Skill UI

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.

Adds one optional external Codex critique after the council workflow has produced a synthesis draft. Use for ambiguous, high-consequence decisions where a separate model should attempt to break the consensus. Requires explicit user consent before sending a compact review packet to OpenAI, labels cross-provider vs same-provider honestly, sandboxes the external call, and marks the review absent if the adapter is unavailable.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

skill-doctor grades the performance and quality of an AI agent setup using real, local conversation history (e.g., Claude Code/Codex sessions). It scores skills against defined rubrics for efficiency and code quality, drafts evidence-backed suggestions for improvement, and generates a comprehensive, shareable local report. All processing is strictly local and private.