Skill UI

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

This comprehensive tool utilizes specialized Apify Actors to analyze audience demographics, engagement patterns, and behavior data across major social platforms, including Facebook, Instagram, YouTube, and TikTok. It provides structured data extraction and a summarized interpretation of key audience insights, making it invaluable for marketing, content strategy, and market research.

This comprehensive tool enables automated lead generation by scraping contact and business data from numerous online platforms. It leverages specialized Apify Actors to extract diverse prospects, including business details, creator profiles, and contact information from sources like Google Maps, Instagram, TikTok, and Facebook. It is ideal for market research, lead qualification, and building robust outreach databases.

This powerful tool facilitates comprehensive market research by extracting diverse data points from major consumer and business platforms, including Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor. It is designed to provide structured data for market sizing, analyzing regional demand, tracking pricing trends, and validating consumer behavior, enabling users to identify actionable business opportunities and potential risks.

This skill enables deep analysis of malware families using the Malpedia API. It allows users to retrieve canonical aliases, associated threat actors, and YARA detection rules, and map complex relationships like loader-payload chains. Ideal for threat intelligence research, incident response, and building automated detection signatures.

This skill provides a systematic method to map observed Indicators of Compromise (IOCs) and threat actor TTPs to the global MITRE ATT&CK framework. It guides users in building technique coverage heatmaps using the ATT&CK Navigator, identifying critical detection gaps, and generating actionable threat intelligence reports across Enterprise, Mobile, and ICS domains. Ideal for SOC analysts, threat hunters, and security researchers seeking structured threat analysis.

Spawns N parallel AI subagents that compete on the same task using isolated git worktrees. A coordinator dispatches, monitors, evaluates results by metric or LLM judge, then merges the winning branch. Ideal for parallel optimization, content variations, refactors, and research exploration.

A guide for refining search queries when researching Indian or South Asian actors. It emphasizes the necessity of including domain keywords such as "Bollywood," "Hindi film," or specific Indian award names (e.g., "Filmfare Award") to filter out irrelevant Western search results and drastically improve search precision.

Infers candidate gene regulatory networks from bulk or single-cell expression data using GRNBoost2 and GENIE3 algorithms. Ranks transcription factor-target associations with Dask-compatible parallel execution, sparse expression support, and network stability validation for biological research applications.

A specialized technique for locating the full legal birth name of European actors, particularly French actors, who frequently use stage names. This process involves a two-phase search: first, identifying the stage name using general career clues (films, era, director); second, using specific queries like "birth name," "née," or "nom de naissance" to bypass the stage name and find the legal name. Ideal for deep biographical research.

This skill transforms the agent into a senior strategic consultant, specializing in Philip Kotler’s macro-marketing environment analysis. It systematically evaluates PESTEL factors (Political, Economic, Social, Technological, Environmental, Legal) and synthesizes them into a high-impact SWOT matrix. Use this for market entry research, periodic strategic audits, or validating business assumptions against real-time macro-economic indicators.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.