A curated list of research-oriented skills that work with OpenAI Codex.
This list is intentionally conservative. Each entry is included only when the upstream source is one of the following:
- an official OpenAI Codex skill
- a repository that explicitly documents Codex support
- a repository that follows the open Agent Skills format Codex can read
Skill names below follow the upstream SKILL.md name or folder slug whenever possible, so install paths and prompt mentions stay close to the source repository.
- What Are Codex Skills?
- Inclusion Rules
- How To Use This List
- Skill List
- Installation and Usage
- License
- References
Codex skills are folder-based instruction bundles that help Codex handle a task more reliably.
A typical skill usually includes:
- a
SKILL.mdfile with trigger rules and workflow guidance - optional scripts, templates, and references
- a stable folder structure that Codex can discover from standard skill locations
In practice, a good skill works like a reusable playbook. Codex loads it when the task matches, follows the instructions, and combines that guidance with the local repository context.
This list keeps entries that satisfy at least one of the following:
- official OpenAI Codex skills
- repositories that explicitly document Codex support
- repositories built around the open Agent Skills format that Codex can consume with little or no adaptation
This list intentionally excludes:
- skills that are exclusive to other platforms, such as Claude Code-only skills
- document workflows that depend on platform-specific built-ins and do not translate cleanly into reusable Codex skills
- repositories whose Codex compatibility is unclear
Treat this repository as a research-workflow index, not a marketplace. The tables help narrow the search space; the upstream SKILL.md is still the source of truth.
If you are new to the list, a task-based pass is usually enough:
- For workflow design, task decomposition, and context management, start with sections 1 and 2.
- For paper writing, presentation work, and formal deliverables, start with sections 3 and 5.
- For literature review and evidence synthesis, start with section 4.
- For experiments, evaluation, fine-tuning, and reproducibility work, start with section 6.
| Skill | What It Does | Link |
|---|---|---|
project-development |
Helps scope LLM projects and design practical research-agent architectures. | Agent-Skills-for-Context-Engineering |
notion-research-documentation |
Researches across Notion and synthesizes cited briefs and reports. | openai/skills |
brainstorming-research-ideas |
Guides structured ideation for high-impact research directions. | AI-Research-SKILLs |
creative-thinking-for-research |
Applies creativity frameworks to generate novel research ideas. | AI-Research-SKILLs |
dspy |
Uses declarative prompt programming and optimizers to build structured research-agent workflows. | AI-Research-SKILLs |
instructor |
Produces Pydantic-validated structured outputs for extraction, labeling, and reliable research automation. | AI-Research-SKILLs |
outlines |
Constrains generation with grammars and finite-state machines for structured outputs and synthetic data workflows. | AI-Research-SKILLs |
| Skill | What It Does | Link |
|---|---|---|
context-fundamentals |
Explains how context works in agent systems. | Agent-Skills-for-Context-Engineering |
context-degradation |
Diagnoses lost-in-the-middle and other context failure modes. | Agent-Skills-for-Context-Engineering |
context-compression |
Compresses long sessions while preserving critical state. | Agent-Skills-for-Context-Engineering |
advanced-evaluation |
Covers LLM-as-a-judge and bias-aware automated evaluation. | Agent-Skills-for-Context-Engineering |
| Skill | What It Does | Link |
|---|---|---|
doc |
Codex-oriented DOCX workflow with rendering checks. | openai/skills |
notion-research-documentation |
Useful for research briefs and structured evidence summaries. | openai/skills |
pdf |
Reads, creates, and reviews PDFs when layout and rendering matter. | openai/skills |
slides |
Creates and edits .pptx slide decks with editable output and layout validation. |
openai/skills |
huggingface-paper-publisher |
Publishes papers on Hugging Face Hub, links them to models or datasets, and manages paper metadata. | huggingface/skills |
ml-paper-writing |
Writes publication-ready ML/AI/Systems papers. | AI-Research-SKILLs |
| Skill | What It Does | Link |
|---|---|---|
notion-research-documentation |
Turns multi-source findings into cited literature notes. | openai/skills |
pdf |
Useful for paper packets, annotated drafts, and layout-sensitive reading workflows. | openai/skills |
transcribe |
Transcribes interviews, meetings, or recorded talks with optional speaker diarization. | openai/skills |
huggingface-papers |
Looks up Hugging Face paper pages and structured paper metadata for summaries or analysis. | huggingface/skills |
llamaindex |
Builds document-ingestion and retrieval pipelines for research corpora. | AI-Research-SKILLs |
faiss |
Provides high-performance dense retrieval for paper collections. | AI-Research-SKILLs |
sentence-transformers |
Generates embeddings for literature search, clustering, and retrieval. | AI-Research-SKILLs |
| Skill | What It Does | Link |
|---|---|---|
gradio |
Builds Gradio demos and interactive research interfaces in Python. | huggingface/skills |
huggingface-trackio |
Tracks training metrics, alerts, and dashboards with Hugging Face Trackio. | huggingface/skills |
slides |
Builds editable slide decks for talks, posters, and result reviews. | openai/skills |
langsmith |
Adds tracing, evaluation, and monitoring to LLM research workflows. | AI-Research-SKILLs |
phoenix |
Open-source observability for tracing, evaluation, and experiment analysis. | AI-Research-SKILLs |
tensorboard |
Visualizes scalars, embeddings, profiles, and training diagnostics. | AI-Research-SKILLs |
stable-diffusion |
Generates figures, concept art, and presentation assets for multimodal research. | AI-Research-SKILLs |
Research workflows now depend on reproducible data handling, evaluation, and experiment tracking, so this category keeps those skills together.
| Skill | What It Does | Link |
|---|---|---|
jupyter-notebook |
Creates clean, reproducible Jupyter notebooks for experiments and tutorials. | openai/skills |
spreadsheet |
Creates, edits, and analyzes spreadsheets with formula-aware workflows and visual checks. | openai/skills |
huggingface-datasets |
Explores Hugging Face datasets through the Dataset Viewer API, including splits, search, filters, and parquet export. | huggingface/skills |
huggingface-community-evals |
Runs local evaluations for Hugging Face Hub models with inspect-ai or lighteval, with sensible backend selection. |
huggingface/skills |
huggingface-llm-trainer |
Trains or fine-tunes language models with TRL on Hugging Face Jobs, including SFT, DPO, GRPO, and reward models. | huggingface/skills |
huggingface-vision-trainer |
Trains or fine-tunes vision models for detection, classification, and segmentation on Hugging Face Jobs. | huggingface/skills |
huggingface-jobs |
Runs data processing, inference, experiments, or training jobs on Hugging Face infrastructure. | huggingface/skills |
peft |
Covers parameter-efficient fine-tuning with LoRA, QLoRA, DoRA, and related adapter methods. | AI-Research-SKILLs |
weights-and-biases |
Tracks experiments, sweeps, artifacts, and model registries. | AI-Research-SKILLs |
mlflow |
Handles experiment tracking, model registry, deployment, and autologging workflows. | AI-Research-SKILLs |
lm-evaluation-harness |
Runs standardized LLM benchmarks such as MMLU, HumanEval, GSM8K, and TruthfulQA. | AI-Research-SKILLs |
bigcode-evaluation-harness |
Benchmarks code models with HumanEval, MBPP, MultiPL-E, and pass@k workflows. |
AI-Research-SKILLs |
vllm |
Serves LLMs with high-throughput inference and OpenAI-compatible endpoints. | AI-Research-SKILLs |
This repository is a curated list, not a unified marketplace. In most cases, you install a skill from its upstream repository and place it in a Codex skill directory.
Current Codex docs describe these standard skill locations:
- repository scope:
.agents/skills/<skill-name>/ - user scope:
~/.agents/skills/<skill-name>/
For official OpenAI skills, the simplest path is usually $skill-installer.
Example 1: install an official curated skill from openai/skills
$skill-installer pdfExample 2: install a third-party skill manually
mkdir -p ~/.agents/skills
cd /tmp
git clone --depth 1 https://github.com/huggingface/skills.git
cp -R skills/skills/huggingface-papers ~/.agents/skills/Example 3: install a research skill from AI-Research-SKILLs
mkdir -p ~/.agents/skills
cd /tmp
git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git
cp -R AI-Research-SKILLs/03-fine-tuning/peft ~/.agents/skills/Some older guides and repos still mention .codex/skills, but the current OpenAI documentation uses .agents/skills as the standard location.
Once the folder is available in a valid Codex skill location, you can invoke it naturally in your prompt.
Examples:
Use the ml-paper-writing skill to turn this repo into a NeurIPS-style draft.Use dspy to prototype an optimizer-backed prompt pipeline for this ablation.Use huggingface-community-evals to smoke-test this checkpoint on MMLU and GSM8K.Use pdf to review these camera-ready pages for layout issues.Use gradio to build a demo for this paper artifact.
- Pick one skill for one clear bottleneck.
- Start with a narrow task instead of a full workflow.
- Read the upstream
SKILL.mdbefore relying on the result. - For academic work, manually check citations, claims, equations, data handling, and benchmark settings.
- If a skill touches remote services or external datasets, verify authentication, quotas, privacy, and licensing before running it at scale.
The content of this repository is released under the MIT License.
Third-party skills linked from this list keep their own licenses. Always check the original repository before installing or redistributing anything.
If you notice a dead link, a naming change, or a clearly better entry for the list, a short issue or PR is enough.
