47,133 repositories indexed
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Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Open standard for machine learning interoperability
A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
A functional standard library for Python.
HarnessRouter Community Edition: the self-hosted, Apache-2.0 edition of the unified interface for agent harnesses. Run Codex, Claude Code, Hermes, PI, DSH, and more through one API, with sessions, streaming, files, cancellation, and failure handling. Implements the Unified Harness Protocol (UHP), an open standard. Your keys, your infrastructure.
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Useful extensions to the standard Python datetime features
Resources on programming practices for safety-critical software.
㊙️ Create standard barcodes with Python. No external dependencies. 100% Organic Python.
Simulate Functional Mock-up Units (FMUs) in Python
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
Enhance the standard unittest package with features for testing asyncio libraries
An opinionated, curated list of tools for enforcing software development standards: one recommended tool per job, with limits.
Schema and generated objects for biolink data model and upper ontology
⚡A curated list of awesome resources related to the Ⱥlgorand Blockchain ⛓
A reusable agent skill and Markdown standard for consistent, current project context.
Molecule Validation and Standardization
Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
trycast parses JSON-like values whose shape is defined by TypedDicts and other standard Python type hints.
Open standard for Provenance & Intent Contracts (PIC) in AI agents. Verify intent, provenance, and evidence before high-impact tool calls.