280 件 ・ 関連度順
概要と使いどころ
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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。
Create, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".
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。
Choose and apply automatic layout strategies for node-link diagrams and connected-node visuals. Use when the user asks how to auto-arrange nodes, reduce line crossings, route edges, avoid overlaps, stabilize layout, or choose graph-layout algorithms for network diagrams, dependency graphs, database schema diagrams, ERDs, state machines, decision trees, flow diagrams, box-and-line editors, or other line-connected nodes.
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。
Use when designing, implementing, debugging, or reviewing Nexent relational schemas, FastAPI endpoints, backend services, database access, SQL migrations, or backend/SDK configuration. Includes table/field naming, types, JSONB boundaries, audit fields, keys, and indexes even before code paths exist. Skip frontend-only work and unrelated SDK algorithms.
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。
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
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。
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
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。
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
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 covers structural econometric models. Use when the user is building, estimating, or debugging structural models — including BLP demand estimation, dynamic discrete choice, auction models, or any workflow involving moment conditions, nested fixed-point algorithms, or MPEC formulations. Triggers on "structural model", "moment conditions", "NFXP", "MPEC", "BLP", "random coefficients", "dynamic discrete choice", "CCP", "Rust model", "auction estimation", "GMM objective", "inner loop", "contraction mapping", or convergence/starting value problems in optimization-based estimation.
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。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
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。
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。
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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。
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
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。
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
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。
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
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。
Expert skill for G2 legend development - provides comprehensive knowledge about legend rendering implementation, component architecture, layout algorithms, and interaction handling. Use when implementing, customizing, or debugging legend functionality in G2 visualizations.
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。
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
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 should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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。
Domain routing and boundary guidance for authorized cryptography weakness testing, including weak algorithms, padding oracles, key management errors, insecure randomness, and hash collision risks. Use when a task belongs to the cryptography testing domain and needs scope, evidence, pivot, or exit criteria.
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。
Use this skill for hands-on DOCA Compress programming on a BlueField DPU, ConnectX NIC, or host with DOCA — enabling compress-deflate, decompress-deflate, decompress-lz4-stream, or decompress-lz4-block tasks on a doca_compress context (the hardware supports DEFLATE both directions plus LZ4 decompress; LZ4 encode is NOT supported), sizing source / destination doca_buf against the per-task cap query, setting mmap permissions, deciding offload vs CPU zlib / zstd, validating with a round-trip smoke, or debugging DOCA_ERROR_* from a Compress call. Trigger on phrasings like "offload this gzip", "decompress incoming network data", "compress task returns INVALID_VALUE on alloc_init", "submitted a task but no completion arrives", or "decompress LZ4 on the BlueField." Refuse and route elsewhere for non-DEFLATE / non-LZ4 algorithms (zstd / Snappy / brotli), LZ4 encode (route to a CPU LZ4 library), pure mmap-to-mmap copies (doca-dma), or DOCA Core lifecycle internals.
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。
In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.
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。
Design guide RNAs for CRISPR-Cas9/Cas12a experiments using CRISPRscan and local scoring algorithms. Score guides for on-target activity using Rule Set 2 and Azimuth models. Use when designing sgRNAs for gene knockout, activation, or repression experiments.
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。
Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.
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。
Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or identifying superspreaders.
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。