252 件 ・ 関連度順
概要と使いどころ
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.
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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, 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.
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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。
Orchestrate a phased WinForms-to-Avalonia migration: behavior contracts, UI-independent application logic, platform adapters, MVVM shell, parity/cutover, evolution, and hardening. Use when the request explicitly involves moving a WinForms or .NET Framework desktop UI to Avalonia, or continuing that migration. Do not trigger for unrelated migrations, rewrites, ViewModel refactors, settings work, or command-line work without WinForms-to-Avalonia context. Load this orchestrator before one matching phase skill.
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用户在选择投资标的或商业模式时，纠结于"便宜买差企业"还是"合理价买好企业"。 语言信号:"这个便宜能不能捡"/"估值低是不是就值得买"/"贵的好公司值不值得买"/"便宜货有什么问题" 不适用于:已有明确投资方法论且不需要在两种风格间做选择的场景。
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。
Forward Deployed Engineer delivery contract for AWS engagements, build-first artifacts, grounded cost estimates, Well-Architected review, evolution roadmap
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Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).
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Brownfield improvements — the full WDS pipeline in miniature for existing products
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Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.
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。
Run one iteration of proteus memory-summary evolution. Called by meta_harness.py.
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。
Self-Evolving Development Loop - Dynamic skill generation with learning and evolution
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Reserved-field rules and field-number lookup for evolving AppSettings, plus the Rust/Kotlin diagnostics wire contract. Use before assigning or removing a proto field. DataStore mapping: protobuf-datastore.
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Kotlin mapping for the AppSettings proto-backed DataStore: UI-state and native-preferences conversion. Use when adding or reading an app-settings field. Removing a field: use protobuf-schema-evolution first.
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Decompose and explain healthcare cost growth into contributing factors including utilization changes, unit price shifts, case mix evolution, and population changes. Use when analyzing PMPM cost trends, explaining cost variance to leadership, preparing actuarial summaries, or identifying cost reduction opportunities.
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Add a new single-objective optimization algorithm to the sci-comp library. Use when the user asks to implement a new optimizer (e.g. gradient descent, BFGS, simulated annealing, differential evolution).
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Industrial production of iron and steel from ore to finished product — blast furnace, Bessemer converter, open-hearth, basic oxygen, electric arc, continuous casting, rolling, and secondary metallurgy. Covers the chemistry of decarburization, dephosphorization, and desulfurization, the metallurgy of plain carbon steels, the evolution from puddling and rolling to BOF and EAF, and the process-structure-property chain that makes mild steel the default structural material of the industrial era.
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Distributed storage systems design and operation for cloud platforms. Covers the GFS/HDFS block-and-master pattern, object storage (Swift/S3) with consistent hashing and eventual consistency, block storage semantics, replication vs erasure coding, the CAP theorem in practice, read-repair and anti-entropy, snapshot chains, and the GFS/BigTable/Spanner evolution. Use when designing a storage subsystem, choosing between object/block/file, or reviewing a replication and consistency strategy.
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ALIFE 2025 Proceedings - 337 pages, 80+ papers on artificial life, evolution, emergence, cellular automata, neural networks, agent-based models. Full Mathpix extraction with equations and figures.
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Comprehensive Artificial Life skill combining ALIFE2025 proceedings, classic texts (Axelrod, Epstein-Axtell), ALIEN simulation, Lenia, NCA, swarm intelligence, and evolutionary computation. 337 pages extracted, 80+ papers, 153 figures.
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Artificial Life atlas covering open-ended evolution, chemical reaction networks, morphogenesis, self-replication, and programmable chemistry. Use when picking a simulation framework, wiring ALife models into the skill graph, or onboarding contributors to the ALife cluster.
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Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.
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Weekly review of IWE work culture (element 14 — System Evolution). Runs during Week Close.
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。