Skill UI

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

Provides a detailed, multi-platform workflow to monitor long-running computational experiments. It checks the status of jobs across various environments (SSH, vast.ai, Modal), collects screen logs, fetches structured JSON results, and pulls detailed training metrics (loss curves, accuracy) from Weights & Biases (W&B). Essential for MLOps and automated result review.

This skill provides comprehensive commands to manage the entire AI model evaluation lifecycle using Arize. Users can list, retrieve metadata, and export results from experiments. It is ideal for comparing multiple model runs, benchmarking performance against a dataset, and analyzing model accuracy using structured evaluation workflows (CRUD operations).

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.

Use this skill to comprehensively verify all constraints related to a candidate cricket player. By cross-referencing structured data from sources like Wikipedia or Cricinfo, you can confirm details such as birth date, debut match specifics (format, opponent, score), career records, and teams played for. It is crucial for establishing factual accuracy, especially when distinguishing between different formats like ODI, T20I, and Test matches.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.

A comprehensive workflow designed to build deep revenue analytics dashboards using exported Clari data. It calculates key metrics such as forecast accuracy, pipeline coverage ratios, and tracks representative performance changes. Ideal for financial analysts and sales managers needing to assess revenue health and forecast reliability.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

A comprehensive guide outlining rigorous data analysis and reporting standards for geographic manuscripts. It covers spatial statistics, remote sensing accuracy, and qualitative interpretation, emphasizing spatial honesty, quantifying uncertainty, and ensuring results are robust across different scales and units.

A comprehensive guide for authors preparing their accepted ECCV papers for multiple publication venues, including Springer LNCS, ECVA open-access portals, and arXiv. It details critical steps such as managing page budgets, ensuring metadata accuracy, fulfilling rebuttal promises, and adhering to strict publication deadlines.

A comprehensive guide designed to help researchers pre-empt the complex and specific objections raised by referees in law-and-economics journals like JLE. It addresses the unique requirements of single-blind review, focusing on institutional accuracy, methodological robustness, and structuring the manuscript to anticipate critical questions before submission.