「research」の検索結果

Expert blueprint for rhythm games including audio synchronization (BPM conductor, latency compensation with AudioServer.get_time_since_last_mix), note highways (scroll speed, timing windows), judgment systems (Perfect/Great/Good/Bad/Miss), scoring with combo multipliers, input processing (lane-based, hold note detection), and chart/beatmap loading. Based on DDR/osu!/Beat Saber research. Trigger keywords: rhythm_game, audio_sync, timing_judgment, note_highway, combo_system, BPM_conductor, latency_compensation.

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 blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.

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Systematic stock screening and investment idea sourcing for A-share markets. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Adapted from the original idea-generation skill for Chinese market conventions and data sources. Triggers on "A股选股", "股票筛选", "寻找机会", "stock screen China", "A-share ideas", "find ideas for Chinese stocks", or "screen for A-share opportunities".

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。

Daily morning research note for A-share markets. Summarizes overnight developments, pre-market sentiment, trade ideas, and key events for the trading day. Adapted from the original morning-note skill for Chinese market conventions. Triggers on "早报", "晨会纪要", "今日策略", "morning note", "morning meeting", or "daily A-share preview".

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。

Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search 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。

Deep competitive analysis for iOS/macOS apps including feature comparison, pricing analysis, strengths/weaknesses, market positioning, and differentiation opportunities. Use when user asks for competitive analysis, competitor research, feature comparison, market positioning, or wants to understand competition in detail.

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End-to-end product development for iOS/macOS apps. Covers market research, competitive analysis, PRD generation, architecture specs, UX design, implementation guides, testing, and App Store release. Use for product planning, validation, or generating specification documents.

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When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' or 'competitive landing pages.' Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. Emphasizes deep research, modular content architecture, and varied section types beyond feature tables.

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When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.

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ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.

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The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.

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mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of g

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The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to

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。

Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute.

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cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

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 creating tailored resumes for job applications - researches company/role, creates optimized templates, conducts branching experience discovery to surface undocumented skills, and generates professional multi-format resumes from user's resume library while maintaining factual integrity

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。

Web search using You.com Search API with high-quality, cited results — runs keyless out of the box; YDC_API_KEY unlocks higher limits and real-time web crawling

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。

Detect suspected manipulation on prediction markets over the past 3 days by cross-referencing price/volume/comment anomalies with multilingual local-press coverage

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。

Track rising, peaking, and fading crypto/tech narratives with quantitative mindshare + velocity signals and explicit positioning calls

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。

Three-mode idea engine - generate collides the week's zeitgeist with what you can ship into scored wedges; validate viability-screens the idea backlog; memo writes evidence-backed startup memos.

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。

Curated trending across GitHub repos and the Hugging Face Hub (models, datasets, spaces) - filtered, clustered, and labeled by momentum with a one-line why-notable per pick.

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。

Business-development radar across your product family - find who's building, forking, integrating, and mentioning your products, ranked into a who-to-talk-to-this-week lead list.

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。

Evolve a skill by generating variations, evaluating them, and updating the best version

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。

Scan the ClawHunter agent bounty marketplace for opportunities that genuinely match this agent's real capabilities (code, security research, writing) and surface only real matches — never a raw unfiltered dump. When a match is real audit-shaped work with a linked GitHub repo, the notification carries a one-tap button to dispatch vuln-scanner at it directly.

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。