1.6万 件 ・ 関連度順
概要と使いどころ
Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per 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。
Game systems architect for technical architecture, engine design, and infrastructure. Use when the user asks to talk to Cloud Dragonborn or requests the Game Architect.
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 scale-adaptive game architecture with engine systems and networking. Use when the user says "game architecture" or "design architecture"
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。
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
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。
Think and work like an expert Machine Learning Researcher. Use when a task calls for Machine Learning Researcher judgment. Reasons from population risk, double descent, and inductive bias; enforces sacred test sets, hierarchical ablations, nested CV, and HELM/Dynabench-aware benchmarking; reports with NeurIPS and Pineau reproducibility checklists while treating leakage, meta-overfitting, benchmark contamination, Goodhart gaming, and seed variance as first-class failure 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。
Think and work like an expert Database Systems Researcher. Use when a task calls for Database Systems Researcher judgment. Reasons from storage hierarchy, concurrency semantics, query-optimization theory, and declared workload models through TPC-C/H, YCSB, and JOB benchmarks, Jepsen/Elle correctness checkers, and perf/blktrace/fio profiling while treating cardinality-estimation plan regressions, tail-latency spikes under skew, unfair fsync-disabled speedups, and benchmark-trick wins as first-class failure 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。
Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).
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。
Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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 an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.
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。
Provider-independent workflow for AI agents producing documentary-style montages, archive-driven timelines, interview-supported sequences, nonprofit or advocacy shorts, historical explainers, and hybrid generated/archive edits. Use for editorial thesis development, research/source logs, fact-checking, archive rights, synthetic reenactment disclosure, chronology, interview selects, lower thirds, generated b-roll direction, music restraint, captions, sensitive subjects, review escalation, delivery variants, and documentary QA.
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。
Advanced web search, content extraction, and site mapping through Tavily via SandBase. Use when asked for web research, URL content extraction, full article reading, news search, or site structure discovery.
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 videos, analyze channels, extract captions, read comments, and discover trends on YouTube through SandBase. Use when asked for YouTube research, video discovery, channel analysis, content strategy research, comment sentiment, or video transcript extraction.
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 discussions, track communities, analyze posts, and discover trends on Reddit through SandBase. Use when asked for Reddit research, community analysis, trending topics, discussion monitoring, content discovery, or audience insight beyond customer-focused research.
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 videos, analyze channel profiles, track live streams, and research content engagement on WeChat Channels (Video Accounts). Covers video search, channel profiling, live stream monitoring, comment analysis, and content collections.
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 multi-source research with cross-source validation and an offline evidence ledger. Use for fact-checking, comprehensive research, or any question requiring multiple independent perspectives; work with the host agent's search tools and optionally add SandBase Tavily, Exa, Scholar, and Cloudsway 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。
Research companies, professionals, job markets, and industry content on LinkedIn through SandBase. Use when asked for LinkedIn research, company analysis, professional profiling, job market research, or B2B competitive intelligence.
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 academic papers, scholarly articles, and research publications through SandBase. Use when asked for literature review, academic citations, scholarly research, paper discovery, or scientific evidence 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。
Search, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.
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 an intensive, cited analytical report: executive summary, multi-angle findings, contrarian views, open questions, and full sources. Use only when the user needs rigorous synthesis of a complex topic (scientific, technical, policy, or market-analytical) that cannot be answered with a short search, and wants a formal written report, not a recommendation list. Do not use for product picks, top-N lists, quick lookups, or routine "find out about X" tasks. If the request does not clearly need this kind of report, do not use this skill. Do not use for a literature review over published papers. This skill collects evidence from the open web. A request for the literature on a biomedical, clinical, life-science, or other scientific topic — papers, studies, trials, preprints — belongs to firecrawl-research-papers, which queries Firecrawl's paper index (PubMed, bioRxiv, medRxiv, arXiv) instead of searching websites.
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 for the architecture phases of an MBSE workflow in MATLAB, when writing idempotent buildXxx.m scripts that produce a three-layer RFLPV architecture (Functional, Logical, Physical) with interface dictionaries, stereotype profiles, allocation sets, and requirements Implement links. Trigger for defining stereotype properties, functional-to-logical / logical-to-physical allocation, mapping requirements to components via slreq Implement links, or running quantitative roll-up analysis on the architecture. Do NOT trigger for ad-hoc structural edits to an already-built System Composer model (adding one component, rewiring a port) — use `building-simulink-models` with `model_edit` for that. Works alongside the `system-composer` skill for detailed SC API patterns.
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 deep technical research for Aha Loop stories. Use before implementing stories involving unfamiliar libraries or architectural decisions. Triggers on: research this, investigate, explore options, compare alternatives.
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 architectural drawings, floor plans, and building code compliance documents using Vision AI. Extracts building type, occupancy, floor areas, room layouts, dimensions, and code parameters. Use when: reading PDF floor plans, analyzing architectural drawings, extracting building data from images or scanned documents.
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。
Reference for architectural dimensions, proportions, and building codes for AI-generated 3D/2D models. Use when: generating floor plans, creating 3D buildings, validating architectural designs, ensuring correct human-scale proportions in models.
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 automating model architecture design, optimizing hyperparameters, or exploring neural network configurations - covers NAS algorithms, search spaces, Bayesian optimization, and AutoML toolsUse when ", " mentioned.
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。