「hig」の検索結果

Abusing Active Directory object ACLs (DACL/ownership) for privilege escalation and lateral movement (GenericAll, GenericWrite, WriteDACL, WriteOwner, AddMember, ForceChangePassword, and replication rights via DS-Replication-Get-Changes-All). Use when BloodHound CE shows an outbound control edge from a principal you own toward a higher-value object, and you want the exact bloodyAD/impacket command to weaponize that ACE, plus detection and remediation. DCSync is covered here strictly as a post-compromise technique, not a user entry path.

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。

A high-level conceptual mapping from Active Directory attack techniques to the compliance controls they touch. Kerberoasting relates to authentication and logging, so it brushes ENS op.acc.5 / op.exp.8, NIS2 Art.21(2)(h), DORA RTS Art.9 / Art.21. Use this when a reader wants to understand which regulatory control an AD finding relates to, or to add an orientation note to a technical finding. This is a conceptual, orientative mapping only; it is NOT an auditor-defensible, curated, ID-by-ID control matrix. Covers ENS (op.acc.*, op.exp.*), NIS2 (Directive 2022/2555 Art.21), and DORA (RTS 2024/1774).

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。

Think and work like an expert Manufacturing Engineer. Use when a task calls for Manufacturing Engineer judgment. Reasons from process physics, capability, and cost through Shercliff-Lovatt process selection, ASME Y14.5 GD&T, CAM simulation, and AIAG APQP/PPAP with MSA-gated SPC capability, treating high %GRR masquerading as variation, false Cpk on unstable or short runs, datum-scheme mismatch, and uncontrolled ECN tweaks as first-class failure modes.

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。

Think and work like an expert Glycobiologist. Use when a task calls for Glycobiologist judgment. Reasons from N-/O-glycan biosynthesis, O-GlcNAc cycling (OGT/OGA), LC-MS glycomics, exoglycosidase sequencing, and lectin microarrays; uses GlyTouCan/SNFG/MIRAGE, pGlyco/GlycoWorkbench, and treats PNGase F limits, isomer collapse, and ER-stress high-mannose as first-class failure modes.

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。

Think and work like an expert Electronic Materials Engineer. Use when a task calls for Electronic Materials Engineer judgment. Reasons from band alignment, defect chemistry, and process–structure–property links; correlates Hall, C–V (Dit), XRD/RSM, SIMS, and ALD/MOCVD/sputtering recipes while treating dopant activation vs. chemical dose, high-κ trap charging, and reliability (NBTI/TDDB) as first-class failure modes.

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。

Think and work like an expert Economic Geologist. Use when a task calls for Economic Geologist judgment. Reasons from mineral systems and deposit-type models (porphyry, VMS, orogenic Au, SEDEX, IOCG) through regolith and lithogeochemistry, LA-ICP-MS sulfide fingerprinting, and geophysical vectors to JORC/CIM/NI 43-101 MRE domaining, variography, OK/MIK estimation, and classification while treating transported regolith, dispersion shadows, pXRF false highs, and Inferred-overclaim as first-class failure modes.

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。

Think and work like an expert Catalysis Scientist. Use when a task calls for Catalysis Scientist judgment. Reasons from active-site structure, turnover frequency, selectivity, and the Sabatier principle through CO/H2 chemisorption site-counting, Weisz-Prater and Mears transport checks, Langmuir-Hinshelwood/Mars-van Krevelen kinetics, and operando DRIFTS/XAS while treating diffusion-limited apparent rates, DRIFTS spectator species, mercury-test false positives for leached Pd, sintering, coking, and selectivity collapse at high conversion as first-class failure modes.

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。

Think and work like an expert Behavioral Neuroscientist. Use when a task calls for Behavioral Neuroscientist judgment. Reasons from contingency structure, deprivation state, and latent arousal/motivation variables through Morris water maze, contextual/cued fear conditioning, operant schedules on Med Associates rigs, and DeepLabCut/SLEAP/SimBA tracking validated against ethograms, while treating thigmotaxis-as-anxiety, locomotor confounds of memory deficits, peripheral CNO sedation, and trials-as-n pseudoreplication as first-class failure modes.

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。

Compare evaluated runs in a retort experiment along factor dimensions. Surfaces effects of each factor, aggregates across replicates, and highlights cells that diverge qualitatively — complementing (not replacing) retort's ANOVA 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。

Testing workflow skill for finding high-value test candidates, writing focused tests, generating realistic workloads, reviewing test value, and diagnosing test-suite health. Use for prompts about what to test next, adding or improving tests, reviewing whether a test is worth keeping, low-signal or flaky tests, workload scenarios, or test-suite trust. Do not use for general QA discussion unless the user wants a concrete testing workflow or artifact.

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。

Deep behavioral audit of a Lattice skill — proposes 3 review personas relevant to the skill, runs independent scenario analysis from each persona's perspective, then merges only the high-confidence, practical findings into a severity-ordered gap report with proposed fixes. Structural validation (conventions, cross-references) is skill-validate's job — this skill finds gaps that would realistically surface when someone actually uses the skill: missing scenario handling, ambiguous instructions, silent failure cases, and behavioral inconsistencies. Filters out theoretical edge cases, low-likelihood speculation, and findings owned by other skills. Use after writing or significantly changing any skill, or when the user says 'review this skill', 'deep review', 'does this skill work', 'find gaps in this skill', 'stress test this skill', 'review from different angles', or 'skill review'. Standalone — does not call 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。

Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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 Amazon Polly for production text-to-speech work: selecting Standard, Neural, Long-form, or Generative engines and compatible voices; authoring SSML; creating speech marks for captions, word highlighting, or lip-sync; managing pronunciation lexicons; running synchronous, streaming, or asynchronous S3-backed synthesis; planning quotas, pricing, IAM, privacy, and QA for narration, audiobooks, accessibility audio, avatars, and multilingual media.

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 an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.

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。

Produce short-form video with Pika (pika.art) — its effect-driven tools (Pikaffects, Pikadditions, Pikaswaps, Pikaframes, Pikascenes, Pikatwists) and audio-driven lip sync (Pikaformance), plus text-to-video and image-to-video. Use when a task calls for playful, stylized, meme-able social clips, or for applying generative effects and object swaps to real uploaded footage, and when deciding whether Pika is the right engine versus a higher-fidelity model. Covers model versions, credit/plan economics, the fal.ai API route, prompt construction, iteration/repair, capability limits, and consent/rights rules for uploaded real-person media. Not a general "best video model" chooser.

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。

Provider-independent production workflow for recutting long talking-head, podcast, interview, webinar, panel, lecture, livestream, founder call, or customer-call footage into short clips, highlight reels, trailers, audiograms, captioned vertical videos, and social cutdowns. Use when an agent must ingest source recordings, transcribe and diarize speakers, select quotes without misrepresentation, preserve context, handle consent/rights/disclosures, clean jump cuts and audio, add captions, graphics, lower thirds, b-roll, platform variants, synthetic-media boundaries, review packets, delivery specs, and recut QA.

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。

Provider-independent cinematic trailer production for AI agents creating movie-style trailers, teaser trailers, launch trailers, game trailers, documentary trailers, series promos, and high-impact generated-media previews. Use for trailer strategy, structure, shot planning, voiceover/title-card copy, music and sound-design direction, generated-video prompt translation, edit pacing, platform/runtime choices, delivery handoff, rights/claims caveats, and trailer QA.

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。

Capture high-quality screenshots of any public URL at custom resolutions. Supports desktop and mobile viewports, full-page captures, and specific element targeting. Ideal for visual monitoring, design research, and content archival.

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。

Translate text content between languages with high quality and natural phrasing. Covers document translation, content localization, and multi-language research support for teams working across language barriers.

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。

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk 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。

Search, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.

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。

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research 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。

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

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。

Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.

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。