조사, 논문, 교육, 자격증 공부 (4.5천건)
Use when the user wants an end-to-end AI4S research pipeline — broad direction or specific topic in, full research package out (exploration + literature survey + experiment + paper). Meta-skill that chains the four downstream skills in order. Pure markdown, no Python runtime.
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Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.
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Investigate questions needing multi-source synthesis: deep dives, comparisons, or find-all lists. Prefer focused workflows for enrichment, prospecting, or docs; not quick lookups or shopping.
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Find published organizational emails, phone numbers, and official contact forms for named businesses or local services. Use for sourced business contacts; exclude employee direct details.
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Research fantasy start/sit, waiver, trade, draft, or keeper decisions using current player news, usage, matchups, and league scoring. Use for roster decisions; do not change rosters or place bets.
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Find scientific papers, follow citations, and verify claims against paper passages. Use for literature questions, related work, citing papers, references, or questions about a named paper.
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用 Codex CLI 子任务完成多来源深度调研并综合成带出处的报告。用于确实需要分头取证的研究，不用于单次查证或已给定材料的总结。
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Rust の性能改善を「計測 → 仮説 → 変更 → 再計測」のループで地道に回すためのスキル。プロファイラの選び方、Cargo の最適化設定、データ構造・アロケーション・ハッシュ・分岐などの定石をフェーズ順に適用する。1 回 1 変更 1 計測の規律を厳守する。rsvelte 固有の既知ボトルネック（`serde_json::Value` 駆逐、`bumpalo` 導入、`Atom<'a>`、codegen 直書き）と OXC 対応表、NAPI 検証手順も §7 に収録。「Rust の性能改善」「ボトルネック調査」「プロファイル取って最適化」などの依頼で使用。
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Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.
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Use when managing Paperclip AI agent companies - creating tasks, managing agents, approving hires, running heartbeats, or any Paperclip control-plane operations via CLI or REST API. Triggers on "paperclip", "задача агенту", "одобри найм", "heartbeat", "запусти агента".
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Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.
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Use when a user wants a TikZ figure for a paper — an icon, an editable architecture/pipeline/flow template, or an example — and wants you to find, edit, and verify it. The single repo-wide skill for working with the OpenTikZ library.
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Conduct in-depth, multi-step research on a given topic by decomposing queries, finding diverse sources, cross-referencing findings, and synthesizing a comprehensive report. Use when the user requests deep research or provides relevant inputs for this workflow.
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Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.
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Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.
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Conduct a structured literature review on a given topic by defining a search strategy, applying inclusion and exclusion criteria, extracting key findings, and synthesizing results into a coherent academic review. Use when the user requests literature review or provides relevant inputs for this workflow.
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Summarize text using extractive, abstractive, hierarchical, and multi-document techniques, producing concise outputs at configurable detail levels. Use when the user requests summarization or provides relevant inputs for this workflow.
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中文战略聚焦教练。用于用户需要在多个目标、项目或学习方向之间做取舍，判断当前主线、最大约束、阶段目标、是否继续/暂停/转向，或基于学习进度进行周度、阶段复盘时。学习场景中只负责回答“为什么学、学到什么程度、当前优先什么”，并把明确使命交给 learning-coach。不要用于日常打卡和进度更新、具体知识讲解、源码带读、连续答疑、调试、练习设计或“开始下一课”。
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中文高级私人学习教练。用户说“开始学习 X”“继续/下一课”“学完了”“记录进度/打卡”“我学到哪了/还要多久”，正在通过真实项目、源码、作品、题目或任务学习编程、Agent、写作、语言、考试、研究或职业技能，或对具体概念和材料连续追问时使用。触发后主动读取学习现场、学习记录、进度、用户背景和真实材料，替用户选择最近发展区与入口，一次只给一个动作或问题，并用解释、实践、反馈和迁移证据推进掌握。不要让用户先设计课程、选择材料或填写长问卷；只有方向取舍本身尚未解决且会造成明显浪费时，才交给 focus-coach。
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Think and work like an expert Machine Learning Engineer. Use when a task calls for Machine Learning Engineer judgment. Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes.
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Think and work like an expert Machine Learning Researcher. Use when a task calls for Machine Learning Researcher judgment. Reasons from population risk, double descent, and inductive bias; enforces sacred test sets, hierarchical ablations, nested CV, and HELM/Dynabench-aware benchmarking; reports with NeurIPS and Pineau reproducibility checklists while treating leakage, meta-overfitting, benchmark contamination, Goodhart gaming, and seed variance as first-class failure modes.
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Think and work like an expert Human–Computer Interaction Researcher. Use when a task calls for Human–Computer Interaction Researcher judgment. Reasons from situated context, Fitts/GOMS/KLM, and CHI contribution types; runs contextual inquiry through LMM/CLMM analysis with SUS/NASA-TLX triangulation; uses Prolific/OSF and treats demand characteristics, novelty effects, ordinal misuse, and WEIRD samples as first-class failure modes.
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Think and work like an expert Global Health Researcher. Use when a task calls for Global Health Researcher judgment. Reasons from burden, equity, and health-system building blocks through DHS/MICS/DHIS2/GBD triangulation, cluster and stepped-wedge designs, RE-AIM/CFIR implementation science, and CIOMS-fair partnership while treating survey weights, HMIS completeness, and GBD smoothing as first-class failure modes.
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Think and work like an expert Emergency Medicine Researcher. Use when a task calls for Emergency Medicine Researcher judgment. ED trial design expert for pragmatic and cluster RCTs, time-zero and immortal-time bias in acute cohorts, NIHSS/SOFA/qSOFA and ESI/CTAS triage, NEDS/NHAMCS registries, and CONSORT/SPIRIT reporting with Hawthorne and selection-bias failure modes.
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