Skill UI

This skill simulates an expert software engineer and systems analyst. It is designed for deep, evidence-based understanding of complex codebases. Users can ask "how does X work" or request architectural reviews, ensuring every claim is grounded in actual code paths, data flows, and dependency chains, following a rigorous multi-iteration process.

Allows users to resume an interrupted or paused autoresearch experiment. It automatically loads the full context, including configuration, strategy files, complete results history, and recent code changes. The system then summarizes the current best metrics and patterns, prompting the user to continue with a single iteration, set up an automated loop, or manually review the results.

This tool systematically generates, validates, and ranks concrete, publishable research ideas based on a broad research direction provided by the user. It guides the user through the entire idea discovery pipeline, from initial landscape survey (Phase 1) to validation (novelty checks, experiments) and review, ensuring the generated ideas are novel and relevant for academic publication. It manages complex workflow orchestration and utilizes multiple external tools.

This advanced skill guides users from a broad robotic concept (e.g., 'humanoid grasping') to a highly defined, research-ready hypothesis. It automates a robust four-stage pipeline: literature survey, idea creation, novelty verification, and critical review. The process rigorously enforces academic constraints, prioritizing simulation-first approaches, grounding ideas in established benchmarks (like ManiSkill or Habitat), and ensuring feasibility for real-world robotics development.

This skill generates a structured, section-by-section outline for academic papers. By synthesizing diverse inputs—including research narratives, auto-review conclusions, and experiment results—it guides researchers in structuring their findings into a cohesive draft plan. It also supports optional style references to align the outline's section structure with specific target venues or published works.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

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.

Stress-tests a research paper against the strongest possible rejection argument. It executes a two-stage process: first, generating a single, devastating rejection memo (the 'attack'), and second, having a fresh reviewer defend the paper point-by-point to surface unresolved critical issues. This tool is invaluable for pre-submission quality assurance, especially for theory papers.

This sophisticated orchestrator manages the entire academic research lifecycle, guiding users from initial research concepts through drafting, structured peer review, multiple revision stages, and final integrity checks. It coordinates specialized skills to ensure a reproducible and high-quality workflow, automating the complex transition from academic idea to publication-ready manuscript.

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 generates a structured, non-advisory worksheet for planning trade ideas, basket adjustments, or comparing prediction markets. It helps users methodically review venues, underliers, constraints, and manual execution steps without placing any trades or providing financial advice. Ideal for the research phase of quantitative finance workflows.

This tool orchestrates the end-to-end process of building a brand-new feature. It automates the entire lifecycle, including initial research, planning, Test-Driven Development (TDD) implementation, code review, and gated commit. Use this when adding a capability that does not currently exist in the system.