Skill UI

Gradient Clipping Helper automates ML training work by guiding gradient clipping workflows, generating production-ready code and configs, and validating outputs for PyTorch, TensorFlow, and related pipelines.

A comprehensive guide to diagnose and resolve common errors in Lindy AI agent workflows. Covers troubleshooting issues related to trigger failures (webhooks, email, schedule), action execution errors (Slack, Gmail, HTTP requests, code), and advanced agent step logic problems, ensuring reliable automation.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

Automate Replicate AI workflows via the Composio MCP integration to inspect model schemas, run predictions (sync or async), upload input files, list versions, and manage prediction history and related assets.

Search over 2500 curated open-source repositories related to ChatGPT, LLMs, RAG, agents, and AI development. This skill helps users find tools, libraries, and frameworks by filtering categories, languages, or keywords. It relies on a bundled database rather than live web searches.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

A comprehensive guide for positioning AI/ML manuscripts for top-tier general AI conferences like AAAI. It helps authors refine their scope, frame their contribution, provide necessary evidence (e.g., ablations, baselines), and strategically compare their work against related research and similar venues (ICML, IJCAI). Ideal for pre-submission manuscript refinement.

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

This guide helps authors audit the novelty and eligibility of multiagent system research for submission to AAMAS. It provides comprehensive advice on positioning the work against literature spanning multiple top-tier venues (e.g., NeurIPS, ICML, AAAI, EC, JAAMAS), integrating concepts from multiagent RL, game theory, and social choice. It ensures submissions are positioned accurately compared to the broader academic community, maximizing impact and minimizing rejection risk.

A comprehensive guide for authors targeting the British Machine Vision Conference (BMVC). This skill helps assess whether a computer vision manuscript fits the venue, providing expert advice on framing, necessary evidence gaps, structural improvements, and differentiating the work from related CV conferences (e.g., CVPR, ICCV). It ensures the contribution is clear and highly specialized for specialist reviewers.

A detailed guide for authors structuring the 'Related Work' section of theoretical machine learning papers. It shifts focus from qualitative prose to quantitative comparison, instructing users to build explicit tables comparing prior bounds versus new bounds, and systematically covering diverse literature lanes like complexity theory, statistics, and open problems to rigorously establish novelty.

A comprehensive guide for positioning continual learning and lifelong agent research for the Conference on Lifelong Learning Agents (CoLLAs). This tool helps authors strategically re-frame their work, assess its fit, and optimize the narrative to meet the specific evidence bar of top CS venues focusing on nonstationary learning, adaptation, and memory. It covers scope calibration, required evidence types, and comparison with related ML conferences.