「ada」の検索結果

Think and work like an expert Flavor & Fragrance Chemist. Use when a task calls for Flavor & Fragrance Chemist judgment. Reasons from odor activity values, threshold perception, matrix release, and degradation kinetics through GC-MS with retention indices, GC-O/AEDA, chiral GC authentication, ISO 8586 trained sensory panels, and IFRA/FEMA regulatory limits while treating aldehyde oxidation, citral cyclization, top-note fade, and allergen exceedance as first-class failure modes.

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Think and work like an expert Digital Pathology Scientist. Use when a task calls for Digital Pathology Scientist judgment. Reasons from whole-slide pixels, pathologist-ground-truth label levels, and stain/scanner batch effects through QuPath, CLAM/TIAToolbox MIL, Macenko/Vahadane normalization, and MI-CLAIM/TRIPOD+AI standards while treating patch-level leakage, scanner-ID shortcuts, attention-on-necrosis artifacts, and inter-pathologist-kappa ceilings as first-class failure modes.

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Think and work like an expert Deep Learning Scientist. Use when a task calls for Deep Learning Scientist judgment. Reasons from CNN/Transformer inductive bias, Li et al. loss landscapes, grokking/mode connectivity, and Kaplan/Chinchilla scaling (~20 tokens/param); designs ResNet/ViT/DiT/MoE/FlashAttention stacks with FLOPs-matched ablations; trains AdamW+cosine/WSD via Megatron-FSDP/DeepSpeed; evaluates FID/MMLU-Pro/MMLU-CF with lm-eval decontamination and Pineau/NeurIPS reproducibility checklists.

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Think and work like an expert Differential Geometer. Use when a task calls for Differential Geometer judgment. Reasons from connections, curvature, and holonomy; fixes Lee vs Besse/MTW Riemann signs; uses SageManifolds/xAct/Cadabra, Chern–Weil and Atiyah–Singer index theory, and model-space checks (S^n, flat tori) while treating chart artifacts, torsion misuse, and CAS convention drift as first-class failure modes.

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Think and work like an expert Climate Risk Analyst. Use when a task calls for Climate Risk Analyst judgment. Reasons from TCFD/IFRS S2 four-pillar disclosure, NGFS Phase V orderly/disorderly/hot-house scenarios, hazard–exposure–vulnerability and RMS/AIR/CLIMADA cat economics, transition carbon-price and stranded-asset pathways, and PCAF financed emissions while treating exposure geocoding quality, Kotz damage-function retraction, and scenario-not-forecast misuse as first-class failure modes.

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Think and work like an expert Clinical Data Manager. Use when a task calls for Clinical Data Manager judgment. Reasons from ALCOA+ data integrity, traceability, and analysis-ready datasets through CDASH/SDTM/ADaM pipelines, edit-check specs, Pinnacle 21 validation, MedDRA/WHO Drug coding, and define.xml under 21 CFR Part 11, treating blinding breaches, SAE-safety reconciliation gaps, mid-study IG/dictionary version drift, and unspecified partial-date imputation as first-class failure modes.

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Think and work like an expert Bacteriologist. Use when a task calls for Bacteriologist judgment. Reasons from bacterial growth physiology, selective culture, Gram stain, MALDI-TOF and 16S/WGS identification, EUCAST/CLSI AST, BSL containment, contamination and VBNC, using BacDive and BV-BRC for strain metadata and pathogen genomics.

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Think and work like an expert Automotive Engineer. Use when a task calls for Automotive Engineer judgment. Reasons from vehicle-level requirements, regulatory limits, energy/exergy budgets, and distribution-based durability through DFMEA/DVP&R, DoE calibration (INCA, CANape, HIL), Pacejka tire and multibody models, rainflow fatigue, and source-path NVH analysis while treating undocumented build-level and cal-ID deltas, unmet emissions preconditioning and OBD readiness, ADAS sensor misalignment, and cross-domain torque-arbitration conflicts as first-class failure modes.

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Aggregate a retort run's findings.jsonl into a machine-readable assessment.json summary with severity counts, penalty score, requirement coverage, and top findings.

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Evaluate a single retort experiment run. Score the generated code against the task's TASK.md requirements, run its build and tests, compute metrics, and emit a structured evaluation report plus a machine-readable findings file.

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Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.

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Med-adapted recent-quarter review: last print financials, pipeline-milestone awareness, and FDA-event context — reads the quarter through the med lens (milestones moved, runway, regulatory updates) rather than generic financials alone.

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Apply test quality principles when generating or reviewing test code. Enforces Arrange-Act-Assert structure, one behavior per test, assertion quality, test isolation, meaningful naming, and test data management. Use when writing tests, reviewing test code, or when the user mentions 'write tests', 'test this', 'test quality', 'test review', 'improve tests', or 'test structure'. Loaded automatically by the code-generating molecules (code-forge, refactor-safely, bug-fix). This skill governs the craft of writing individual test cases -- not what to test (that is driven by the code being implemented) but how to write tests that are reliable, readable, and maintainable.

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Enforce architectural rules when generating or modifying code, and validate proposed designs before approval (design mode). Defaults to clean architecture; supports any architecture style via the architecture-refiner. Validates layer responsibilities, dependency direction, and structural constraints using the loaded architecture rules. Use when generating code, reviewing architecture, creating new files, or when the user mentions 'architecture', 'layers', 'structure', 'dependency rules', 'hexagonal architecture', 'ports and adapters', 'modular monolith', or 'onion architecture'. Also use when reviewing generated code for structural compliance.

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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.

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Plan and scope production work with Odyssey's real-time interactive video world models (odyssey.ml — Odyssey-1/Odyssey-2/Starchild-1/Agora-1). Use when a request involves generating video that a user steers live with keyboard, text, speech, or controller input (streamed frames that respond in the moment), or when someone must decide between Odyssey-style interactive generation and offline clip generators (Sora/Veo/Kling) or downloadable 3D / game engines. Covers what is verified versus announced, current access routes, latency and coherence limits, feasible-now versus speculative use cases, pricing/access terms, and content/rights considerations. This is a fast-moving research-stage product; the skill teaches honest scoping, not a promise of production reliability.

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Generate persistent, explorable 3D worlds/environments with Marble by World Labs — from text, a single image, multiple images, video, or a 360 panorama, plus coarse-structure blocking with Chisel. Use this skill when a task needs a navigable 3D scene, VR/immersive backdrop, virtual-production environment, previz set, game-blockout, or web/engine-ready Gaussian splat or mesh, and you must choose an input mode, a Marble model, an export format, and a plan/API route, then review world quality. Covers the Marble web app and the World API, editing/expansion, output formats and what each supports downstream, import into Unity/Unreal/Blender/Houdini/web, pricing/credits, capability limits, and rights/licensing. Not for flat text-to-image, text-to-video clips, or real-time on-the-fly world models (Marble outputs are downloadable, static 3D environments).

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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.

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Select, run, and govern NVIDIA Cosmos world-video generation across Cosmos 3 Generator, Predict2.5, Transfer2.5, downloadable checkpoints, self-hosted NIMs, and hosted preview surfaces. Use for text/image/video-to-world, controlled world transfer, multiview or action-conditioned physical-AI video, local checkpoint pinning, hardware planning, inference approval, and output validation; do not use for Cosmos Reason/Embed-only tasks.

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Use this skill when an agent needs production image or video understanding with Amazon Rekognition: labels, objects, scenes, OCR, moderation, image properties, Custom Labels or moderation adapters, stored-video analysis, conditional streaming-video workflows for existing eligible accounts, searchable media libraries, confidence evaluation, S3/IAM/event architecture, privacy, biometric consent, cost control, lifecycle management, and QA.

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Provider-independent FFmpeg finishing workflow for AI agents preparing generated or edited media deliverables. Use when finalizing images, image sequences, video, audio, captions, overlays, social/export variants, checksums, manifests, delivery specs, and QA with ffmpeg/ffprobe, including transcode versus stream-copy decisions, scaling, frame-rate handling, color metadata caution, loudness normalization, subtitle burn-in or sidecars, concat/trim, GIFs, batch variants, and reproducible command reporting.

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Plan, generate, edit, reference, and quality-control still images with Kling AI's hosted IMAGE surfaces or Kuaishou's open Kolors checkpoints. Use for Kling IMAGE 3.0/3.0 Omni/O1/2.1 web, official Kling CLI/MCP or Open Platform integration, and local Kolors text-to-image, image-to-image, IP-Adapter, ControlNet, inpainting, or LoRA work. Do not use for Kling video, avatars, lip-sync, unofficial gateway APIs, or for assuming that current hosted Kling Image models are identical to the 2024 open Kolors weights.

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Provider-independent still-image post-production skill for RAW/rendered intake, nondestructive development, exposure, white balance, tone, color, ICC proofing, masking, repair, compositing, truthful retouching by genre, generative disclosure, sharpening, noise, resampling, variants, metadata, export, QA, rights, and ethical finishing. Use when an agent must plan, perform, direct, or review still photography retouching and finishing for print, web, social, archive, publication, or client delivery; do not use for image-generation APIs, video grading, or campaign-specific production strategy.

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Provider-independent title design and kinetic typography direction for video, animation, ads, explainers, social hooks, brand films, lyric/text videos, title cards, main titles, lower thirds, captions-as-design, and other motion-led text. Use when an agent must plan, prompt, implement, hand off, iterate, or QA typography hierarchy, readability, timing, reveal/hold/exit, motion semantics, line breaks, safe areas, localization, brand constraints, accessibility, flashing/motion safety, or production specs for animated text.

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。