Skill UI

Guides researchers through ten structured ideation frameworks to move from vague curiosity to actionable, defensible research proposals, supporting exploration of new problem spaces, pivoting projects, and surfacing high-impact angles.

This agent enhances AI's auto-memory by analyzing raw learned patterns (from MEMORY.md). It allows users to promote proven knowledge into structured project rules (CLAUDE.md) or extract them into reusable skills. Use it to audit memory health, enforce best practices, and graduate temporary notes into permanent, actionable standards.

This advanced workflow orchestrates the entire research idea lifecycle. Starting from a broad research direction, it systematically executes literature mapping, creative brainstorming, novelty verification, and proposal refinement. The final output is a validated IDEA_REPORT, a detailed proposal, and a concrete experiment plan, transforming preliminary concepts into actionable, structured research projects. Ideal for users needing a full, end-to-end research guidance system.

Acontext serves as a robust memory layer for production-grade AI agents. It provides persistent sessions, disk-based storage, and sophisticated learning spaces that distill complex conversations into reusable, structured skills. Users can manage the entire system—from project setup and login to skill uploading—via a powerful Command Line Interface (CLI) and API, significantly enhancing agents' contextual understanding and long-term knowledge retention.

This guide helps determine the optimal deployment strategy for Qdrant, a vector database. It compares four main options—Local Mode, Self-Hosted (Docker), Qdrant Cloud (Zero-Ops), and Qdrant EDGE—based on project requirements such as control needs, operational overhead, data residency, and required latency. Use it to decide whether to prototype, manage full infrastructure, or prioritize minimal latency.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This skill allows users to generate complete songs, including vocals or pure instrumental tracks, using advanced AI APIs. By providing a structured JSON specification, you can define the desired style, mood, scene, and optional lyrics. Ideal for creating background music, theme songs, jingles, or full compositions for creative projects.

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 project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

This skill manages the end-to-end project lifecycle for academic conference submissions, specifically tailored for major venues like ICLR. It provides a structured timeline, defining critical milestones from initial topic selection and abstract submission through paper deadlines, public discussion, and final camera-ready versions. Use it to sequence research milestones, manage evidence paths, and ensure compliance with conference rules like anonymity audits and strict deadlines.

This skill manages complex, stateful proof projects as a local-first pipeline. It guides the user through drafting, rigorous verification, and source bookkeeping. It systematically handles proof continuation across runs, maintaining a full audit trail. When local attempts stall, it orchestrates handoffs to external AI models (like GPT Pro) or performs optional deep audits (DeepSeek), ensuring a structured, traceable, and verifiable output suitable for academic or technical submission.