「ultra」の検索結果

Lazy senior dev mode: the smallest change that fully solves the task, and a reply a busy human understands in one read. Use on any coding task (writing, fixing, refactoring, reviewing, choosing dependencies) and when the user says "ponytail", "be lazy", "simplest solution", "yagni", or complains about over-engineering or bloat. Levels: lite, full (default), ultra.

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Ultra-fast RNA-seq transcript/gene quantification via quasi-mapping (no BAM). Builds a k-mer index from transcriptome FASTA, quantifies in minutes. Outputs TPM/count tables (quant.sf) with optional GC- and sequence-bias correction. Integrates with tximeta/tximport for DESeq2/edgeR. Use STAR when a genome-aligned BAM is needed.

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Pipeline for Neuropixels extracellular electrophysiology: probe geometry (ProbeInterface), Kilosort sorting via SpikeInterface, quality metrics, unit curation (ISI, firing rate, SNR), post-sort analysis (PSTH, tuning curves, population decoding). Supports Neuropixels 1.0/2.0/Ultra in rodent/primate experiments.

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Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.

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Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.

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Tạo AI Skill mới từ ý tưởng hoặc quy trình công việc. Kết hợp phỏng vấn thông minh, pattern detection, và quantitative evaluation để tạo skill chất lượng production. Dùng khi user nói "tạo skill", "biến quy trình thành skill", "make a new skill", "tự động hóa công việc này", hoặc mô tả quy trình lặp lại mà họ muốn AI tự làm. Cũng dùng khi user muốn cải thiện, test, hoặc optimize skill hiện có.

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Guides Attention-Gated Networks PyTorch medical imaging workflows for ultrasound classification, attention-gated U-Net segmentation, data layout, CUDA setup, and visualization helpers.

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Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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Ultra-fast speech transcription using iFLYTEK Speed Transcription API. Transcribe audio files (WAV/PCM/MP3) up to 5 hours in ~20 seconds per hour. Supports Chinese, English, and 202+ Chinese dialects with automatic language detection. Use when user asks to transcribe audio files, convert speech to text, or mentions "speed transcription" or "极速转写".

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Think and work like an expert Molecular Pathologist. Use when a task calls for Molecular Pathologist judgment. Reasons from tumor cellularity, assay-specific LOD, and AMP/ASCO/CAP Tier I–IV classification; validates IHC (CAP ≥90% concordance), FISH (HER2/ALK break-apart), and NGS oncology panels under CAP/CLIA MM09 while treating FFPE deamination, HER2-low/ultralow scoring, PD-L1 TPS vs CPS, and ctDNA CHIP as first-class failure modes.

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Think and work like an expert Composites Engineer. Use when a task calls for Composites Engineer judgment. Reasons from CLT/ABD laminate mechanics, Halpin–Tsai micromechanics, and CMH-17/NCAMP allowables through autoclave/OOA/RTM process control, ASTM D30 mechanical qualification, ultrasonic C-scan and CAI damage tolerance while treating fiber waviness, void content, under-cure, and quasi-isotropic strength traps as first-class failure modes.

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Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.

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Detects circadian and ultradian rhythms in time-series omics data using CosinorPy cosinor models, MetaCycle (JTK_CYCLE, ARSER), and RAIN non-parametric tests. Fits cosine models to estimate phase and amplitude, tests rhythmicity significance at pre-specified periods. Use when testing for 24-hour or other known-period oscillations in circadian, feeding-fasting, or light-dark cycle experiments. Not for unknown-period discovery (see temporal-genomics/periodicity-detection).

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Create personalized triathlon, marathon, and ultra-endurance training plans. Use when athletes ask for training plans, workout schedules, race preparation, or coaching advice. Can sync with Strava to analyze training history, or work from manually provided fitness data. Generates periodized plans with sport-specific workouts, zones, and race-day strategies.

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Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.

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Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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Turn a feature request into a minimal, file-level implementation plan before any code.

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Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.

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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", and whenever they complain about over-engineering, bloat, boilerplate, or unnecessary dependencies.

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Write a kolu blog post grounded in the real build history — mine the Claude Code session logs behind a feature for the actual story (for large efforts, fan out over the transcripts with an ultracode workflow), draft it in the author's voice, and wire it into the Astro site. Use when asked to write a blog post or engineering essay about something that was built, especially one that should read as a narrative of what actually happened rather than invented marketing copy.

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Manages the entire session lifecycle: from context loading (PRD, Tech Stack) at startup, to maintaining project memory during active sessions, and creating ultra-compact checkpoints for handoff. / Mengelola siklus hidup sesi: dari pemuatan konteks awal, pemeliharaan memori proyek, hingga pembuatan checkpoint ringkas untuk handoff.

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Manage AI coding CLI accounts with sub-100ms switching. Use when hitting rate limits on Claude Max, GPT Pro, or Gemini Ultra subscriptions and need instant account swapping without browser OAuth.

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A survey of the best-known active scientific controversies in nutrition — saturated fat and cardiovascular disease, dietary cholesterol, low-carb vs low-fat, ultra-processed foods, red meat and cancer, salt and blood pressure, and the replication status of popular single-nutrient claims. Use when a question asks whether a widely reported nutritional claim is settled, or when the department needs to distinguish "contested but plausible" from "settled" from "disproven."

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Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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。