Skill UI

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

This skill facilitates the crucial process of sharing learned intelligent patterns across isolated projects or machines using the InterPlanetary File System (IPFS). It allows users to publish a project's accumulated knowledge base to a decentralized network and later fetch, apply, and merge these patterns into new or existing projects. This is invaluable for bootstrapping new applications with prior knowledge or consolidating learnings across a large monorepo.

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

A comprehensive, multi-stage workflow for enhancing and structuring raw footage using advanced AI tools. It covers the entire pipeline—from initial recording (Screen Studio) through AI planning (Claude/Codex), deterministic cutting (FFmpeg), compositing (Remotion), asset generation (ElevenLabs/fal.ai), and final human polish (Descript/CapCut). Designed for users creating vlogs, tutorials, or polished corporate videos.

A comprehensive, human-guided onboarding skill designed for users new to AI coding agents and Power BI agentic development. It provides structured setup help, deep conceptual explanations, and conversational guidance, mimicking a mentor's session. Modes include concise setup assistance and deep conceptual learning. Use cases: Setting up a new agent environment, understanding core AI concepts (models, prompts, tools), or auditing existing agent configurations.

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.

This guide provides a detailed walkthrough of the AAMAS peer review process. It covers everything from initial submissions and technical evaluation (correctness, significance, rigor) to the rebuttal phase, area-chair synthesis, and final decisions. Use this when planning academic submissions or understanding the rigorous criteria for publishing multi-agent systems research.

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

A comprehensive guide detailing the academic review pipeline for the COLM conference. It covers the entire journey from abstract submission to final decisions, emphasizing unique elements such as LLM use rules, reciprocal reviewing obligations, and strategic methods for maximizing impact during the rebuttal phase. Essential reading for authors planning submissions to specialized, young venues.

A comprehensive guide for ML research groups planning multi-venue conference submissions (e.g., COLM). It details the critical path of academic publishing, emphasizing backward induction from hard deadlines (abstract, paper, rebuttal, decision). The workflow covers compute budget allocation, co-author coordination, claim tracking, and managing the technical pipeline from initial topic selection to final presentation, ensuring reproducibility and timely execution in fast-paced AI research cycles.

A comprehensive project management skill designed for authors preparing papers for the EACL conference. It maps out the entire single-cycle submission process, covering backward planning from the ARR deadline, rebuttal strategies, commitment decisions, and final camera-ready stages. Essential for navigating highly structured academic publication timelines and minimizing risk.

A comprehensive guide for computer vision researchers to determine if their project is a good fit for ECCV, considering scope, technical durability, and academic timing. It weighs the project's contribution against the specific requirements of ECCV versus competing top-tier venues like CVPR and ICCV, ensuring strategic submission planning.