Results for “structure”

Extracts structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications, using the BrowserAct API.

Queries 20+ bioinformatics databases from the command line or Python for gene info, sequences, BLAST/BLAT, protein structures, viral data, and expression metrics.

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems.

Extracts structured article details and full content from Zhihu search results via the BrowserAct API, with keyword and date filtering.

Improves multi-step research reliability with structured protocols for source validation, error recovery, and transparent reasoning.

Guides a structured interview and repository research to clarify feature requirements, classify scope, and produce context artifacts for approval before design.

Produces structured US equity research and trading support: company snapshots, earnings analysis, valuation scenarios, catalysts, risks, and action plans with falsifiable claims.

Extract structured job listing data from Indeed search results, including job titles, companies, salaries, descriptions, benefits, and apply links.

Coordinates multiple research agents to gather information on a topic across GitHub, HuggingFace, papers, competitors, and best practices, then compiles findings into a structured report.

Extracts structured image metadata from Google Images via the BrowserAct API, including thumbnails, titles, source logos, and click-through URLs.

Run structured competitive analysis with feature matrices, SWOT, pricing comparison, review mining, and positioning maps using the inference.sh CLI.

Guides writing Korean government funding program business plans (사업계획서) from an evaluator's perspective, covering structure, scoring emphasis, and writing principles.

Runs a full research-to-ideation pipeline: competitive gap analysis, technology trend research, user feedback analysis, and feature ideation, producing structured reports.

Turns strategy-first Madden NFL YouTube videos into structured, game-ready notes covering meta snapshot, formations, plays, adjustments, and provenance, using transcripts and a curated source list.

Automates question submission to Amazon's Alexa/Rufus AI shopping assistant and collects structured response data, with optional keyword search context for category-specific answers.

Extracts YouTube video transcripts and performs structured 8-dimension competitive analysis to uncover value propositions, audience profiles, and content gaps.

Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page.

Extract structured review data from Google Maps search results using the BrowserAct API, enabling local business analysis, reputation monitoring, and competitive benchmarking.

Extracts structured data from Reddit posts and comments via the BrowserAct API for competitor analysis, brand sentiment tracking, and market research.

Research companies, industries, or competitor sets using web search and seven analytical lenses to produce structured intelligence for downstream product management tasks.

Analyzes global market conditions across US, European, and Asian indices, forex, commodities, and economic indicators to produce structured market reports with risk sentiment assessment.

Optimize content to be cited by AI search systems and LLMs, including AI Overviews, ChatGPT, Perplexity, Claude, and Gemini, through structure, authority, and presence improvements.

Analyze and optimize content for visibility in AI Overviews, ChatGPT, Perplexity, and other AI search systems by evaluating citability, structure, authority, and technical accessibility.

Analyze Amazon competitor listings by ASIN to produce structured competitive intelligence and strategic opportunity points for your own go-to-market.

Optimizes content for AI search systems like AI Overviews, ChatGPT, and Perplexity by analyzing citability, structure, authority, and technical accessibility, and producing a GEO analysis report.

Runs a three-layer verification pipeline on AI-generated output: extracts verifiable claims, finds supporting or contradicting sources via web search, and produces a structured verification report with source links for human review.

Optimizes content for AI search engines like Google AI Overviews, ChatGPT, and Perplexity by analyzing citability, structure, brand mentions, and technical accessibility, then generating a GEO analysis report.

Returns structured prediction-market data for Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow, and UMA oracle via a local API, covering markets, cross-venue search, leaderboards, smart money, wallet analytics, identity clustering, and resolution status.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.

Quantifies market top probability using O'Neil Distribution Days, Minervini leading stock deterioration, and Monty defensive sector rotation, producing a 0-100 composite score with risk zone classification for tactical 2-8 week timing signals.