리서치·학습 스킬

Naturalistic decision-making research showing how experts make decisions through recognition-primed processes

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Compassionate bereavement support, memorial creation, grief education, and healing journey guidance. Specializes in understanding grief stages, creating meaningful tributes, and supporting the non-linear path of loss.

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Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.

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Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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Structured interview and observation techniques for extracting tacit knowledge from domain experts

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Detects fabricated content, false citations, and unverifiable claims in agent outputs. Uses source verification and consistency checking. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).

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Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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Learns from DAG execution history to improve future performance. Identifies successful patterns, detects anti-patterns, and provides recommendations. Activate on 'learn patterns', 'execution patterns', 'what worked', 'optimize based on history', 'pattern analysis'. NOT for failure analysis (use dag-failure-analyzer) or performance profiling (use dag-performance-profiler).

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Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

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Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

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Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields

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Create interactive AI role-play training activities as self-contained HTML. Use when the user asks to generate a role-play, persona practice, sales simulation, customer conversation drill, compliance scenario, or any learner-vs-AI-character training exercise.

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Systematic literature review methodology for social science research. Use when conducting systematic reviews, research synthesis, meta-analyses, scoping reviews, or comprehensive literature searches across economics, political science, sociology, education, public health, and development. Produces structured notebook cells with search strategy, evidence table, thematic synthesis, and gap analysis.

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Ask Claude, ChatGPT, and Gemini a question and capture the full trace behind the answer (the search queries each engine ran, the pages it retrieved, and the sources it cited, span by span). Use when asked to "trace an AI answer", "see what AI engines cite", "check AI citations", "which sources does ChatGPT/Claude/Gemini use", "why does the AI recommend X", or for any GEO (Generative Engine Optimization) measurement.

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Search and analyze Reddit discussions for market research, product feedback, and community insights using Xpoz. Use when asked to "search Reddit", "what does Reddit think about X", "Reddit feedback on X", "subreddit analysis", or "Reddit market research".

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Product Research / 产品研究编译器：当用户要围绕一个产品问题做系统调研、竞品证据、替代方案、行业信号、 用户与市场研究、最佳实践提炼、产品候选池、本地 HTML 研究看板、可视化研究报告或跨职能 Dashboard 时使用。 当用户只有大白话、模糊方向、业务愿望或 Roadmap/PRD 前置材料想法，需要先转成清晰研究目标、研究问题和输出要求时也使用。 适合把研究转成 Research Project、学习报告、证据矩阵、PM 决策看板、候选池、模板、实践任务、业务判断、商业化输入或高门槛应用研究前置。适合“系统研究一个主题” “整理到 Obsidian”“做深度专题”“研究行业最佳实践”“概念解读”“概念源流”“PM 技术评审提问脚本” “行业演进看板”“这个主题对我的产品决策有什么用”“让我系统学会并输出 learning report HTML”。不适合创建 Skill、评审 SKILL.md、普通即时搜索或一次性摘要。 当用户已经要在明确候选项中选一个、需要最终推荐和排除理由时，应使用 decision-research。 当 decision-research 或 grill-me 返回一个可研究、可关闭的精确证据 gap 时，也使用本 Skill 只补 Evidence Delta。

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决策调研 / Decision-Driven Research：当用户面对一个具体决策需要找信息时使用——「有没有现成方案」 「怎么接入 X 平台」「这个技术可行吗」「业界怎么做 Y」「选 A 还是 B」「桌面端应该怎么定位」 「高级版和基础版怎么拉开差异」「这个产品方向对不对」。 核心行为：先框定研究层级和问题类型，再锚定决策问题，枚举竞争假设，主动找反对证据， 用排除逻辑给出有立场的结论。支持技术选型、产品策略、商业判断、竞品定位等所有需要做决定的调研。 可用中文唤起：「帮我调研」「有没有现成方案」「这个怎么接」「技术上可行吗」「帮我选一个」 「这个产品方向对不对」「我们应该怎么定位」「行业怎么做」。 与 research-topic-compiler 的边界：需要最终选择、推荐、排除理由、置信度和颠覆条件时用这个； 如果目标是长期学习、候选池沉淀或 Research Project，用 research-topic-compiler，再把 Candidate Backlog 交回本 Skill 做最终决策。 只有发现一个会实质改变结论、可研究且可关闭的证据 gap 时，才向 research-topic-compiler 发出精确 Return Request。 不用于：问题还没定义清楚时（先用 ai-collaboration-calibration L4-fuzzy 脑暴）。

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