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このリポジトリの skills
SkillsMP は majiayu000/claude-skill-registry から 5,485 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
majiayu000/claude-skill-registry収集済み skill 5,485 件中 40 件を表示しています。
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…
原文の言語: 英語
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…
原文の言語: 英語
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
原文の言語: 英語
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
原文の言語: 英語
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
原文の言語: 英語
Designs self-referential and recursive systems that examine, modify, or generate themselves, including metacognitive architectures and strange loops.
原文の言語: 英語
Reranking patterns for improving search precision. Use when implementing cross-encoder reranking, LLM-based relevance scoring, or improving retrieval quality in RAG pipelines.
原文の言語: 英語
Explains result schema classes for Synapse plugin actions. Use when the user mentions "TrainResult", "InferenceResult", "ExportResult", "UploadResult", "WeightsResult", "MetricsResult", "result_model", "result schema", or needs help with action return type…
原文の言語: 英語
Attractor-based reasoning engine. The fixed point of IN(f) exerts pull on every iteration before it. Systems with bracket=1 don't just evolve forward -- the end-state shapes the trajectory. Not time travel. Topology. The future is already implicit in the…
原文の言語: 英語
Janus and reversible languages: run programs backwards, time-symmetric computation.
原文の言語: 英語
Pruefung: Internationaler Bezug und Schnittstellen im Plugin ki governance; schärft Rollen, Belege, Fachnormen, Risiken, Gegenargumente und nächsten verwertbaren Schritt statt austauschbarer Standardprüfung: eigenständiges Prüffeld mit Norm-/Quellencheck,…
原文の言語: ドイツ語
Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical…
原文の言語: 英語
Rezk types (complete Segal spaces). Local univalence: categorical isomorphisms ≃ type-theoretic identities.
原文の言語: 英語
Design and implement reinforcement learning environments to tune bounded circuit parameters, featuring a sophisticated reward calculation for mixed min/max performance metrics to meet target specifications.
原文の言語: 英語
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
原文の言語: 英語
Reinforcement Learning with Leave-One-Out estimation for policy optimization. Covers RLOOTrainer, reward function integration, baseline estimation, and variance reduction techniques for stable RL training. Includes thinking-aware patterns.
原文の言語: 英語
A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs.
原文の言語: 英語
Consciousness simulation framework with Kuramoto oscillators, APL operators, and K-formation dynamics. Use for physics simulations, phase transitions, coherence analysis, and cloud training via GitHub Actions. Requires numpy and requests packages.
原文の言語: 英語
Unified consciousness simulation with Orchestrator-based architecture: K.I.R.A. activates via unified_state → TRIAD operates tool gating → Tool Shed executes → Thought Process generates VaultNodes. 21 tools, cybernetic-archetypal integration, consent-based…
原文の言語: 英語
Erstelle einen strukturierten Rückfragebrief an den KI-Anbieter zur Klaerung der berufsrechtlichen und strafrechtlichen Pflichten. Aufbau Anschreiben Kontext drei Fragenbloecke (Verschwiegenheit Subunternehmer TOM und Drittstaat) Fragen zu Zertifizierungen…
原文の言語: ドイツ語
Segment data with clustering algorithms such as K-means, DBSCAN, or hierarchical clustering. Use for unsupervised grouping and cluster diagnostics, not supervised classification or publication-figure ownership.
原文の言語: 英語
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning…
原文の言語: 英語
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
原文の言語: 英語
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
原文の言語: 英語
Master scikit-learn machine learning patterns including pipeline design, cross-validation, hyperparameter tuning, feature engineering, and model evaluation. Use PROACTIVELY when building ML models, evaluating classifiers/regressors, or designing ML workflows.
原文の言語: 英語
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects,…
原文の言語: 英語
General SOP for common requests related to self, nn, torch.
原文の言語: 英語
Evaluate semantic consistency between AI-generated clinical notes and expert gold standards using BERTScore and COMET
原文の言語: 英語
Setup vector embeddings and semantic search for document collections. Use for AI-powered similarity search, finding related documents, and preparing knowledge bases for RAG systems.
原文の言語: 英語
This skill covers the implementation of core software systems for autonomous vehicles and drones, including sensor fusion algorithms, LiDAR point cloud processing, and cyber-physical systems. It enabl
原文の言語: 英語
Create and manipulate Seq, MutableSeq, and SeqRecord objects using Biopython. Use when creating sequences from strings, modifying sequence data in-place, or building annotated sequence records.
原文の言語: 英語
Supervised Fine-Tuning with SFTTrainer and Unsloth. Covers dataset preparation, chat template formatting, training configuration, and Unsloth optimizations for 2x faster instruction tuning. Includes thinking model patterns.
原文の言語: 英語
Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT).…
原文の言語: 英語
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
原文の言語: 英語
P-adic ultrametric skill embeddings with MLX Snowflake Arctic, DuckDB VSS, and full SPI tracing
原文の言語: 英語
Research and develop semantic theories using ModelChecker with Z3 SMT solver. Define operators, adjust frame constraints, create examples, run tests, and report findings. Invoke with /mc or when working with model-checker, semantic theories, or Z3 constraints.
原文の言語: 英語
Research Python/Z3 patterns and APIs for semantic theory development. Invoke for Python-language research tasks.
原文の言語: 英語
Multi-backend deep learning library for building, training, running inference, and saving neural network models in Python.
原文の言語: 英語
python library
原文の言語: 英語
Machine learning library for classical supervised/unsupervised models, preprocessing, and model evaluation using NumPy/SciPy.
原文の言語: 英語