research-pipeline
End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
154 skills found
End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.
A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.
Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.
Advanced QE reporting, quality dashboards, and predictive analytics for test metrics, code coverage, and deployment readiness to drive data-informed quality decisions.
Guidelines for curating high-quality datasets for LLM post-training (SFT/DPO/RLHF), covering data formats, quality filtering, and collection strategies.
Evidence-first literature collector for automated research pipelines. Scales paper pools to 1200+ with metadata normalization, provenance tracking, and multi-source ingestion.
Generates comprehensive API references, user manuals, and architectural system documentation directly from your codebase and technical specifications.
Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.
Comprehensive Python healthcare AI toolkit for clinical data processing, medical coding translation, and developing deep learning models like RETAIN and Transformers for EHR, physiological signals, and clinical prediction tasks.
Debugging guide for AReaL distributed training issues, including hangs, NCCL errors, OOM, and numerical consistency in FSDP2/TP/CP/EP.
An intelligent gateway that analyzes, scores, and routes user requests across 27 agents, 27 skills, and 14 MCPs to optimize Claude Code execution.
A specialized decision-making agent for complex architectural choices, task planning, and error resolution within the orchestration system.