Skill UI

This skill performs a deep, cross-model audit of experimental results to ensure integrity before claiming findings. It systematically checks for common fraud patterns, such as generating fake ground truths, misleading score normalization, or citing phantom results. Use it when evaluating reported experimental outcomes to ensure methodological rigor and trustworthiness.

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

This tool collects a comprehensive diagnostic archive for the Hex platform. It gathers crucial evidence, including API connectivity status, project inventory, recent run histories, data connection health, and current rate limit information. Use this bundle when encountering persistent bugs, preparing detailed support tickets, or troubleshooting complex data flow issues.

Provides a comprehensive checklist for deploying Hex data analytics integrations into production. It covers critical operational best practices, including API key management, rate limit handling, error resilience (circuit breakers, exponential backoff), and robust monitoring strategies. This guide ensures that data pipelines are deployed reliably, minimizing downtime and guaranteeing consistent, high-quality data reporting for go-live scenarios.

Conduct comprehensive and systematic literature reviews by searching multiple academic databases (e.g., PubMed, arXiv). This skill guides users through the entire research lifecycle, from defining scope (PICO) and developing search strategies to synthesizing findings. It generates professional markdown and PDF reports, complete with verified citations and mandatory AI-generated scientific schematics for maximum clarity and academic rigor. Ideal for meta-analyses and thesis writing.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.

A comprehensive, structured framework for conducting sophisticated Open Source Intelligence (OSINT) and cryptocurrency forensic investigations. This guide covers operational security (OpSec), target profiling, advanced transaction tracing across multiple blockchain layers (L1/L2), wallet analysis, and identifying linked digital assets. Use this methodology to guide systematic, multi-dimensional intelligence collection campaigns.

This guide provides comprehensive technical instructions for optimizing Palantir Foundry costs. Learn to use incremental transforms to drastically reduce compute rebuilds, right-size Spark profiles based on data volume, and migrate from expensive API polling to efficient, event-driven webhooks, ensuring both cost efficiency and system stability.

Implement robust data governance and security measures within Palantir Foundry. This skill guides users through PII redaction, applying data markings (e.g., CONFIDENTIAL, RESTRICTED), ensuring GDPR/CCPA compliance, and establishing automated data retention policies. It provides methods for column-level security and auditable deletion workflows for sensitive information.

This guide provides a comprehensive reference architecture for building production-ready, enterprise-grade applications on Palantir Foundry. It covers the entire data lifecycle, detailing best practices for the standard data pipeline (Raw > Clean > Model), Ontology design, external API integration patterns, and robust multi-layered security models. Use it when designing, planning, or optimizing complex data infrastructure within Foundry.

Performs rigorous, zero-context verification of all quantitative claims, comparisons, and scope statements reported in a research paper against the raw evidence files. It is designed to detect confirmation bias, such as rounding errors, data cherry-picking, or misreported deltas, ensuring strict fidelity between published claims and underlying raw results. Ideal for pre-submission quality control.

This advanced tool generates publication-ready academic figures and illustrations. It employs a sophisticated, multi-stage iterative workflow involving an AI Planner, Layout Optimizer, Style Verifier, and a dedicated native image generation bridge. The process includes strict visual review and self-correction loops (CVPR/NeurIPS standards) to ensure the highest level of academic polish.