「hig」の検索結果

Expert TISAX (Trusted Information Security Assessment Exchange) advisor for the automotive supply chain — the ENX/VDA assessment regime that OEMs like VW, BMW, and Mercedes-Benz require from suppliers and service providers. Covers the VDA ISA 6 catalogue (current through 2026) and the ISA2027 transition (published July 1, 2026; mandatory for assessments ordered from January 1, 2027), assessment levels AL1/AL2/AL3, all 12 assessment objectives/labels (Confidential, Strictly Confidential, High/Very High Availability, Proto Parts/Vehicles, Test Vehicles, Proto Events, Data, Special Data), maturity scoring (0–5, target 3, cutback rules), the ENX portal process, scoping, audit-provider selection, corrective action plans and the 9-month window, 3-year label validity, and ISO 27001 mapping. Use for any TISAX, VDA ISA, ENX, automotive information-security assessment, prototype protection, or OEM supplier-security requirement question — gap assessments, label selection, readiness, and audit prep.

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When the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.

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Depth assessment of whether an AI system is high-risk under Art. 6 of the EU AI Act, grounded in the Commission's draft Art. 6(5) classification guidelines (general principles + Annex I + Annex III). Covers the Annex I product-safety route, all eight Annex III areas with worked examples, the Art. 6(3) exception and its profiling re-exception, and the Art. 25 quasi-provider trap. Outputs a structured decision block, a practitioner memo, and a JSON interchange artefact.

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Determines whether a technology qualifies as an AI system under Art. 3(1) of the EU AI Act and classifies its risk tier (prohibited, high-risk, GPAI with systemic risk, limited risk, minimal risk). This skill should be used when the user asks to "classify an AI system under the AI Act", "determine the AI Act risk tier", "check if something is an AI system", "assess prohibited practices", "check high-risk classification", "determine Art. 6 exception applicability", or mentions "KI-Verordnung", "Risikoklassifizierung", Art. 5, Annex III, or GPAI systemic risk.

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EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.

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When you want to read and extract structured notes from a book — PDF, EPUB, MOBI, markdown, .txt, pasted text, or URL to a public-domain work. Reads in chunks (by chapter when a TOC exists, by 50-page blocks otherwise), extracts per-chapter TL;DR + key concepts + quotes + action items + frameworks, and offers to capture to second-brain raw/ as a highlights- file. Four modes — notes (default, chapter-by-chapter), summary (whole-book TL;DR + 3–5 takeaways), quotes (pull-quote highlights only), study (notes + Q&A spaced-rep prep). Triggers on "/read-book," "read this book," "extract notes from this PDF," "what's in this book," "summarize this ebook," "pull quotes from this." Sibling to watch-video (same content-consumption pattern, different medium).

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Expert blueprint for rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensi

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Comprehensive UI/UX review for macOS Tahoe apps. Covers Liquid Glass design, HIG compliance, SwiftUI patterns, and accessibility. Use when reviewing macOS UI or checking HIG compliance.

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Web search using You.com Search API with high-quality, cited results — runs keyless out of the box; YDC_API_KEY unlocks higher limits and real-time web crawling

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Evidence-first development — surround the implementation with an executable spec and a gauntlet of constraints (tests, types, coverage, mutation) so line-by-line review becomes optional. Use when the user explicitly asks for high-assurance or evidence-first work ("reliable", "TDD", "prove it works", "I won't read the code"), or when the change touches high-stakes domains (money, auth, data loss, concurrency, public API). For routine changes where the user just wants normal tests, write good tests directly instead of invoking this loop.

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Make a 12–20 second high-energy motion-graphics promo for any product, rendered from code to MP4 with synthesised music. Fast beat-locked scene changes, kinetic typography, a shape that carries across every transition, a HUD frame. Use when someone wants a hype reel, teaser, launch sting or showreel-style promo for a product, app or brand.

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Standardizes the creation and evaluation of high-density Agent Skills (Claude, Cursor, Windsurf). Ensures skills achieve high Activation (specificity/completeness) and Implementation (conciseness/actionability) scores. Use when: writing or auditing SKILL.md, improving trigger accuracy, or refactoring skills to reduce redundancy and maximize token ROI.

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디자인·프론트 업계가 '감'으로 쓰던 인터페이스 디테일을 수치화한 규칙으로 적용(APPLY)하거나 감사(AUDIT)한다. Jakub Krehel의 make-interfaces-feel-better 철학을 계승 + HIG/Material/WCAG/DS 토큰/실무자 리서치로 확장한 17축·113규칙(provenance 등급별). 모션 타이밍·이징·동심원 radius·tabular-nums·44px 타깃·focus ring·prefers-reduced-motion 등. 'feel 좋게 다듬어줘', '인터페이스 디테일 적용', 'feel 점검', '마이크로 인터랙션 손봐줘', 'make this feel better', 'polish the interactions', 「インターフェースの細部を詰めて」, 「介面細節打磨」 류에 트리거. 브랜드 토큰은 DESIGN.md(omd:apply)가 우선.

Answer a hard question by fanning it out to a PANEL of models running in parallel — each answering independently with web search and bash, none seeing the others' work — then having Opus 4.8 judge every response into a structured analysis (consensus, contradictions, partial coverage, unique insights, blind spots) and write a final answer grounded in it. The panel is two independent Opus 4.8 runs (slug opus4.8-4.8), Opus 4.8 + GPT-5.5 via codex (opus4.8-gpt5.5), Opus 4.8 + Gemini 3.1 Pro via agy (opus4.8-gemini3.1pro), or all three (opus4.8-gpt5.5-gemini3.1pro). Opus always judges and writes the final answer — the pipeline can't be reversed. Runs on local CLI subscriptions (no metered API), and saves a timestamped provenance .md per run. Use this whenever the user asks to "run it through Fusion", says /fusion, wants a multi-model / panel / ensemble answer, wants a question cross-checked across models, or wants a higher-confidence answer with consensus and blind spots surfaced — even if they don't say "fusion". General-purpose: any topic (research, law, strategy, technical, personal). Best for high-stakes research, design calls, and debugging where being confidently wrong is expensive.

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Produce a high-confidence, CONCISE implementation / architecture / strategy PLAN that follows the OMC plan system end to end. It first runs OMC's real requirements interview (auto-chains the `omc-plan` skill: one question at a time, explore-first, Analyst consult) to produce an initial plan in `.omc/plans/`, then DEEPENS that plan with a 3-round ITERATIVE fusion loop — each round an Opus 4.8 panelist (Agent subagent) and a GPT-5.5 panelist (via codex) critique/improve it IN PARALLEL and BLIND, then Opus 4.8 (the orchestrator) judges and synthesizes one tighter plan that seeds the next round, stopping early on convergence. Writes the final dense plan back to the same `.omc/plans/<slug>.md`, then offers the OMC handoff (`/omc-plan --review` → `/team` or `/ralph`). Use for high-stakes planning, architecture decisions, strategy calls, and hard debugging — especially when the user runs /plan or asks how to approach a non-trivial build. Reuses the `fusion` skill's run_codex.sh + judge rubric. NOT for trivial tasks (single pass is enough).

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Use the official Higgsfield CLI or its Blender integration to discover models, generate requested assets and resume recorded jobs.

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Audit and rewrite an Instagram profile end-to-end for 2026: profile photo, searchable NAME field weighted with a keyword, @handle, bio (150 chars: value plus topic plus proof), goal-matched link, category label, story highlights, the first-9 grid, and up to 3 pinned posts. Triggers on "review my Instagram profile", "fix my bio", "optimize highlights", "profile audit". The whole follow decision happens on the profile header. Not for writing captions (use ig-caption-writer).

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Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.

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Create shot lists for highlight videos. Timestamp key plays, music cue suggestions, pacing. Platform-specific cuts for TikTok, YouTube.

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Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.

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Guides the Art. 36 prior consultation process when a DPIA indicates high residual risk that cannot be mitigated. Covers required documentation per Art. 36(3), the 8-week DPA response timeline, outcome management, and interaction protocols with supervisory authorities. Keywords: prior consultation, Art. 36, supervisory authority, DPA, high residual risk, DPIA escalation, consultation documentation.

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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.

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Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.

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