orchestra-re
94 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
orchestra-researchacademic-plotting
Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
- 導入数
- 2
- GitHub Stars
- 1.4万
- 更新日
- 10月11日
orchestra-researchara-compiler
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchara-rigor-reviewer
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchbrainstorming-research-ideas
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.
- 導入数
- 2
- GitHub Stars
- 1.4万
- 更新日
- 10月11日
orchestra-researchchroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchcreative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchcrewai-multi-agent
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchdeepspeed
Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchevolving-ai-agents
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchexperiment-tracking-swanlab
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.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchgptq
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchgrpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchguidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchhqq-quantization
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchhuggingface-tokenizers
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchimplementing-llms-litgpt
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchlangsmith-observability
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.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchllama-factory
Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchllamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchllava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchlong-context
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchmiles-rl-training
Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchmodel-pruning
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日
orchestra-researchmoe-training
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
- 導入数
- 2
- GitHub Stars
- 1.3万
- 更新日
- 6月16日