Skill UI

This skill enables AI agents to manage complex, multi-step projects by persisting planning artifacts (task_plan.md, findings.md, progress.md) to the local file system. It ensures that work survives context loss and allows for automatic session recovery, making it ideal for long research tasks or complex coding workflows.

This comprehensive workflow guides users through the end-to-end process of utilizing cloud-based GPU computing resources on Vast.ai. It covers searching for ideal GPU offers based on specs and price, provisioning an instance, running complex training or inference jobs via SSH, collecting model artifacts, and crucially, destroying the instance to prevent unexpected billing. Ideal for deep learning training and research.

A specialized toolkit designed for generating and validating research-only artifacts for Clinical Decision Support (CDS) systems. It supports evidence-profile generation, cohort analysis, survival modeling, biomarker evaluation, and governance documentation. IMPORTANT: This skill is strictly for aggregate research and documentation; it must never be used for patient care, diagnosis, or live clinical operations.

This advanced skill automates the entire lifecycle of cloud infrastructure deployment. Instead of manual documentation review, it researches current best practices across multi-cloud platforms (AWS, GCP, Azure), synthesizes a coherent deployment strategy, and generates production-grade Infrastructure as Code (IaC). It outputs comprehensive artifacts, including Terraform modules, Kubernetes manifests, CI/CD pipelines, monitoring setups, and cost estimates, ensuring secure and optimized deployment.

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.

This tool performs a rigorous Level 2 semantic epistemic review of Agent-Native Research Artifacts (ARAs). It assesses six critical dimensions—including evidence relevance, falsifiability, and methodological rigor—to determine the content's intellectual soundness. It is used after initial structural validation to provide objective, constructive criticism, scoring the work from Strong Accept to Reject.

A powerful utility that transforms structured Markdown and JSON artifacts (like research reports, review summaries, or project plans) into polished, single-file HTML views. It ensures artifacts are highly readable for human consumption, maintaining the source Markdown as the canonical truth while adding necessary presentation layers like dashboards or academic layouts.

Arbor executes an Autonomous Optimization (AO) loop, allowing users to iteratively improve complex artifacts—such as models, agents, or data pipelines—against a measurable objective. It implements Hypothesis Tree Refinement (HTR), structuring research into a persistent tree to track hypotheses, insights, and evidence. This process is crucial for long-horizon optimization tasks (e.g., Kaggle-style benchmarking) and utilizes a held-out test set to prevent overfitting and ensure cumulative, auditable progress.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.

This skill enables the creation and maintenance of highly structured, configurable research wikis. It manages source provenance, compiling raw source materials into organized concept pages, index maps, and derived artifacts. Ideal for systematically documenting complex knowledge bases, such as research notes, product documentation, or domain studies, ensuring full traceability and consistency.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This guide provides a comprehensive project management framework for submitting research to the AAAI conference. It maps out every critical milestone, from initial topic selection and abstract submission, through the two-phase peer review, writing the rebuttal, and preparing the final camera-ready version and public artifacts. It emphasizes backward planning from hard deadlines and maintaining evidence integrity throughout the rigorous academic lifecycle.