Skill UI

This guide clarifies the role of computation in theoretical computer science submissions, particularly for venues that prioritize theorems over empirical data. It distinguishes between legitimate forms of evidence—such as certified proof steps, constructive object discoveries, and illustrative examples—and inappropriate performance benchmarking. It helps researchers structure their work to align with theorem-based research, ensuring computational elements enhance the core theoretical contribution.

This skill guides authors on positioning information retrieval (IR) work for the Text REtrieval Conference (TREC). It functions as a venue-selection and manuscript re-framing tool, ensuring the submission aligns with TREC's focus on shared evaluation tracks, test collections, and NIST-style benchmarking. It helps authors structure evidence, understand reviewer expectations, and distinguish the work from general CS conference submissions.

This comprehensive guide helps authors evaluate if their hydrology or water-science manuscript is a suitable fit for the Water Resources Research journal. It mandates that contributions must be quantitative, generalizable advances, not single-basin case studies. The tool emphasizes rigorous benchmarking, adherence to AGU house styles, and compliance with open data/code deposition policies.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.

A sophisticated model-routing advisor for generating, redlining, or rewriting contract language. It determines the optimal LLM based on user constraints (stakes, cost, speed, jurisdiction) and grounds its recommendations in legal benchmarking evidence. It provides a primary model, fallback, and critical warnings.

Provides a rigorous, multi-factor, sector-relative fundamental analysis of publicly listed companies (US, Indian, or global). This skill generates an evidence-backed written report and a sector-relative scorecard, analyzing profitability, returns on capital, balance sheets, and governance. It emphasizes citing primary source documents (e.g., 10-K, annual reports) and benchmarking against appropriate industry peers, avoiding single-metric comparisons.

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This skill provides a standardized framework for evaluating autonomous AI agents. It tracks task completion rates, planning accuracy, tool invocation precision, and operational costs. Suitable for engineering teams managing distributed systems, it enables reproducible benchmarking, regression monitoring, and compliance tracking through structured data models like CSV, SQL, and JSON schemas.

Salary benchmark register for role, department and grade against market and internal minimum, median and maximum. Use for compensation review. Produces CSV, SQL DDL, JSON Schema, and Notion mapping on request.