「cook」の検索結果

Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.

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 for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.

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。

Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text. Use to find warm leads engaging with relevant discussions. No LinkedIn cookies required.

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 differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.

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, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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。

Parse current CNKI search results page into structured paper data (title, authors, journal, date, citations). Use after a search has been performed and you need to extract the 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。

Search CNKI (中国知网) for papers by keyword. Use when the user wants to find academic papers on a topic.

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。

Navigate CNKI search result pages (next/previous/specific page) or change sort order. Use when user wants to see more results or change sorting.

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 for journals/publications on CNKI by name, ISSN, CN, or sponsor. Use when the user wants to find a specific journal or browse publications.

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。

Query journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or 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。

Perform advanced search on CNKI with field filters like author, title, journal, date range, source category (SCI/EI/CSSCI/北大核心). Use when user needs precise filtered search beyond simple keywords.

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。

Browse journal issues, view table of contents, and download original TOC PDF from CNKI. Use when user wants to see papers in a specific journal issue or download the original cover/TOC pages.

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。

Extract full paper details from a CNKI paper page including title, authors, affiliations, abstract, keywords, fund, classification. Use when the user needs detailed information about a specific paper.

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。

Download a paper PDF/CAJ from CNKI. Requires user to be logged in. Use when user wants to download a specific paper.

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。

Export paper from CNKI and push to Zotero, or save as RIS file. Use when user wants to save a paper to Zotero or export citation 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。

Headless Chromium CLI for QA testing and site dogfooding. 100-200ms per command after startup. Persistent state with cookies, tabs, sessions. Snapshot system with element refs. Navigate, interact, verify page state, take screenshots, check responsive layouts, test forms, handle dialogs.

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 the analytics layer with script-backed benchmarks: KPI trees, report templates, MMM, cookieless. "define our KPIs"

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。

Draft GDPR/DSGVO-compliant privacy notices as .docx for any EU/EEA jurisdiction and audience. Use when user asks to create a privacy policy/notice, mentions "Datenschutzerklärung", "politique de confidentialité", "privacy notice", needs Art. 13/14 disclosures, AI Act transparency, cookie policy, or notices for applicants ("Bewerber-Datenschutz"), employees ("Beschäftigten-Datenschutz"), B2B partners, or B2C customers. Covers DE (DSGVO+BDSG+TDDDG), FR (RGPD+LIL+LCEN), AT, IT, ES, NL, BE, IE, UK GDPR. Five notice types: Website/App, Applicant, Employee, Business Partner, B2C Customer.

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。

GDPR Legitimate Interest Assessment (LIA) — Guided assessment for Art. 6(1)(f) GDPR using the EDPB three-step test. Based on EDPB Guidelines 1/2024, EDPB Opinion 28/2024 (AI models), the OSS Case Digest on Legitimate Interest (McIntyre 2026), CNIL AI guidance (June 2025), ICO guidance, and key CJEU case law. Use when: (1) user needs to assess whether legitimate interest is appropriate, (2) user asks about Art. 6(1)(f), "Interessenabwägung", "berechtigtes Interesse", "balancing test", or "LIA", (3) user wants to document a legitimate interest assessment, (4) user mentions the three-step test, (5) user evaluates necessity or proportionality under Art. 6(1)(f), (6) user discusses direct marketing, fraud prevention, IT security, employee monitoring, AI training, web scraping, or credit checks in context of legal basis, (7) user handles a right to object (Art. 21), (8) user asks about children's data, profiling, or cross-border data sharing under legitimate interest, (9) user compares legitimate interest against other legal bases. Do NOT use for: consent assessments (Art. 6(1)(a)), contract performance (Art. 6(1)(b)), DPIA workflows, or pure ePrivacy/cookie compliance questions — though the skill does address the ePrivacy/GDPR overlap where relevant.

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 idle/clicker games including big number handling (mantissa + exponent system), exponential growth curves (cost_growth_factor 1.15x), generator systems (auto-producers), offline progress calculation, prestige systems (reset for permanent multipliers), number formatting (K/M/B suffixes, scientific notation). Use for incremental games, idle games, or cookie clicker derivatives. Trigger keywords: idle_game, big_number, exponential_growth, generator_system, offline_progress, prestige_system, number_formatting.

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 GDPR/CCPA/DPDP privacy consent flows with granular category preferences, consent state persistence, audit logging, and ATT (App Tracking Transparency) integration. Use when user needs privacy consent UI, cookie/tracking consent, or compliance management.

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。

Names 2 other right-fit people at the prospect's own company so an email can ask "is this you or them?" without ever naming the person being emailed. Triggers on "name two other people at the company", "who else works there", "should I be talking to someone else", "colleague name variable", "other prospects line", "wrong person CTA". Outputs other_prospects, a ready-to-drop phrase like "Jeff Barg or Rob Cook".

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。

TypeSafe AI（typesafe.ai）官方文档离线 Wiki，覆盖 Jev System One 模型、Choice/Score/Noul 问题原语、state 结构与置信度、架构 Patterns、Python/JavaScript SDK、HTTP API 参考与 Cookbooks 示例；用于查询 TypeSafe API/SDK 的接入、用法与示例。不用于其他 LLM 厂商 API、千问或百炼系列文档问题。

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。