「unit」の検索結果

Use when handling research ethics, consent, community accountability, and data/transparency for an American Anthropologist (AA) manuscript — informed consent, anonymization, protection of vulnerable interlocutors, heritage and repatriation obligations, and what (and what NOT) to share. At AA this is an ethics-and-accountability skill grounded in the AAA ethics principles and an ethics of care. Plans the ethics and transparency; it does not grant waivers.

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 positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.

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 targeting the Bulletin of the American Meteorological Society (BAMS) or deciding whether a broad-interest atmospheric/oceanic/hydrologic-sciences contribution fits this venue. Encodes the journal's review-and-community positioning, the broad-audience-significance bar, synthesis and field-campaign expectations, AMS house style, official-submission re-check, and misfit heuristics.

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 polishing the prose of an Advanced Materials manuscript for Wiley house style, concision, precise materials nomenclature and units, and readability across materials subfields. Polishes language; does not restructure the advance or manage the length budget.

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 positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.

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 leads from developer forums (Hacker News, Reddit) by detecting intent signals — alternative seeking, competitor pain, scaling challenges, DIY solutions, and migration intent. Scores users by intent strength and cross-platform presence.

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。

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth 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。

Mine comments and replies on social posts, videos, and ads for recurring customer language, questions, objections, desired outcomes, complaints, product requests, purchase signals, and creative opportunities. Use for voice-of-customer research grounded in linked source 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。

Compare competitors' social content across platforms to identify recurring topics, formats, hooks, audience reactions, distribution patterns, and credible content opportunities for a brand.

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。

Research market trends, size, competitors, and growth 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。

Monitor existing customer accounts for upsell and cross-sell signals: team growth on LinkedIn, new job postings, product usage patterns, funding announcements, and public company news. Produces a weekly expansion opportunity list with context and talk tracks. Chains web search, LinkedIn profile monitoring, and job posting detection.

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 website SEO - keywords, content, competitors, and improvement 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。

Search Hacker News stories and comments using the free Algolia API. No Apify token needed. Use when you need to find HN discussions, track mentions, discover Show HN launches, or monitor tech community sentiment.

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。

Portable visual skill pack for the Agent Skills ecosystem (Claude Code, Claude Desktop, Claude Cowork, Claude Design, Goose, Cursor, Codex). Discovers community-published styles + formats via the gooseworks CLI, runs an extract-style workflow on reference images, and exports rendered PNGs via Playwright.

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。

Comprehensive GTM analysis of any target company. Researches what a company is currently doing across all go-to-market dimensions — content/blog, founder LinkedIn activity, SEO/traffic, hiring signals, social/community presence, customer acquisition channels, podcast appearances, review sites, competitive positioning, and partnerships. Produces a structured report identifying what's working, what's missing, and where white space exists for new strategies. Use before designing GTM strategies for a client or prospect.

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。

End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds negative keyword lists, recommends bid strategy, and exports a campaign-ready CSV for Google Ads Editor import.

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。

Monitor Twitter/X, Reddit, LinkedIn, and Hacker News for trending narratives, viral posts, and hot-button topics in your space. Maps trends to ad hook opportunities with timing urgency scores. Tells you what to run ads about right now while the topic is hot.

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。

Discover newsletters in a target niche relevant to your ICP, evaluate audience fit, estimate reach and CPM, and output a ranked shortlist of sponsorship opportunities. Uses web search to find newsletters, then scores each against ICP alignment criteria. Use when a marketing team wants to reach an existing engaged audience for less than the cost of building their own, or when testing a new channel before committing.

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。

Find newsletters relevant to a target audience/industry for sponsorship opportunities. Discovers newsletters through web search, newsletter directories, and industry research. Returns newsletter name, author, estimated audience, topic focus, sponsorship rates (if available), and contact info.

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。

Ports existing Databricks notebooks and jobs to Fabric, covering dbutils to notebookutils, secret scopes to Key Vault, DBFS to OneLake, Unity Catalog mapping to schema-enabled Lakehouses, Jobs and Delta Live Tables to Spark Job Definitions and Pipelines, and Photon to the Native Execution Engine. Use whenever Databricks code has to be converted. For Fabric notebook code that is not being migrated, use spark-cli.

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。

General-purpose X/Twitter research agent. Searches X for real-time perspectives, dev discussions, product feedback, cultural takes, breaking news, and expert opinions. Works like a web research agent but uses X as the source. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "/x-research", (2) user is working on something where recent X discourse would provide useful context (new library releases, API changes, product launches, cultural events, industry drama), (3) user wants to find what devs/experts/community thinks about a topic. NOT for: posting tweets or account management. Note: currently uses recent search (last 7 days). Full-archive search is available on the same pay-per-use X API plan but not yet implemented in this skill.

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。

Collect and assess quality metrics for Simulink models using the metric.Engine API. Use when evaluating model design quality (maintainability, complexity, architecture) or testing completeness (unit testing, SIL, PIL, coverage, pass rates) after edits or test runs. Covers metric discovery, collection, report generation, and diagnostics. Triggers on: "collect metrics", "quality metrics", "metric.Engine", "testing metrics", "maintainability metrics", "SIL metrics", "PIL metrics", "what metrics are available", "assess quality", "coverage metrics", "generate metric report", "diagnose metric errors".

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。

Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting 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。

Write unit and integration tests for Akka.NET actors using modern Akka.Hosting.TestKit patterns. Covers dependency injection, TestProbes, persistence testing, and actor interaction verification. Includes guidance on when to use traditional TestKit.

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。