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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…
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
Designs self-referential and recursive systems that examine, modify, or generate themselves, including metacognitive architectures and strange loops.
لغة النص الأصلي: الإنجليزية
Reranking patterns for improving search precision. Use when implementing cross-encoder reranking, LLM-based relevance scoring, or improving retrieval quality in RAG pipelines.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
Janus and reversible languages: run programs backwards, time-symmetric computation.
لغة النص الأصلي: الإنجليزية
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,…
لغة النص الأصلي: الألمانية
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…
لغة النص الأصلي: الإنجليزية
Rezk types (complete Segal spaces). Local univalence: categorical isomorphisms ≃ type-theoretic identities.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الألمانية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
لغة النص الأصلي: الإنجليزية
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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,…
لغة النص الأصلي: الإنجليزية
General SOP for common requests related to self, nn, torch.
لغة النص الأصلي: الإنجليزية
Evaluate semantic consistency between AI-generated clinical notes and expert gold standards using BERTScore and COMET
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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).…
لغة النص الأصلي: الإنجليزية
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
لغة النص الأصلي: الإنجليزية
P-adic ultrametric skill embeddings with MLX Snowflake Arctic, DuckDB VSS, and full SPI tracing
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
Research Python/Z3 patterns and APIs for semantic theory development. Invoke for Python-language research tasks.
لغة النص الأصلي: الإنجليزية
Multi-backend deep learning library for building, training, running inference, and saving neural network models in Python.
لغة النص الأصلي: الإنجليزية
python library
لغة النص الأصلي: الإنجليزية
Machine learning library for classical supervised/unsupervised models, preprocessing, and model evaluation using NumPy/SciPy.
لغة النص الأصلي: الإنجليزية