「hig」の検索結果

Compile one or more OpenViking knowledge bases or document collections into topic-organized, evidence-grounded high-level knowledge, including cross-source findings, trends, changes, drivers, comparisons, implications, and uncertainties. Use with ov compile when the user asks to distill or synthesize a knowledge base, compare multiple collections, or derive higher-order insights such as changes across financial reports; do not use for document-by-document summaries.

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Create premium, Awwwards-quality website designs as React (.jsx) components that look like they were built by a top-tier agency charging $50k+ per project. Use this skill whenever the user asks for a website, landing page, portfolio, or web component that should look expensive, premium, luxury, editorial, high-end, or agency-quality. Also trigger when the user mentions Awwwards, FWA, award-winning design, or references brands like Apple, Aesop, Bottega Veneta, Stripe, or any luxury/fashion/design-forward brand. Trigger when the user says things like "make it look professional", "make it look expensive", "I want it to look really good", "high quality design", "not generic", or expresses dissatisfaction with typical AI-generated website aesthetics. Do NOT trigger for dashboards, admin panels, internal tools, or functional UI where aesthetics are secondary to utility — the standard frontend-design skill handles those.

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Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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。

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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。

EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.

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。

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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。

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

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。

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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。

Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for review.

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Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.

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 a high-contrast clean skeuomorphic design system with molded dark surfaces, crisp light separation, tactile inset depth, and restrained signal accents.

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 sharp, physically coherent high-resolution materials for 3D rendering with appropriate PBR maps, texel density, UV direction, mipmaps, anisotropic filtering, and progressive asset delivery. Use for detailed wood, stone, fabric, foliage, and architectural close-ups without excessive download or GPU memory costs.

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。

Manage the Higress AI Gateway via its Console API (consumers, routes, AI providers, MCP servers). Use when creating consumers, configuring routes, or managing AI gateway settings.

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 unattended IDEATION iteration of the quality-assurance loop — find the highest-value untested behavior in the codebase, judge it against the QA value bar, and file ONE locked `qa` issue specifying the test to write. Never writes code or tests; the next-qa skill builds from the queue this skill fills. Use when the user says "QAアイデア", "next qa idea", or wants the QA backlog refilled without implementation.

Read the open paper and write study annotations into its PDF with zotero-cli - a context box on the title, a four-part summary on the abstract, role-coded abstract highlights, one box per figure panel with a teaching comment, colour-coded body highlights, and a reading note. Use when asked to "annotate", "read and annotate", or "help me understand" a paper.

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。

Hunt Forgot Password / Account Recovery Authentication Flaws — 5 distinct patterns: (1) username enumeration via different responses for valid vs invalid email, (2) reset token exposed directly in the API response body, (3) reset token not invalidated after use (replay), (4) password reset link works from a different IP/browser (no binding), (5) no rate limit on the reset request endpoint. These are the standalone recovery-flow broken-auth primitives — distinct from reset-email host-header poisoning (hunt-host-header) and the full ATO chain (hunt-ato owns password-reset as an ATO path; prove the primitive here, chain it there). Detection: trace the full forgot-password flow from request to token to use; check response diffs between valid/invalid emails; test token replay after consumption. Medium to High (enumeration=Medium, token-reuse=High, account-takeover=Critical when chained to known-email).

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。

Convene the Council of High Intelligence — multi-persona deliberation with historical thinkers for deeper analysis of complex problems.

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。

Brainstorm a longlist of benefits and costs for a project. Outputs two clean tables (benefits and costs), each with materiality (H/M/L), a cash flow tag (cash in / cash out / non-cash), a quantification method, and a monetisation method. Supports HMT Green Book, EU Better Regulation Guidelines, World Bank, Asian Development Bank, and the Victorian Treasury High Value High Risk (HVHR) framework.

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。

Score and diagnose Google Ads landing pages. Use when asked to audit a landing page, check landing page quality, diagnose high-CTR but low-conversion-rate ad groups, improve Quality Score's Landing Page Experience component, or compare an ad group's messaging against its landing page. Trigger on "landing page audit", "landing page score", "landing page quality", "why is my conversion rate low", "LPX", "landing page experience", "ad to page match", or when `/google-ads-audit` surfaces a high-CTR / low-CVR ad group.

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Use this skill when the user is dropping below the higher-level DOCA libraries (doca-rdma / doca-eth / doca-rmax) into the raw-verbs escape hatch — managing QP / CQ / PD / MR / SRQ / AH / CC-group / Ethernet-SQ-RQ primitives inside DOCA Core, porting libibverbs code into the DOCA Core model, capability-querying a specific verb / opcode / WR flag / QP attribute via doca_verbs_query_device, or debugging DOCA_ERROR_* from doca_verbs_* calls. Trigger even when the user does not say "doca-verbs" — implicit phrasings include "raw QP attribute the task API doesn't expose", "keep my ibv_* code next to doca_* on the same QP", "IO_FAILED on WR submit", "QP state transition rejected", "attach a congestion- control group", or "porting my libibverbs code". The skill's first job is to route MOST users back UP to the higher-level library. Refuse and route elsewhere for general doca-rdma / doca-eth / doca-rmax workloads, DOCA install, Core internals, and general libibverbs theory — those belong to other skills.

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。

长视频 / 直播录像高光切片：从一条长视频里找出高光片段，切成多条独立短视频，可选转竖版 9:16 + 加字幕。找点两种方式——音频能量峰值（情绪高涨/欢呼/大声处）或转录后由内容判断挑金句段。当用户说 直播切片、高光切片、长视频切短、录像剪精华、切片、高光时刻、把直播剪成短视频、提取精彩片段、长视频找亮点 时使用。基于 shared/scripts/highlight_cut.py（librosa 能量分析）。与 clipify 区别：clipify 专做英文口播找笑点+动态人脸pan，本 SKILL 更通用（中文/直播皆可）、静态转竖版更稳。

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从长视频中自动提取精彩片段，切成独立短视频，支持 16:9→9:16 竖版转制和逐字字幕烧录。 当用户说"视频切片""提取精彩片段""长视频切短""切成短视频""高光剪辑""逐字字幕""转竖版短视频"时使用。 和 video-highlights 的区别：clipify 专做英文口播找笑点+动态人脸 pan；video-highlights 更通用（中文/直播皆可），静态转竖版更稳。

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。

Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.

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。