「sci」の検索結果

Design a drift monitoring system for a production ML model. Use when asked to "monitor this model in production", "detect data drift", or "set up ML monitoring".

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。

Design and implement a feature engineering pipeline for a ML problem. Use when asked to "engineer features for this model", "what features should we build", or "design feature transformations".

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。

Analyze A/B test results — statistical significance, practical significance, and segmentation. Use when asked to "analyze our A/B test", "is this result significant", or "read these experiment results".

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。

Design or audit a feature store — serving, freshness, and sharing across models. Use when asked "do we need a feature store", "design a feature store", or "share features across 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。

Design drift alerts and escalation — thresholds, runbooks, and retrain triggers. Use when asked to "alert on model drift", "when should we retrain", or "write a drift escalation runbook".

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。

Audit existing ML monitoring — find gaps in drift coverage and missing alerts. Use when asked to "audit our ML monitoring", "are our models monitored", or "find drift coverage gaps".

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。

Design an A/B test — power analysis, randomization, and success metrics. Use when asked to "design an A/B test", "how many users do we need", or "run a power 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。

Audit feature engineering code for leakage, quality issues, and pipeline correctness. Use when asked to "audit our feature pipeline", "is there data leakage", or "find feature quality issues".

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。

Audit existing experimentation infrastructure and past experiments for methodology issues. Use when asked to "audit our experiments", "is our experimentation sound", or "review past test methodology".

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。

Build a forecasting model for a time series — demand, revenue, or usage prediction. Use when asked to "forecast demand", "predict next quarter revenue", or "build a time series 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。

Design a data cleaning and transformation pipeline — missing values, outliers, and deduplication. Use when asked to "clean this dataset", "handle missing values", or "deduplicate this 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。

Survey existing forecasting code or models in a codebase — find gaps, stale models, and missing validation. Use when asked "what forecasting models do we have", "audit our forecasts", or "find stale 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。

Validate and benchmark a forecasting model — walk-forward CV, error metrics, baseline comparison. Use when asked "is this forecast any good", "validate a forecasting model", or "backtest the forecast".

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。

Audit existing data cleaning code — find missing validation, silent data loss, and quality gaps. Use when asked to "audit our data cleaning", "are we losing data silently", or "find data quality gaps".

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。

Design a data validation pipeline — schema checks, range validation, and quality metrics. Use when asked to "validate incoming data", "add schema checks", or "define data quality metrics".

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。

Operate the Ryuumonbuchi MCP server for headless Ghidra reverse engineering. Use when driving decompilation, disassembly, patching, type and symbol recovery, memory edits, transactions, and Ghidra project analysis through a Ryuumonbuchi server (stdio or streamable HTTP). Covers the safe read-only-first workflow, session and address discipline, mutation and transaction rules, headless.run, task and error recovery, raw ghidra.call/ghidra.eval/ghidra.script escape hatches, and a sleep/timing-gate bypass recipe.

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。

Design and build websites with film-inspired visual systems, director-driven art direction, storyboard-first layout planning, and cinematic motion. Use when the user asks for a cinematic site, movie-style landing page, director-inspired UI, film-noir, sci-fi, romance, thriller, action, animation, or a movie-like website aesthetic, including requests phrased in Chinese. Do not use for generic web design unless the user explicitly wants a film or director reference.

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。

Expert guide for modern WebGPU and WebGL 3D graphics in the browser using Three.js (WebGPURenderer + TSL), Babylon.js, PlayCanvas, React Three Fiber (R3F), and 3Dviz spatial reasoning — covers object craft across 3 scales, surface continuity at joins, physical interaction envelopes, grounded scientific visualization, KTX2/Meshopt optimization, and 3D visual anti-slop / Panduan ahli grafis 3D web, WebGPU modern, dan penalaran spasial 3Dviz.

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。

Add a new single-objective optimization algorithm to the sci-comp library. Use when the user asks to implement a new optimizer (e.g. gradient descent, BFGS, simulated annealing, differential evolution).

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。

Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.

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。

Extracts paper recommendations from social-media posts and online articles (小红书 / Xiaohongshu / RedNote, 微信公众号 / WeChat Official Accounts, Twitter / X threads, Reddit posts, Bilibili videos, blog posts, newsletters, Jina Reader URLs), identifies the underlying academic papers, locates the authoritative original sources (arXiv, conference proceedings, DOI), and triages relevance to the user's research before any library action. Use when the user forwards a social-media link, screenshot, or article that mentions a paper / method / model, asks to "find the original paper" from a blog or thread, shares a 调研贴 / 论文推荐 / paper recommendation post, or wants to evaluate whether a buzz-worthy paper is worth reading before adding it to Zotero.

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。

Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.

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。

Manages a Zotero academic reference library through both the local API (localhost:23119, read-only) and the Web API (api.zotero.org, read-write), using a structured collection hierarchy (Inbox / Active Projects / Background / Reading Queue / Archive / Meta) plus project, status, priority, and type tags. Handles adding papers with full metadata, deduplication, attaching provenance notes, moving items between collections, updating tags after reading, listing the prioritized reading queue, and setting up the literature scaffold for a new project. Use when the user asks to add / save / file / organize a paper in Zotero, check / list / clean up the reading queue, move papers between collections, tag papers for a project, query their library ("what do I have on X?"), or set up Zotero for a new research project.

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。

Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).

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。