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概要と使いどころ
Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
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Draft Project Manual specification requirements for EPD submittals and sourced GWP limits. Use for embodied-carbon requirements in project sections; use the EPD parsing, research or comparison skills for product evidence.
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。
Find and compare current FF&E candidates from a design brief, with sourced purchasing facts and optional CSV-library save. Use to research or source products; not to match from an image or pair coordinating items.
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Search for EPDs by product category, CSI division, or material type — finds EPDs from EC3, program operator registries, and manufacturer sites. Use when the user asks to "find EPDs for...", locate declarations for a material, or source low-carbon product documentation.
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。
Guided, hands-on course teaching architects how to use Codex or Claude Code — six short modules, each built around an exercise on a bundled sandbox project (a fictional Brooklyn art museum expansion). Resumable across sessions via PROGRESS.md. Use when the user runs $learn or /as:learn, says they're new to AI-assisted project work, or asks how to learn it.
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。
Research population, income, age, housing, and employment around a site. Use for census, demographic, or local-market questions tied to a location.
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。
Enrich FF&E schedule rows with categories, colors, materials, and style tags. Use to tag, categorize, or fill those missing fields; not to research new products.
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。
Research climate, sun, flood, seismic, soil, contamination, and topography for a site. Use for environmental site analysis or hazard questions tied to a location.
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。
Learn daily from this machine's Claude Code, Codex, Hermes, OpenClaw and Cursor sessions. Review skill changes and lessons with accept/reject/tbd; recall lessons at session start.
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 the least code that works, and cut what an AI agent over-built. Use on any coding task: write, fix, refactor, or review a diff. Also when the user says 'less', 'simplify', 'too complex', 'over-engineered', or 'what can we delete'. Not for prose, research, or non-code questions.
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。
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.
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。
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
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Scientific manuscript writing: IMRAD, citation styles (APA/AMA/Vancouver/IEEE), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA/ARRIVE), writing principles (clarity/conciseness/accuracy), venue-specific style. For LaTeX see companion assets.
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Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.
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。
Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.
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。
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
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。
Conducting systematic, scoping, and narrative literature reviews. Covers PRISMA/PRISMA-ScR protocols, search strategy (Boolean, MeSH), database selection (PubMed, Scopus, Web of Science, Embase), screening, data extraction, evidence synthesis (narrative, meta-analysis, thematic), and reporting. Use when planning or executing a formal literature review.
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。
Research posters in LaTeX using beamerposter, tikzposter, or baposter. Layout, typography, color schemes, figure integration, accessibility, and QA for conferences. Includes templates. For figure generation use matplotlib-scientific-plotting or plotly-interactive-plots.
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Guidelines for clinical decision support (CDS) documents: biomarker-stratified cohort analyses and GRADE-graded treatment reports. Covers structure, executive summaries, evidence grading (1A–2C), stats (HR, CI, survival), and biomarker integration. Use for pharma research docs, clinical guidelines, regulatory submissions.
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Selecting a reference manager and applying citation styles. Compares Zotero, Mendeley, EndNote, Paperpile; covers APA/Vancouver/ACS/Nature styles, DOI management, citation tracking, and Word/Google Docs/LaTeX integration. Use when setting up a reference workflow or fixing citation formatting.
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。
Cancer Research (AACR) figures: resolution (300-1200 DPI), formats (EPS/TIFF/AI), hierarchical panel labels (Ai, Aii, Bi), figure/table limits, legend requirements with replicate counts.
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。
UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.
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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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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.
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。