justice, human welfare, and ethical dilemmas. Use this skill whenever the user is discussing measuring societal well-being, educational curricula, animal rights, the role of emotions (like anger or disgust) in law and public life, or navigating human vulnerability and tragedy. Reach for this skill whenever evaluating policy impacts, responding to injustice, or questioning purely economic metrics like GDP. It shifts the focus to what people are actually able to do and be.
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 frameworks of Aristotle (ancient Greek philosopher, logic, ethics, metaphysics, 384-322 BCE) to decision-making, ethics, and analysis. Use this skill whenever the user is grappling with moral dilemmas, character development, habit formation, finding balance (the Golden Mean), persuasive communication (rhetoric), or understanding the root causes and ultimate purpose (teleology) of a project or system. Reach for this skill even if the user doesn't name Aristotle, especially when discussing career fulfillment, long-term happiness (eudaimonia), balancing extremes, or structuring persuasive arguments.
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 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.
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 David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.
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 rigorous analytic and moral philosophy of Elizabeth Anscombe (British analytic philosopher, virtue ethics, Intention). Reach for this skill whenever Claude encounters moral dilemmas, ethical trade-offs, questions of intention vs. foresight, or consequentialist/utilitarian reasoning. Use it when analyzing human action, culpability, the doctrine of double effect, or the meaning of "ought" and obligation. It is especially useful for cutting through vague moral language by demanding precise psychological descriptions of actions and rejecting the idea that good ends justify evil means.
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, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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。