「info」の検索結果

Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM; Liu 2022 *NAR Genom Bioinform* 4:lqac046), MeTPeak (HMM over sliding windows; Cui 2016 *Bioinformatics* 32:i378), MACS3 / MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment confirmation via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output formats, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union; cross-caller IDR-equivalent), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody-based methods cannot resolve, and orthogonal-validation routes (miCLIP / GLORI / m6A-SAC-seq / m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set as a whole, reconciling differing peak sets across tools, validating MeRIP peaks against orthogonal single-base methods, interpreting peaks at the 5' end where m6Am contamination is possible, or recommending a multi-tool consensus strategy.

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。

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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。

Digital equity and trauma-informed design for marginalized populations. Activate on "accessibility", "offline-first", "trauma-informed", "reentry", "recovery population", "shared device", "unstable phone", "digital equity", "design justice", "low-literacy", "intermittent access". NOT for general UX, marketing optimization, or enterprise SaaS design.

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 hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.

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。

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

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。

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".

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。

When the user requests to create or customize an academic personal website from a GitHub template repository. This skill handles the complete workflow of forking academic template repositories (like academicpages.github.io), extracting structured personal information from memory or provided data, and systematically updating configuration files (_config.yml), navigation menus (_data/navigation.yml), content pages (_pages/about.md), and publication listings (_publications/). It specifically handles academic profiles including personal details, education background, research experience, publications, skills, and contact information. Triggers include requests to 'fork and customize academic homepage', 'build personal academic website', 'create research portfolio', or 'set up GitHub pages with academic template'.

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。

Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks.

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。

Gateway to 400+ bioinformatics skills from bioSkills and ClawBio. Covers genomics, transcriptomics, single-cell, variant calling, pharmacogenomics, metagenomics, structural biology, and more. Fetches domain-specific reference material on demand.

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 this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.

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 WinForms on .NET 8+. High-DPI, dark mode (experimental), DI patterns, modernization.

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 external reconnaissance using Open Source Intelligence (OSINT) techniques to map an organization's external attack surface without directly interacting with target systems. The tester gathers information from public sources including DNS records, certificate transparency logs, search engines, social media, code repositories, and data breach databases to build a comprehensive target profile. Activates for requests involving OSINT reconnaissance, external footprinting, attack surface mapping, or passive information gathering.

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。

Searches the aem.live documentation for information on AEM Edge Delivery Services features. Use this skill when you need more information about a feature, want guidance on how to implement a feature, and using existing tools you have to search the web isn't turning up relevant 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。

NTLM info disclosure is a Medium-severity finding when chained to context — the leak itself is intentional protocol behavior (RFC-compliant NTLMSSP challenge), but on internet-exposed enterprise infrastructure it provides exact reconnaissance for the next stage of an attack. Highest-value targets:

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。

Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.

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。

Performs web searches using DuckDuckGo to retrieve real-time information from the internet. Use when the user needs to search for current events, documentation, tutorials, or any information that requires web search capabilities.

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。

Search the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata — optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

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。

Enriches inbound leads with firmographic and technographic data. Takes minimal lead info and enriches via WebSearch, Apollo, or ZoomInfo to produce a complete lead record with company size, revenue, tech stack, social profiles, and decision-maker details.

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。

AIエージェントの作業ログから、GitHubのPRやissueに添える記録を作ります。関連部分を絞り、機密情報を除いてプレビューと掲載を扱います。

• PRに添えるエージェント作業記録の作成
• ローカルログから関連する依頼と結果を抽出
• 機密情報を除いた記録のプレビュー

DiscordのローカルアーカイブからメッセージやDMを検索し、期間を絞った確認や要約に役立てるスキル。保存データの鮮度と取得範囲も確認します。

• Discordの過去の議論を探したいとき
• チャンネルの一週間を振り返りたいとき
• 保存済みの直近DMを確認したいとき

OpenClawの記憶をまとめたWikiを、元資料に基づいて更新・点検します。手書きのメモを保ちながら、ページの重複や情報の矛盾、出典の不足を確認します。

• 元資料からWikiを更新したいとき
• 蓄積した知識を確認・引用したいとき
• 更新後の矛盾や出典不足の点検

Web検索とページ本文の抽出を使い分け、調査に必要な情報を集めるスキル。期間やサイトを絞った検索、指定URLからの関連部分の取得にも対応します。

• 期間を絞って最近のニュースを調査
• 特定サイトに絞って調べたいとき
• 指定URLの本文を取得したいとき

macOSのBearでノートを作成・読み取り、既存ノートへの追記やタグによる検索を行うスキル。grizzlyを通じて、内容の取得やタグ一覧の確認も扱えます。

• タグ付きの仕事メモを作りたいとき
• 既存ノートに情報を追記したいとき
• タグからノートを探したいとき

コーディングの作業ログを振り返り、ファイルの探しやすさ、自動チェック、指示文、ツール利用などを点検して、次回に向けた環境改善案を重要度順に整理するスキル。

• 時間がかかった開発作業の振り返り
• 繰り返すミスの自動チェックを探したいとき
• エージェント向け指示を整理したいとき