919 件 ・ 関連度順
概要と使いどころ
Design, review, and validate Azure cloud architectures. Use when choosing Azure compute, storage, networking, or identity services, or applying the Azure Well-Architected Framework to a workload.
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 wants to test their LLM/agent application for safety and security vulnerabilities — jailbreaks, prompt injection, PII extraction, harmful content generation, or evaluator gaming. Also use when the user mentions security testing, adversarial testing, red teaming, safety evaluation, ASR (Attack Success Rate), or "is my app safe to deploy." Outputs ASR paired with over-refusal rate and an audit document.
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 legal question, a set of draft questions, or source material such as a statute, jury instruction, rubric, contract checklist, or coding manual into a battery of narrow questions that TypeSafe's Jev, a fast classifier that returns probabilities rather than prose, can answer reliably across many documents. The skill breaks conclusions such as negligence, felony murder, or standing into element-level questions, writes the governing rule into each question, and delivers the questions alone, in Jev's request format or in plain English. Built for legal work that asks the same questions of many texts: coding statutes across jurisdictions, coding opinions or case files for empirical research, checking contracts against a review checklist, screening intake narratives, and grading student work against a rubric. Use when asked to write Jev questions, build a classifier for legal documents, or turn a legal test into yes-or-no questions. Running the questions requires Jev access; writing them does not.
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 Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-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。
Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.
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 debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.
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。
Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.
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 Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-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。
Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.
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 debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.
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。
Godot Expert Auditor: Aurelius. Exhaustive never-list enforcement and architectural slap-down for Godot 4.7 projects. Use when auditing signal decay, ObjectDB orphans, typed Array/Dictionary slop, material.duplicate batch breaks, export case-sensitivity, Expression.execute risks, or sector never-lists. Keywords: auditor, Aurelius, never-list, signal decay, ObjectDB orphans, typed Array, export case-sensitivity, instance uniforms, PackedScene.get_state.
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。
Query A-share and Chinese financial market data via multiple data sources. Tier-1 paid source is iFind (同花顺, covers stocks/funds/macro/bonds/HK-US/ESG/index-sectors). Tier-2 free source is AkShare (行情/财报/行业/指数). Use whenever the agent needs Chinese financial data — compatible with 沪深 A 股, 科创板, 创业板, 北交所, and 港股通 stocks.
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。
Three-statement financial model for A-share stocks. Adapts the standard 3-statement-model methodology for Chinese accounting standards (CAS), Chinese GAAP differences, and AkShare-sourced financials. Use instead of the original 3-statement-model skill when building integrated IS/BS/CF models for Chinese listed companies.
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。
DCF valuation model for A-share stocks using Chinese financial data. Uses AkShare MCP for financial statements, WACC inputs (risk-free rate from China government bond yields), and growth projections. Use instead of the original dcf-model skill for Chinese equities.
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 new China-specific financial skills following the established patterns and conventions. Use this meta-skill to scaffold new china-* skills that integrate with iFind MCP (Tier-1) and AkShare MCP (Tier-2 fallback), follow CAS accounting standards, and adapt workflows for the Chinese market. Triggers on "创建中国skill", "新建A股skill", "create china skill", "new china-skill", "制作skill", or when creating any new china-* 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。
Update A-share financial models with new quarterly data, management guidance, or macro changes. Reflects actuals, rolls estimates forward, flags material changes, and updates valuation. Adapted from the original model-update skill for Chinese market conventions and AkShare data. Triggers on "A股模型更新", "模型更新", "plug earnings into model", "update model for [company]", "刷新预测", or "更新财务模型".
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。
Comparable company analysis for A-share stocks. Uses AkShare MCP to build peer groups, pull financial data, compute valuation multiples (PE, PB, PS), and assess relative value within a Chinese industry sector.
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。
Institutional-quality equity research initiation reports for A-share companies. Covers company analysis, financial modeling, valuation, chart preparation, and final report assembly. Adapted from the original initiating-coverage skill for Chinese market conventions, CAS accounting, and AkShare data sources. Triggers on "A股首次覆盖", "首次评级", "initiate coverage China", "initiate on [company]", "A股研究报告", or "start research on [company]".
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。
Generic PowerPoint authoring skill for A-share investment analysis and pitch decks. Creates professional 路演PPT / 投资分析PPT for any A-share company using live data from iFind MCP (Tier-1) and AkShare MCP (Tier-2 fallback) tools. All company-specific values are parameterized — never hardcoded. Triggers on "A股PPT制作", "投资PPT", "制作PPT", "路演PPT", "pitch deck", "PowerPoint [company/ticker]", or any request to create slides for a Chinese stock. When invoked, use the `generate_a_share_ppt` script with --company and --ticker args.
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。
Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.
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 professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles. Adapted from the original datapack-builder skill for Chinese data sources (AkShare, 巨潮, exchange filings) and CAS accounting. Triggers on "A股数据包", "投行数据包", "datapack China", "due diligence pack A-share", or "IC data pack [company]".
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。
SwiftUI layout beyond stacks — the Layout protocol (when custom layout beats GeometryReader), Grid vs lazy grids, custom containers with sections and container values, and lazy-stack/ScrollView performance rules (what breaks laziness, prefetch discipline, scroll APIs). Use when building custom layouts or containers, fixing lazy-stack jank or memory growth, or wiring programmatic/snapping scrolling.
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。
Generates a streak tracking system with timezone-aware day boundaries, streak freeze protection, and streak-at-risk push notifications. Use when user wants daily/weekly engagement streaks, consecutive day tracking, or habit tracking.
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 epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi
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。