Skill UI

Pilot Protocol is an open-source overlay network providing AI agents with first-class network citizenship. It offers permanent virtual addresses, encrypted P2P communication, NAT traversal, and a structured app store. Use this skill when agents require stable networking across cloud changes, need direct, secure messaging without shared accounts, or must integrate structured external data services (e.g., live crypto prices) via local, typed capabilities.

This skill intercepts the AI response flow to allow users to explicitly control the desired depth, length, and token usage of the generated answer before the model begins writing. By analyzing the prompt's complexity and the user's intent (e.g., 'short version', 'detailed answer'), it estimates the appropriate token range and prompts the user to select a granularity level (25%, 50%, 75%, or 100%).

A powerful skill for generating brand-new images or performing professional edits on existing photos using the OpenAI GPT Image API. Users can provide detailed prompts, specify desired aspect ratios (e.g., 1024x1536), select background types, and control output quality. Ideal for creative projects, logo design, concept art, and advanced photo manipulation.

A comprehensive guide detailing the submission rules and best practices for supplementary materials, including videos, appendices, code, and data, for top-tier robotics conferences. It outlines distinct submission channels, strict technical requirements (e.g., 250MB video cap, anonymity), and the necessary transition steps from the review phase to the final camera-ready publication format.

Comprehensive guidelines for structuring and submitting reproducible AI/ML research papers for top-tier conferences. It details two modes: Mode 1 for theoretical/KR work (requiring full, explicit proofs in the supplement), and Mode 2 for empirical/ML work (requiring fixed seeds, cached outputs, and rigorous provenance pinning for datasets, models, and environments). The guide also covers best practices for maintaining double-blind anonymity and properly dividing content between the main body and the supplementary material.

This guide models the comprehensive two-round, journal-style peer review process used at major computer vision conferences like WACV. It details the distinct stages: Round 1 outcomes (Accept, Revise and Resubmit, Reject), the optional R1 rebuttal phase, and the final R2 decision. It emphasizes that the most critical phase for authors is executing high-quality revisions during the Revise-and-Resubmit stage, rather than solely relying on rebuttals.

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.