702 件 ・ 関連度順
概要と使いどころ
Turn messy user research notes, interviews, support tickets, surveys, and product context into an evidence-backed decision room: a single HTML artifact with an evidence ledger, theme map, confidence heatmap, opportunity matrix, decision memo, and experiment queue. Use when teams need to move from qualitative signals to product or design decisions without fabricating certainty.
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Senior Designer review: rates each design dimension 0-10, explains what a 10 looks like, and flags AI Slop signals. Useful as a gate before merging UI work.
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。
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.
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Train RuView models — camera-free WiFlow pose (10 sensor signals, no labels), camera-supervised pose (MediaPipe + ESP32 CSI → 92.9% PCK@20, ADR-079), RuVector contrastive embeddings (AETHER, ADR-024), domain generalization (MERIDIAN, ADR-027), local SNN environment adaptation, plus GPU training on GCloud and Hugging Face publishing. Use when building, fine-tuning, evaluating, or shipping a model.
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。
Retrieve probabilistic forecasts and their scorecard context. Use when the user asks what World Monitor is forecasting, how probabilities shifted, or how calibrated the forecasts are.
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 trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
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。
Configure RuVLLM local inference helpers: generate a config, and create and adapt in-process MicroLoRA and SONA instances from quality signals. Use when you need local, air-gapped inference tooling, not for fine-tuning hosted models.
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 auditing content pages for freshness signals, setting up HTTP Last-Modified headers in a web server config, or ensuring Article JSON-LD dateModified is updated when CMS content changes.
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 auditing locally-focused or regionally-targeted pages for geo targeting signals, particularly on sites targeting audiences through Bing or regional search engines. Note that these meta tags have no documented effect on Google's ranking.
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 auditing a blog, news site, or YMYL content site for trust signals, drafting editorial policy page content, or evaluating whether a site meets Google's quality rater trustworthiness criteria.
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 auditing YMYL content pages for trust signals, generating disclaimer copy for medical or financial content, or reviewing affiliate disclosure requirements under FTC or ASA guidelines.
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。
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.
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。
Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
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Master modern Angular state management with Signals, NgRx, and RxJS. Use when setting up global state, managing component stores, choosing between state solutions, or migrating from legacy patterns.
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 users ask for World Cup or 世界杯 AI match predictions, WC assistant probabilities, World Cup news insights, master analysis, recomputing football match win rates with custom correction signals, or trading a related prediction market after reviewing the AI 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。
Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learning GDScript best practices.
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Install and operate Hermes Tweet, a Hermes Agent plugin for X/Twitter research, timeline reading, tweet analysis, and approval-gated private or state-changing operations. Use this skill when installing Hermes Tweet, researching X/Twitter accounts, monitoring launch signals, investigating mentions, auditing giveaways, or preparing gated X operations. Use proactively when a Hermes Agent workflow needs current X/Twitter context. Requires XQUIK_API_KEY for read and action tools.
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 comprehensive rollout plans with preflight checks, step-by-step deployment, verification signals, rollback procedures, and communication plans for infrastructure and application changes
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Stablecoin supply and flow analysis — USDT/USDC mint-burn signals, exchange stablecoin reserves, on-chain stablecoin velocity, and capital rotation indicators for crypto market timing.
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Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.
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。
US ETF fund flow analysis, sector rotation breadth, and style factor flows — track institutional capital movement via ETF creation/redemption, sector breadth signals, and thematic momentum.
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Perpetual futures funding rate analysis and cash-carry basis trading — funding rate regimes, annualized basis signals, carry trade construction, and funding rate arbitrage between exchanges.
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Seasonal/calendar-effect strategy. Generates trading signals from time-based patterns such as month-of-year effects and day-of-week effects. Suitable for any OHLCV 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。
SEC EDGAR filing analysis — 10-K, 10-Q, 8-K, proxy statements, insider Form 4. Extract key financials, risk factors, management discussion, and generate investment signals from US public company filings.
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。