ctf-ai-ml
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.
npx skills add ljagiello/ctf-skills --skill ctf-ai-ml --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CTF AI/ML Quick reference for AI/ML CTF challenges. Each technique has a one-liner here; see supporting files for full details. ## Prerequisites **Python packages (all platforms):** ```bash pip install torch transformers numpy scipy Pillow safetensors scikit-learn ``` **Linux (apt):** ```bash apt install python3-dev ``` **macOS (Homebrew):** ```bash brew install python@3 ``` ## Additional Resour
What does the ctf-ai-ml skill do?
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.
How do I install it?
Run `npx skills add ljagiello/ctf-skills --skill ctf-ai-ml --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ljagiello/ctf-skills, a repository with 2,919 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.