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概要と使いどころ
Design, implement, optimize, and review SIMD code in .NET. USE FOR: vectorizing scalar loops with TensorPrimitives, Vector64/128/256/512, or platform hardware intrinsics; reviewing existing SIMD code, including the generic Vector type, for contract equivalence, tail handling, memory safety, portability, fallbacks, and measured performance. DO NOT USE FOR: performance work unrelated to SIMD or vectorization.
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。
Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.
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 the user is investigating QML / Qt Quick performance — both vague complaints ("the UI feels laggy", "this is slow", "frames are dropping", "the app stutters") and explicit asks to profile, find hotspots, or optimize bindings, signals, or rendering. Runs qmlprofiler on a 2D QML application, parses the .qtd trace, and analyzes hotspots against the source with frame-time, memory, and pixmap-cache summaries. Does NOT cover Qt Quick 3D.
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。
IR playbook execution, evidence collection, forensic timeline analysis, memory forensics, and post-incident reporting following NIST SP 800-61 and SANS PICERL methodology
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。
Deploy, update, validate, and troubleshoot the AgentKit hybrid-cloud customer-service demo in this directory. Use when a user asks an AI coding agent to follow the README, deploy or update the demo, configure OpenAPI/Runtime/Knowledge/Memory/Sandbox/MCP/Skills/A2A, run customer-view validation, or record manual platform steps and failures.
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 code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
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。
官方Viking CLI 命令行助手：本CLI覆盖火山引擎/BytePlus VikingDB(向量库)/Knowledge(知识库)/Memory(记忆库)的数据集管理和数据的读写及检索， 可用于扩展Agent的知识检索边界 提升Agent的记忆能力； 当用户对知识库/向量库提问时，使用本Skill; 当用户要操作向量库/知识库 或 从向量库/知识库检索信息时使用本Skill； 当用户要记忆检索和记忆存储时，使用本Skill。
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 native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.
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 this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
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 you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit
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 you need to refactor Java code based on trusted profiling analysis findings — including reviewing repository-owned or maintainer-sanitized docs/profiling-problem-analysis and docs/profiling-solutions files, identifying specific performance bottlenecks, and implementing targeted code changes to address CPU, memory, or threading issues. This should trigger for requests such as Refactor the code with profiling; Apply profiling; Optimize hot path; Reduce allocations found in profiling; Fix CPU bottlenecks from profiling analysis. Part of Plinth Toolkit
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 you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit
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 you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements. This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput. Part of Plinth Toolkit
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。
Initialize and configure the Operator User Partition.
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 or refresh the Operator Project Index.
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。
Initialize and configure the Operator Project Brain.
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 or refresh the Operator Project Index.
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。
Initialize and configure the Operator User Partition.
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。
Initialize and configure the Operator Project Brain.
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, repair, audit or consolidate agent skills (SKILL.md packages). Use when: writing or fixing a skill, its description or structure. Not for one-off lessons; use Memory.
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。
Write, find or curate lessons and agent rules with stated evidence and limits. Use when: asked to remember something or write a rule into agent instructions.
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。
Turns a hard product or career decision into a one-page decision memo with the eigenquestion, options, pre-mortem with kill criteria, reversibility rating, a named decider and a review date. Use when you ask 'how should we decide', 'should we build or kill this', 'one-way door or two-way door', 'run a pre-mortem', 'we keep going in circles', 'quit or pivot', or want to grade a decision doc. Draws on 396 insights from 177 Lenny's Podcast guests, led by Annie Duke, Shreyas Doshi and Shishir Mehrotra.
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。
Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.
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。
Go performance optimization techniques including profiling with pprof, memory optimization, concurrency patterns, and escape 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。