Results for “structure”

Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.

Assesses NumPy code for cuPyNumeric migration readiness by analyzing source code against an API support manifest and GPU-scaling idioms, producing a structured verdict with per-finding reasoning.

Extract structured video metrics and channel information from YouTube by keyword search using the BrowserAct API, with no CAPTCHA or IP restrictions.

Extracts posts from public Facebook pages or profiles, returning structured data with text, engagement metrics, reaction breakdowns, media thumbnails, and pagination support.

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

Detect kernel-level rootkits in Linux memory dumps using Volatility3 plugins and live system scanners to identify hooked syscalls, hidden modules, and tampered structures.

Query and filter Datadog logs from the shell using the Composio CLI, enabling scoped log searches, pivoting across services and environments, and exporting structured JSON for downstream analysis.

Audits local content archives and builds a reusable, structured content engineering project with content units, topic maps, and assembly drafts for ongoing reuse.

Extract text, tables, and structured data from documents using Azure AI Document Intelligence SDK for .NET, with support for prebuilt and custom models.

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

Extract structured best-selling product data from Amazon, including titles, prices, ratings, reviews, sales volume, and promotions, using the BrowserAct API.

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across strike price and time to expiration to identify cheap, expensive, or anomalous options.

Implements a complete observability stack with Prometheus metrics, Grafana dashboards, Jaeger distributed tracing, and structured logging, including heartbeat-based absence detection for scheduled jobs on Google Cloud.

Structured data + RAG. NL2SQL hybrid patterns (text-to-SQL then execute vs embed rows), table embedding strategies (row-level, schema-level, hybrid), semantic layer integration (Cube, dbt metrics), LangChain SQLDatabaseChain, LlamaIndex PandasQueryEngine, safe SQL execution (read-only, sandboxed), schema-aware retrieval. Full PostgreSQL + pgvector hybrid code. USE WHEN: user mentions "tabular RAG", "NL2SQL", "text to SQL", "RAG on tables", "database RAG", "SQL RAG", "semantic layer", "structured data RAG" DO NOT USE FOR: unstructured doc RAG - use `rag-architecture`; metadata filtering only - use `self-querying-retriever`; KG retrieval - use `graph-rag`

Fetch any X/Twitter post as clean LLM-friendly JSON, converting x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics.

Scrapes tweets from X (Twitter) by search query, user handle, or direct URL, returning structured data including text, author info, engagement metrics, media, and hashtags.

Collects tweets from an X (Twitter) user profile timeline by handle, supporting tweets, replies, or media-only modes, and returns structured data with engagement metrics and pagination.

Discovers key contacts across industries, roles, and social platforms via the BrowserAct API, extracting structured profile data for lead generation and outreach.

结构化业务数据分析：附件读取与口径核验、定向筛选、规则/阈值判定、指标异动归因、漏斗/留存/实验分析、经营复盘及可审计报告。当用户提供 Excel、CSV、PDF、图片或多份业务材料，要求查数、判异常、解释变化、比较方案或形成行动建议时使用。Use for evidence-grounded analysis of structured business data, including filtering, rule checks, reconciliation, diagnostics, experiments, and decision reports.

Extracts structured video and comment data from YouTube using the BrowserAct API, enabling audience insight, content research, and competitive monitoring without CAPTCHA or IP restrictions.

Extract public web data via HasData APIs, including search engine results, structured data from ecommerce, travel, jobs, and local business platforms, with support for web scraping, pre-parsed APIs, and async jobs.

Extracts structured channel-level and video detail data from a YouTube channel via the BrowserAct API, including metrics like views, likes, comments, and subscriber count.

Ingests, normalizes, and enriches structured and unstructured threat intelligence feeds into STIX 2.1 format, evaluating feed quality and deduplicating indicators for distribution to SIEM, firewall, and EDR platforms.

Explains the Financial Services Cloud data model, covering managed-package and Core FSC object structures, household relationships, financial account ownership, and the rollup framework, with SOQL query patterns.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Routes runtime-log requests into an evidence packet to isolate the first actionable blocker, repeated signature, blast radius, or safest next read-only check.

Searches X (Twitter) for tweets matching an advanced query and returns structured data including text, author profile, engagement metrics, media, hashtags, mentions, and pagination cursors.

Extracts structured business data from Google Maps search results using the BrowserAct API. Provide search keywords, language, and country filters to get clean, usable business data.

Debug LLM applications using the Phoenix CLI: fetch traces, analyze errors, structure trace review with open and axial coding, inspect datasets, review experiments, and query the GraphQL API.

Model threat actors, intrusion sets, campaigns, and TTPs as a STIX 2.1 knowledge graph in OpenCTI using the pycti Python client, connectors, and import workers for structured cyber threat intelligence.

Perform systematic exploratory data analysis to understand dataset structure, distributions, relationships, and anomalies before modeling. Use when a dataset is new, its quality is unknown, or the user requests open-ended profiling; use data-analysis instead for a defined hypothesis or decision question.

Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.

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.

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Scrapes posts from a Facebook group given a group URL, sort order, and desired count, returning structured post metadata including post_id, permalink, author, timestamp, body text, media, reactions, comments, and shares.

Scrapes posts from any public Facebook Page timeline, returning structured data including post text, engagement metrics, reaction breakdowns, hashtags, external links, and media type. Supports date range filtering and cursor-based pagination.