「algorithm」の検索結果

Create, standardize, or update step-by-step algorithm visualizations for a given problem ID. Use when the user mentions 'visual', 'visualization', 'animate', or 'step-by-step' alongside a problem ID in /problem/:id format.

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Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.

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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.

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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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Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

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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.

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Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.

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Validates DAG structures, performs topological sorting, detects cycles, and resolves dependency conflicts. Uses Kahn's algorithm for optimal execution ordering. Activate on 'resolve dependencies', 'topological sort', 'cycle detection', 'dependency order', 'validate dag'. NOT for building DAGs (use dag-graph-builder) or scheduling execution (use dag-task-scheduler).

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Apply hierarchical decomposition and structural exploitation strategies from DAG theory to complex system design, problem-solving, and abstraction challenges

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Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.

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API gateway and reverse proxy configuration with Kong, Nginx, Traefik, routing, and auth middleware. Activate on: API gateway, reverse proxy, Kong, Nginx, Traefik, load balancer, ingress, auth middleware. NOT for: rate limiting algorithms (use api-rate-limiting-throttling-expert), service mesh (use service-mesh-microservices-expert).

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This skill suggests using real-time continuous glucose monitoring (CGM) and algorithm-driven insulin pumps (ADIPs) rather than multiple daily injections (MDIs) with self-monitoring of blood glucose (SMBG) three or more times daily for adults and children with type 1 diabetes (T1D). Trigger phrases include "Should I use RT-CGM+ADIP or MDI+SMBG for this T1D patient?" or "Is intensive pump therapy indicated for this patient?"

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The skill suggests using real-time continuous glucose monitoring (CGM) combined with algorithm-driven insulin pumps (ADIPs) rather than multiple daily injections (MDIs) with self-monitoring of blood glucose (SMBG) three or more times daily for adults and children with type 1 diabetes (T1D) to lower hypoglycemia incidence. Clinical triggers include discussions about insulin pump therapy, CGM use, or reducing hypoglycemia in T1D patients on intensive insulin regimens.

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Recommends continuing a patient’s personal continuous glucose monitor (CGM) in the hospital with or without algorithm-driven insulin pump (ADIP) therapy rather than discontinuing it for individuals already using CGM outpatient. Use when deciding whether to maintain personal CGM during admission or when a patient requests to keep their device, particularly in those at high risk for hypoglycemia.

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Recommends use of hybrid closed-loop pump rather than insulin pump with CGM (without algorithm) or multiple daily injections with CGM for pregnant individuals with type 1 diabetes. Triggered when deciding on insulin delivery methods for pregnant patients with type 1 diabetes.

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Diagnose monogenic and syndromic obesity in children and adolescents using a structured step-by-step algorithm. Use this skill whenever a clinician suspects a genetic cause of obesity, asks about leptin deficiency, MC4R mutation, POMC deficiency, PCSK1 deficiency, leptin receptor deficiency, Bardet-Biedl syndrome, Prader-Willi syndrome, Alström syndrome, or any case of early-onset severe obesity with hyperphagia. Also trigger for questions about targeted pharmacotherapy including setmelanotide or metreleptin, or when to order a genomic obesity panel. Cross-references the NHS Genomic Test Finder skill to surface the relevant R-code once a diagnosis is reached.

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-level distributed systems theory covering consistency models, consensus algorithms, fault tolerance, replication, distributed transactions, and the CAP theorem.

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Expert-level optimization knowledge. Use when working with linear programming, convex optimization, nonlinear programming, integer programming, dynamic programming, gradient methods, duality theory, or optimization algorithms. Also use when the user mentions 'linear programming', 'simplex method', 'convex optimization', 'Lagrangian', 'KKT conditions', 'gradient descent', 'Newton method', 'integer programming', 'dynamic programming', 'duality', 'Bellman equation', or 'branch and bound'.

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-level graph theory knowledge. Use when working with graph algorithms, network flows, matching, graph coloring, planar graphs, spectral graph theory, random graphs, or graph applications in computer science and networks. Also use when the user mentions 'shortest path', 'minimum spanning tree', 'network flow', 'bipartite matching', 'graph coloring', 'clique', 'independent set', 'vertex cover', 'planarity', 'graph isomorphism', 'adjacency matrix', or 'Laplacian'.

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-level numerical methods knowledge. Use when working with root finding, numerical integration, numerical differentiation, interpolation, numerical linear algebra, ODEs, PDEs, or optimization algorithms. Also use when the user mentions 'Newton method', 'bisection', 'Runge-Kutta', 'finite difference', 'finite element', 'interpolation', 'numerical integration', 'quadrature', 'iterative solvers', 'condition number', 'round-off error', or 'truncation error'.

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[Debugging] Use when analyzing or optimizing performance bottlenecks: database queries, N+1 fan-out, indexing, API latency, memory/GC, concurrency and pool saturation, algorithmic complexity (O(n²)), network/protocol round trips, frontend rendering and Core Web Vitals, caching, and distributed/resilience paths. Calibration constants and domain laws (latency ladder, Little's Law, utilization knee, CWV thresholds, symptom→cause triage) live in references/performance-knowledge.md.

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SOP for locating, auditing, and importing production-tested reference patterns and peer-reviewed literature. Trigger when: - Designing or implementing non-trivial algorithms, system protocols, design patterns, or major library integrations. - Prompt contains: prior art, reference implementation, research patterns, standard implementation, arXiv, RFC.

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Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.

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。