3,094 件 ・ 関連度順
概要と使いどころ
Think and work like an expert Experimental Physicist. Use when a task calls for Experimental Physicist judgment. Reasons from GUM error budgets, traceable calibration chains, and multiplied signal-chain transfer functions — separating Type A and Type B uncertainty, null runs, and ELN-linked reproducibility before precision or discovery claims.
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Think and work like an expert Electric Machines Engineer. Use when a task calls for Electric Machines Engineer judgment. Reasons from magnetic circuit design, dq-frame machine models, FEM flux paths, and drive efficiency maps while treating saturation, cogging, thermal derating, and inverter harmonics as first-class failure modes.
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Think and work like an expert Electronics Engineer. Use when a task calls for Electronics Engineer judgment. Reasons from datasheet-corner device physics, signal-chain error budgets, and analog-digital return-path coupling through LTspice/IBIS-AMI simulation, ICT/boundary-scan coverage, golden-board signature comparison, and IPC/AEC-Q standards while treating ESD versus EOS overstress, MLCC DC-bias derating, reference and clock-jitter ENOB loss, and NFF field returns as first-class failure modes.
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Think and work like an expert Chromatin Biologist. Use when a task calls for Chromatin Biologist judgment. Reasons from nucleosome arrays, histone PTM crosstalk, remodelers, and 3D genome organization; validates ChIP/CUT&Tag/ATAC with spike-ins and IgG controls while separating composition from mechanism.
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Think and work like an expert Chronobiologist. Use when a task calls for Chronobiologist judgment. Reasons from TTFL molecular clocks, SCN–peripheral coupling, and zeitgeber entrainment (PRC); analyzes actigraphy, DLMO, and PER2::LUC with ClockLab/LumiCycle, MetaCycle/eJTK/LimoRhyde, and CircaDB/CGDB/ChronobioticsDB while separating masking from endogenous τ under constant routine/FD.
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Think and work like an expert AI Researcher. Use when a task calls for AI Researcher judgment. Reasons from data generating processes, inductive biases, and compute-data-algorithm trade-offs through train/val/test discipline, seed sweeps, ablation ladders, and standards like NeurIPS reproducibility checklists, model cards, and lm-eval-harness, while treating data leakage (Kapoor & Narayanan taxonomy), benchmark contamination, spurious correlations, and reward-model overoptimization as first-class failure modes.
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Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.
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Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.
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3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements
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Qualitative stock analysis, management operating plan MOP assessment, key performance indicator KPI identification, catalyst identification and classification, earnings call transcript analysis, qualitative evidence gathering for investment thesis
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Earnings sentiment analysis, analyst estimates vs guidance, earnings surprise history, consensus sentiment, earnings revision trends, analyst rating changes, earnings beat miss track record, guidance accuracy, whisper numbers, pre-announcement sentiment
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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.
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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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Use for Stability AI Stable Audio production work: selecting Stable Audio hosted API or open-weight models, generating or editing music, loops, sound effects, foley, beds, stingers, and sonic-branding audio from text or source audio, planning rights-safe uploads, setting model parameters, polling asynchronous jobs, and reviewing generated audio for media projects.
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Select, integrate, and operate multi-model video-generation gateways — hosted inference aggregators such as fal.ai, Replicate, and WaveSpeed that expose many third-party video (and video-adjacent audio) models behind one account, one API surface, and one bill. Use when deciding whether to route video generation through a gateway versus a direct model-provider API; when comparing gateways on catalog, async/queue job design, webhooks, pricing, schema discovery, and version pinning; and when operating gateway workloads in production — spend controls, retries, cross-gateway failover, content- safety differences, data retention, and commercial-rights passthrough of the underlying model. Not for direct model-provider APIs, local/self-hosted inference, model training/fine-tuning, or image-only gateway use.
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Production guidance for assessing, exporting, packaging, integrating, capturing, validating, and handing off Lottie vector animations. Use for Bodymovin/Lottie JSON, dotLottie archives, renderer and player compatibility, fonts and glyphs, image assets, markers and segments, deterministic video capture, responsive sizing, optimization, accessibility, and cross-platform QA; not for general After Effects animation craft or live-action/video delivery.
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Plan, prompt, execute, troubleshoot, and quality-control Black Forest Labs FLUX image generation and editing across the BFL direct API and licensed local/open-weight deployments. Use for FLUX.2 model selection, text-to-image, single- or multi-reference editing, typography, exact-color work, mask-based erase/inpainting, outpainting, API integration, reproducibility, deployment licensing, and production rights or safety decisions; also use when migrating or maintaining legacy FLUX.1/FLUX1.1 workflows.
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Use NVIDIA Maxine / NVIDIA AI for Media audio effects for speech cleanup and enhancement in live or offline media pipelines, including Background Noise Removal, denoise+dereverb, room echo removal, acoustic echo cancellation, audio super-resolution, Studio Voice, Speaker Focus, and Voice Font. Use when selecting, integrating, prompting around, QAing, or privacy-reviewing NVIDIA AFX SDK or BNR NIM workflows for conferencing, broadcast, podcast, avatar, ad, social, and video post-production audio.
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Production guidance for authoring, integrating, and reviewing GSAP animation in browser-rendered media. Use for deterministic kinetic typography, SVG drawing and morphing, motion paths, FLIP transitions, responsive motion systems, or frame-seekable GSAP timelines inside HTML video composition and React-based renderers; not for ordinary CSS transitions or generic frontend development.
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Provider-independent production workflow for AI agents creating brand launch films, product reveal videos, manifesto films, campaign hero films, website hero videos, investor/customer launch assets, and social cutdowns. Use when planning, scripting, generating, editing, reviewing, or delivering launch films that must align strategy, claims, brand voice, visual direction, product proof, accessibility, compliance, and multi-platform delivery.
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Provider-independent production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation assets. Use when planning, scripting, storyboarding, generating, reviewing, localizing, or QAing animation whose primary purpose is learning.
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Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).
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Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.
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Estimate divergence times using molecular clock models with BEAST2, MCMCTree, and TreePL. Use when dating speciation events, calibrating phylogenies with fossils, choosing between strict and relaxed clock models, or estimating evolutionary rates across lineages.
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。