548 件 ・ 関連度順
概要と使いどころ
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.
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。
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。
Social media strategy, content creation, and platform optimization. Use when creating social content, developing engagement strategies, optimizing for platform algorithms, or building community.
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。
Delegates coding/research tasks to the Google Antigravity CLI (`agy`) for external-model execution (Gemini 3.x, Claude Sonnet/Opus 4.6, GPT-OSS). Replaces the broken `collaborating-with-gemini` skill. Use when: (1) External-model delegation via Antigravity, (2) Multi-model prototyping (switch model per call), (3) Backend/logic implementation, (4) Algorithm design and optimization, (5) Bug analysis and debugging, (6) API/database code generation, (7) Code review. Triggers: "delegate to agy", "use Antigravity", "external model", "agy", "Gemini 3.5", "Claude Sonnet 4.6", "GPT-OSS". IMPORTANT: Always request unified diff patches only. Supports multi-turn sessions via SESSION_ID.
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。
6-step framework for engineering viral product launches on X. Covers emotional positioning, copy scoring, algorithmic gamification, and 48-hour engagement strategy. Use when: prepare for a launch, launch playbook, viral marketing campaign, launch on X, product launch strategy, write launch copy.
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。
Produce video scripts and clipping plans for TikTok, YouTube Shorts, Instagram Reels, and long-form YouTube. Generates hook-first scripts optimized for each platform's algorithm, plus a clipping plan to extract short-form from long-form content. Use when "video script", "TikTok script", "YouTube script", "Reels script", "Shorts script", "video content", "clipping plan", "video ideas", or any request to create video content for social platforms.
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。
Audit any URL against 17-point technical SEO checklist + algorithmic authorship rules. Uses /browse if available, falls back to checklist-based audit.
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。
An agent skill that uses BlurHash, the compact image placeholder representation algorithm by Wolt, to generate and decode tiny hash strings that represent blurred previews of images. Enables instant placeholder rendering in web and mobile applications while full images load.
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。
Inspect an SSH endpoint or config for outdated ciphers, key exchange choices, and hardening gaps before exposure or upgrades.
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。
Nature-style engineering manuscript reviewer skill for testing devices, robots, platforms, systems, algorithms embodied in physical systems and engineering methods against requirements, validation, benchmarking, uncertainty, robustness and failure modes.
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。
Reference patterns for search, optimization, and greedy algorithms. Covers binary search (classic and on-answer), two pointers, sliding window, greedy strategies, prefix sums, merge intervals, and monotonic stack. Use when solving problems that involve searching sorted data, optimizing over constraints, processing subarrays/substrings, or reducing range queries.
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。
Verify a competitive programming or LeetCode solution for correctness, edge cases, and performance. Use when asked to "verify my solution", "check my code", "is this solution correct", "test my solution", "review my competitive programming answer", "validate my algorithm", or any solution verification request.
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。
Byzantine consensus voting for multi-agent decision making. Implements voting protocols, conflict resolution, and agreement algorithms for reaching consensus among multiple agents.
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 developing or extending EZ_math_model itself from a written implementation plan, especially adding algorithms, tool skills, templates, or multi-step repo changes.
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。
Build MindNode-inspired mind mapping interfaces — 8 node shapes, 27+ themes, layout algorithms (horizontal, vertical, radial with D3), keyboard shortcuts, focus mode, dual view. React + xyflow/react v12 + Zustand + Tailwind. Triggers on mind map, visual thinking tool, node-based diagram, brainstorm canvas, MindNode.
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 and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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。
Solve Diophantine equations (integer-only solutions) including linear, quadratic, and Pell equations. Covers the extended Euclidean algorithm, descent methods, and existence proofs. Use when finding all integer solutions to ax + by = c, solving Pell's equation, generating Pythagorean triples, proving no integer solutions exist via modular constraints, or finding the fundamental solution from which all others are generated.
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。
Compose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.
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。
Analyze prime numbers using primality tests, factorization algorithms, prime distribution analysis, and sieve methods. Covers trial division, Miller-Rabin, Sieve of Eratosthenes, and the Prime Number Theorem. Use when determining whether an integer is prime or composite, finding prime factorizations, counting or listing primes up to a bound, or investigating prime properties within a number-theoretic proof or computation.
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。
Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recursion, linear algebra) that benefit from compiled code.
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。
Cheminformatics toolkit for fine-grained molecular control. Parse and write SMILES, SDF, MOL and InChI; compute descriptors (MW, LogP, TPSA, QED, Bertz); build fingerprints (Morgan/ECFP, RDKit, MACCS, atom pair, torsion) and score Tanimoto, Dice or cosine similarity; run SMARTS substructure search and reaction SMARTS; generate 2D depictions and ETKDG 3D conformers; extract Murcko scaffolds and canonical hashes; control sanitization and stereochemistry directly. Also trigger on rdkit, Chem.MolFromSmiles, rdFingerprintGenerator, SDMolSupplier, SMARTS query, ETKDG, or FilterCatalog. For standard workflows with a simpler interface use the datamol skill, which wraps RDKit; use rdkit for advanced control, custom sanitization, and specialized 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。
Use when a protocol, concurrent algorithm, or design needs a model-checked TLA+ or Alloy spec, or a TLC or Apalache trace needs reading. Not for choosing when to model: use validation-first-driven.
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。
Systematic methodology for achieving 100% backward dataflow reachability in a new language. Create gauntlet fixture, write trace, diagnose gaps, fix analyzer/algorithm, iterate to 100%. Language-agnostic process. Use when: (1) adding a new language to Grafema, (2) auditing dataflow coverage for existing language, (3) user says "/dataflow-gauntlet".
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。