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Found 201 Skills
Debug experiment code with structured error analysis. Categorize errors, apply targeted fixes with retry logic, and use reflection to prevent recurring issues. Use when experiment code fails or produces incorrect results.
Write structured experiment report documents from ML/research experiment notes, configs, logs, metrics, tables, and figures. Use this skill whenever the user asks to write an experiment report, research update, mentor update, weekly experiment summary, result analysis document, or presentation-ready experiment writeup, especially when the output should explain motivation, setup, algorithms, metrics, results, figures, interpretation, conclusions, limitations, and next steps.
USE FOR image search. Returns images with title, source URL, thumbnail. Supports SafeSearch filter. Up to 200 results.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Use when analyzing research datasets, cleaning tabular data, selecting statistical tests, producing result tables, creating publication figures, or moving notebook logic into reproducible code.
Use when adding, reading, registering, or organizing research sources such as PDFs, arXiv papers, Zotero items, proposals, datasets, reports, archives, web pages, BibTeX, or source metadata.
Robust URL-to-Markdown extraction for OpenClaw workflows. Use this when the user needs to "extract/summarize/convert a webpage to Markdown" (especially for WeChat official accounts at mp.weixin.qq.com) and web_fetch or browser access is blocked or returns messy content. It first uses a low-cost probe via web_fetch, then falls back to the official MinerU API (through the local mineru-extract skill), and returns a traceable result contract with source links.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
Use when choosing, comparing, or preparing for computer science venues, conferences, workshops, journals, tracks, deadlines, reviewer expectations, paper fit, or publication positioning.
Use when the user wants to turn public social videos, transcripts, posts, or creator research into reusable content assets such as LinkedIn posts, X threads, short-form scripts, newsletters, blog outlines, carousels, or content calendars.
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.