Topic: codex
8,457 skills in this topic.
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receiving-code-review
Assesses and responds to incoming code review feedback on PRs (reviewer comments, requested changes), especially when suggestions are unclear, technically questionable, or scope-expanding. Use before implementing review suggestions to align on intent and keep changes minimal.
CodingCossack/agent-skills-library 17
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test-driven-development
Red-green-refactor development methodology requiring verified test coverage. Use for feature implementation, bugfixes, refactoring, or any behavior changes where tests must prove correctness.
CodingCossack/agent-skills-library 17
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finishing-a-development-branch
Git branch completion workflow. Use when implementation is complete, tests pass, and a feature branch needs to be integrated via merge, pull request, or cleanup.
CodingCossack/agent-skills-library 17
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using-git-worktrees
Git worktree–based workspace isolation for parallel or non-disruptive development. Use when work must occur without modifying or interfering with the current working tree.
CodingCossack/agent-skills-library 17
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project-management
Manage projects, tasks, and workflows with Codex; use when project planning, task tracking, or team coordination is mentioned.
BA-CalderonMorales/codex-cheat-sheet 14
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pdf-processing
Extract text and tables from PDFs; use when PDFs, forms, or document extraction are mentioned.
BA-CalderonMorales/codex-cheat-sheet 14
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log-review
Inspect error logs quickly; use when log snippets or stack traces are mentioned.
BA-CalderonMorales/codex-cheat-sheet 14
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form-filling
Guide PDF or web form filling; use when structured form completion is requested.
BA-CalderonMorales/codex-cheat-sheet 14
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x-impact-checker
Analyze X (Twitter) posts for viral potential using the actual recommendation algorithm. Use when user wants to: (1) Check if a post will go viral, (2) Optimize a tweet for engagement, (3) Improve post performance. Triggers: "Check if this will go viral", "Make this post buzz", "Will this tweet perform well?", "Optimize my tweet", "How can I make this viral?", "バズるかチェックして", "Xでバズる投稿にして", "伸びるかチェックして", "この投稿を伸ばして", "投稿を改善して", "ツイートを最適化して"
tonkotsuboy/x-impact-checker 23
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rwkv-architecture
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
Orchestra-Research/AI-Research-SKILLs 6,644
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skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
Orchestra-Research/AI-Research-SKILLs 6,644
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modal-serverless-gpu
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Orchestra-Research/AI-Research-SKILLs 6,644
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lambda-labs-gpu-cloud
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
Orchestra-Research/AI-Research-SKILLs 6,644
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nanogpt
Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).
Orchestra-Research/AI-Research-SKILLs 6,644
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outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
Orchestra-Research/AI-Research-SKILLs 6,644
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sentencepiece
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
Orchestra-Research/AI-Research-SKILLs 6,644
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hqq-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.
Orchestra-Research/AI-Research-SKILLs 6,644
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gptq
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.
Orchestra-Research/AI-Research-SKILLs 6,644
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huggingface-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.
Orchestra-Research/AI-Research-SKILLs 6,644
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gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Orchestra-Research/AI-Research-SKILLs 6,644
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distributed-llm-pretraining-torchtitan
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Orchestra-Research/AI-Research-SKILLs 6,644
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instructor
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
Orchestra-Research/AI-Research-SKILLs 6,644
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guidance
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
Orchestra-Research/AI-Research-SKILLs 6,644
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awq-quantization
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
Orchestra-Research/AI-Research-SKILLs 6,644