615건 · 관련도순
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
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Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.
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Write structured reviews of scientific manuscripts and grant proposals. Assess methods, statistics, reproducibility, ethics, and reporting completeness, then organize strengths, major concerns, minor issues, and specific suggestions into constructive feedback.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and query Kusto graphs from natural language. Covers transient graphs (make-graph), persistent graph models/snapshots, pattern matching (graph-match), shortest paths, connected components, and graph-to-table export. Generates the edges-first thinking: define edges, define node lookups, union, make-graph. WHEN: make-graph, graph-match, graph-shortest-paths, graph-to-table, graph-mark-components, persistent graph, graph model, graph snapshot, build a graph from data, find paths between nodes, pattern matching in graph, connected components, transient graph, Kusto graph, KQL graph.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Enter OpenSpec explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements in a project that uses OpenSpec. Use when the user wants to think through something before or during an OpenSpec change. Also use when the user says "openspec explore" or "opsx explore".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Find 10x product opportunities and high-leverage improvements. Use when user wants strategic product thinking, mentions '10x', wants to find high-impact features, or says 'what would make this 10x better', 'product strategy', or 'what should we build next'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Formulate and audit real strategy using Richard Rumelt's "Good Strategy Bad Strategy": an honest diagnosis, a guiding policy, and coherent action instead of goals, vision, and wishful thinking. Use when the user mentions "good strategy bad strategy", "strategy kernel", "diagnosis guiding policy coherent action", "our strategy is just goals", "strategic planning", "mission vs strategy", "annual plan", or "is this actually a strategy". Also trigger when auditing a strategy doc or pitch deck for fluff, turning a goal list into real strategy, formulating strategy for a product or company, or finding leverage and proximate objectives. Covers the kernel of strategy, bad-strategy detection, and sources of power. For product positioning, see obviously-awesome. For uncontested markets, see blue-ocean-strategy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply lean thinking to UX: hypothesis-driven design, collaborative sketching, and rapid experiments instead of heavy deliverables. Use when the user mentions "Lean UX", "design hypothesis", "outcome over output", "design studio method", "assumption mapping", "lightweight research", "too much design documentation", or "get the team designing together". Also trigger when reducing design-documentation overhead, getting cross-functional teams to co-design, or running fast usability experiments. Covers hypothesis statements, MVPs for UX, and cross-functional collaboration. For Build-Measure-Learn, see lean-startup. For usability audits, see ux-heuristics.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creative problem-solving techniques for breaking through stuck points - includes collision-zone thinking, inversion, pattern recognition, and simplification
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
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Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?"
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Answers questions about Deep Code CLI itself — including features, configuration options, slash commands, Skills, MCP integration, permissions, notifications, session persistence, and troubleshooting. Use this when users ask how to configure or use Deep Code, how to set up an MCP server, configure notifications (such as Slack/Feishu), manage permissions, view available skills, understand slash commands, configure thinking mode, etc.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train and run models on Tinker (Thinking Machines) — LoRA and full fine-tuning, SFT with correct loss masking, RL/RFT with group-relative advantages and importance sampling, agentic RL over multi-turn tool-using agents, inference via the SamplingClient or the OpenAI-compatible endpoint, checkpoint management and export, every hyperparameter, and cost estimation from the live price table. Use this skill whenever the user mentions Tinker, tinker://, TINKER_API_KEY, console.tinker.ai, or the tinker CLI; wants to fine-tune, SFT, RFT, or RL a model on Tinker; asks about Tinker models, context windows, LoRA rank, sampling params, loss functions (cross_entropy, importance_sampling, ppo, cispo, dro), Datum construction, forward_backward or optim_step; wants to sample from, download, resume, merge, or export a Tinker checkpoint; or asks what a Tinker run will cost. Whenever Tinker work is initialized in a project, this skill also creates a TINKER_PRICING.md there so prices sit next to the training code. Reach for it even on vague asks like "train a model on this data" or "get inference working" when Tinker is the platform in play.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when evaluating markets through Howard Marks-style second-level thinking, credit cycles, risk premiums, market psychology, and defensive versus aggressive posture.
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Adversarial 5-7 question challenge to a deck's pedagogical choices — ordering, prerequisites, cognitive load, motivation. Use when user says "devil's advocate", "poke holes in this deck", "push back on my slides", "stress-test the design", "what would a skeptical student ask?". Read-only; surfaces questions to force rethinking. Lighter than `/pedagogy-review`.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and query Kusto graphs from natural language. Covers transient graphs (make-graph), persistent graph models/snapshots, pattern matching (graph-match), shortest paths, connected components, and graph-to-table export. Generates the edges-first thinking: define edges, define node lookups, union, make-graph. WHEN: make-graph, graph-match, graph-shortest-paths, graph-to-table, graph-mark-components, persistent graph, graph model, graph snapshot, build a graph from data, find paths between nodes, pattern matching in graph, connected components, transient graph, Kusto graph, KQL graph.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.