Entry
Appears in 4 awesome lists
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…
Section: Frameworks, Rule Standards & Benchmarks · 🟢 — Large community cybersecurity skill library for AI agents, mapped to MITRE ATT&CK, NIST CSF, MITRE ATLAS, D3FEND, and NIST AI RMF. — note: independent community project, not affiliated with Anthropic. · updated 2026-08-08); Related: sast-skills · Cisco AI Defense – skill-scanner
Section: 10. AI Safety, Alignment & Interpretability · Structured library of 754 cybersecurity skills mapped to five frameworks including MITRE ATT&CK and NIST CSF 2.0, compatible with Claude Code, Cursor, and agentskills.io. Apache 2.0 licensed.
Section: Other · 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…
Section: Development and Testing · 753 cybersecurity skills across 38 domains: cloud security, pentesting, red teaming, DFIR, malware analysis, threat intel, and more (MITRE ATT&CK mapped)
The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.
books: Freely available programming books
Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
🟪 - Another awesome list for mathematics, useful generally but often applicable to programming.
(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).