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概要と使いどころ
Use when designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.
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 preparing ACM CCS appendices and supplementary material, including the ethics considerations appendix, protocol transcripts, formal proofs, measurement tables, and anonymized supporting material under the 12-page body limit, anonymity, and reviewer-discretion constraints for a security 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。
Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.
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 packaging AAMAS multiagent code, environments, opponent and population sets, random seeds, game definitions, and logs as anonymous supplementary evidence or a public post-acceptance release, even without a separate artifact badge, so that game-theory and MARL reviewers can inspect and re-run the interaction claims.
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 preparing AAMAS supplementary material - proofs, extended game and mechanism definitions, extra multiagent experiments, opponent-set details, and the anonymized artifact zip - under the size cap, anonymity, and reviewer-discretion limits, including how to split a game-theory-plus-experiments paper across the 8-page body and the supplement.
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 designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.
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。
Orchestrate Project Osmos for long-running Fabric and OneLake data-engineering outcomes, including task status, messages, follow-ups, cancellation, and deletion. Osmos tasks are remote service tasks, not local background agents. In Fabric portal Copilot, direct interaction is unsupported; point users to GitHub Copilot CLI. Use spark-cli for direct non-Osmos Spark work. Triggers: "Project Osmos", "Project Osmos task status", "check my Osmos task".
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。
Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
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。
Map complete topic clusters for any subject area — hub pages, spoke articles, supporting content, internal linking architecture. Identifies content gaps, priority order, and builds a structured content calendar. Produces topic maps that build genuine topical authority, not random blog posts.
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。
Turn TikTok, Instagram, YouTube, Facebook, X, LinkedIn, Reddit, or Rumble transcripts into timestamped hooks, claims, objections, proof, calls to action, sponsorship signals, and reusable content atoms. Use directly for transcript analysis or as support for creator, competitor, trend, demand, and repurposing work.
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。
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。
Render pixel-accurate Apple Notes (iPhone, light mode) screenshot mockups from a JSON note spec. Outputs HTML + PNG at the iPhone 16/15 Pro native 1180×2556. Supports paragraphs, images, checklists, dividers, autocorrect underline, smart quotes, and an optional iOS keyboard chrome overlay used by the parent video-ad molecule.
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。
Render pixel-accurate ChatGPT mobile (iOS) screen mockups in light mode from a thread JSON. Supports user text bubbles, user image attachments, assistant markdown prose, citation chips, the OpenAI spiral logo, the Apps-SDK GPT chip in the composer, and three header styles (model-tag, plain title, "Get Plus"). Fixed 9:16 viewport. Outputs HTML + PNG.
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。
Render pixel-accurate iMessage screenshot mockups (DM or group) from a thread JSON. Supports minimal, with-keyboard, and full iPhone 15 Pro frame variants. Outputs HTML + PNG.
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 Instagram-ready product reels from any e-commerce product page URL. Scrapes product images, classifies by type, generates AI-animated clips via Higgsfield API, creates text overlays with style presets, and composes a 15-20 second reel with music. Supports model-based and product-only reels.
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。
Creates talking head videos from any source material (docs, changelogs, blog posts, notes, transcripts). Produces multi-scene videos with avatar narration over screenshots/images using HeyGen v2 API. Supports Quick Shot and Full Producer modes.
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。
Book restaurant reservations via browser automation. Use when asked to make dinner reservations, book a table, or find availability at restaurants. Supports OpenTable, Resy, and direct restaurant booking sites.
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 support tickets, Slack channels, NPS scores, and usage patterns to flag accounts showing early churn indicators. Produces a weekly risk scorecard with severity tiers, root cause hypotheses, and suggested save plays per account. Designed for seed/Series A teams where the founder or a single CSM manages all accounts manually.
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。
Aggregate customer feedback from multiple sources — support tickets, NPS comments, Slack messages, G2 reviews, call transcripts, survey responses — into a unified VoC report with theme clustering, sentiment analysis, trend detection, and actionable recommendations for product, marketing, and CS teams. Chains review-site-scraper for public review 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。
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。
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。