3. Data, Science & AI

Monitors real-time signal quality metrics (RSSI, SNR, S-meter) from WaveCap-SDR devices for signal strength checks and debugging.

Optimizes Automatic Gain Control (AGC) parameters for WaveCap-SDR to resolve audio issues in FM/AM/SSB radio modes.

Adjusts SDR radio parameters including frequency, gain, and filters for optimal reception in WaveCap-SDR software.

Enables adding or updating vectors in cloud-based vector storage for AI applications on the Agentuity platform.

Deletes vectors by key in Agentuity cloud platform, requiring authentication for AI data management operations.

Generates Mermaid dependency graphs and critical path analysis for GOAL/SubPlan workflows based on business logic and dependencies.

Executes SQL queries on a database for Agentuity cloud platform operations, requiring authentication.

Performs common image operations including transformation, enhancement, and analysis for data processing and AI applications.

Analyzes CSV and JSON data files to generate statistical insights, summaries, and data-driven conclusions.

Command-line tool for semantic vector similarity searches on Agentuity cloud platform, requiring authentication for secure AI data retrieval.

Provides a greedy algorithm for selecting diverse, coverage-balanced subsets from datasets, applicable to test case selection, data sampling, and portfolio optimization.

Extracts structured data from web pages using CSS selectors and XPath for data processing and analysis.

Expert handling of .faf (Foundational AI-context Format) files for persistent project context, including AI-readiness scoring and MCP server configuration.

Calculates AI-readiness score (0-100%) for projects using project.faf, showing Podium tiers, strengths, gaps, and improvement roadmap.

Analyzes correlations between content attributes and performance metrics from GA4, GSC, and SEO rankings to identify optimization drivers.

Provides common patterns for extracting and combining analytics data from GA4, GSC, and SE Ranking with API handling, rate limiting, and caching.

Runs multiple AI models in parallel for code review and consensus analysis, enforcing performance statistics tracking and mandatory checklists.

Guides users in enhancing project AI-readiness by identifying gaps and suggesting improvements to achieve higher Podium tiers (Bronze to Trophy).

Analyzes GA4, GSC, and SEO ranking data to provide content optimization benchmarks, status indicators, and actionable insights.

Intercepts bulk file reads to replace them with semantic search, preventing token waste during AI data processing.

Provides technical guidelines for transferring AI models trained in simulation to real-world robotic hardware, addressing domain gap challenges in Physical AI.

Automates migration of legacy FAF project files to the latest IANA-registered version (3.0.0+), ensuring compliance and preserving content.

Provides integration patterns for combining NVIDIA Isaac Sim simulation with Isaac ROS to develop Physical AI applications in robotics.

Provides integration patterns for Vision-Language-Action models in cognitive robotics using ROS 2 framework.