245 件 ・ 関連度順
概要と使いどころ
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or EvolutionaryScale Forge API. Use AlphaFold for traditional folding; RDKit for small molecules.
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 AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.
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。
Guide for managing consent for scientific research under GDPR Article 89 and Recital 33 broad consent provisions. Covers ethical review board coordination, purpose evolution management, appropriate safeguards including pseudonymization, and the interplay between consent and other lawful bases for research processing.
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。
Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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。
Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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。
Optimize DSPy programs with dspy.GEPA — a reflective/evolutionary optimizer to consider against task-specific baselines within an authorized evaluation budget. Use when the user says optimize, compile, GEPA, reflective optimization, or "make this program better" and a DSPy program + metric + trainset exist.
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 Chinese notes, articles, manuscripts, and digital card-box material with FIRE 2.0: Full-D numbering, Index keyword webs, Route thinking paths, and Evolution over time. Use when Codex needs to prepare material for semantic search, turn temporary notes into permanent or project notes, build table-of-contents/index/search structures, create compact Chinese FIRE cards, or maintain this skill's original discussion thread provenance.
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。
Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false positives in bug localization. Supports git repositories and provides explanations for why commits are identified as bug-introducing.
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。
Automatically generate regression tests for Java codebases by analyzing changes between old and new code versions. Use when users need to: (1) Generate tests after refactoring or code changes, (2) Ensure previously tested behavior still works in new versions, (3) Cover modified or newly added code paths, (4) Migrate existing tests to work with updated APIs or signatures, (5) Maintain test coverage during code evolution. Supports JUnit and TestNG frameworks with unit tests, parameterized tests, and exception testing patterns.
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。
Estimate divergence times using molecular clock models with BEAST2, MCMCTree, and TreePL. Use when dating speciation events, calibrating phylogenies with fossils, choosing between strict and relaxed clock models, or estimating evolutionary rates across lineages.
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。
Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, generating bootstrap consensus, or needing quick exploratory phylogenies before running full ML analysis.
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 codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.
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。
Workflow for orthology, synteny, ancestral reconstruction, and evolutionary comparison across genomes.
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。
Think and work like an expert Geomorphologist. Use when a task calls for Geomorphologist judgment. Reasons from coupled form, process, and time and from rates, thresholds, and lag times through field mapping, lidar/SfM DEM morphometry (chi profiles, Ksn in LSDTopoTools/Landlab), cosmogenic nuclide dating (CRONUS-Earth, OSL, U-Th), and landscape-evolution models, while treating equifinality, inheritance, DEM artifacts, and steady-state assumed over transient response as first-class 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。
Think and work like an expert Hematologist. Use when a task calls for Hematologist judgment. Reasons from hematopoietic hierarchy, clonal evolution, and hemostatic balance through peripheral smear review, reticulocyte production index, flow cytometry, mixing studies, and WHO/ICC-anchored NGS and cytogenetics, while treating pseudothrombocytopenia, preanalytic line-draw and EDTA artifacts, and missed consumptive emergencies like TTP and DIC as first-class 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。
Think and work like an expert Cancer Biologist. Use when a task calls for Cancer Biologist judgment. Reasons from hallmark capabilities, clonal evolution, and TME context; separates driver from passenger, cell-autonomous from stromal mechanisms, and 2D artifacts from PDO/PDX-validated dependencies using TCGA, DepMap, OncoKB, and REMARK-grade biomarker logic.
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。
Migration specialist for zero-downtime schema changes, data migrations, and backward-compatible evolutionUse when "migration, schema change, database migration, zero downtime, backward compatible, rollback, blue green, data migration, migration, schema, database, zero-downtime, backward-compatible, rollback, blue-green, feature-flag, ml-memory" mentioned.
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 in navigating and manipulating git history. Covers finding bugs with bisect, understanding code evolution, recovering lost work, and rewriting history safely. Understands that git history is your time machine. Use when "git history, git bisect, find when bug introduced, recover deleted, rewrite history, git blame, lost commit, " mentioned.
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。
The enhancement layer for AI-generated content. This skill covers AI-powered visual effects, compositing, upscaling, restoration, and post-production magic—turning raw AI output into polished, professional content. AI generation gets you 80% of the way. Visual effects get you the remaining 20% that separates "clearly AI" from "how did they do that?" This skill covers ComfyUI workflows, Runway's AI tools, intelligent upscaling, rotoscoping, color grading, and the integration of AI elements into traditional footage. The practitioners of this skill are technical artists who understand both traditional VFX workflows and the new AI-native approaches that are revolutionizing post-production. Use when "AI visual effects, VFX, upscale, upscaling, composite, rotoscope, background removal, color grade, inpaint, outpaint, style transfer, enhance, ComfyUI, post-production AI, vfx, visual-effects, compositing, upscaling, post-production, comfyui, enhancement, color-grading" mentioned.
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。
Version REST and GraphQL APIs. Use when a user asks to version an API, handle breaking changes, implement API deprecation, manage multiple API versions, or design an API evolution strategy.
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。
You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.
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。
Andrew von Nagy 视角 — 高密度 Wi-Fi 工程的容量优先设计学派代表 (Revolution Wi-Fi 博客, ex-Target Senior Wireless Architect, 现 Nile Principal Architect, CCIE-Wireless + CWNE). 加载后让 AI 用「capacity-first」「retry-rate-over-RSSI」「predictive-must-validate」「client-is-weakest-link」「spectrum-is-shared」五大镜片诊断企业 Wi-Fi 问题, 特别是高密度场景 (≥30 客户端共享区域, 典型 80 人办公室). 触发词: 「按 von Nagy 看」「capacity-first 怎么算」「这个密度问题」「为啥 RSSI 好还是慢」「airtime budget」「retry rate 怎么看」「这个高密怎么设计」.
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。
Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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。
Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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。