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Agent Skills

Instruction packs that give your AI agent know-how. Three different kinds — pick the right one below.

✦ Standalone skills4,642

Self-contained. Install one into any project and it works on its own — no other software needed.

🧰 Tool add-ons1,006

Come bundled with a specific tool and only work together with it — they teach your agent how to operate that tool.

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sveltejs · Official

2,091 standalone skills

llama-cpp

✓★ 11

by firecrawl

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

🔥🔥🔥✓ VerifiedFreeAdvanced setup

lambda-labs-gpu-cloud

✓★ 11

by firecrawl

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.

🔥🔥🔥FreeQuick setup

miles-rl-training

✓★ 11

by firecrawl

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

🔥🔥🔥FreeQuick setup

ml-paper-writing

✓★ 11

by firecrawl

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

🔥🔥🔥FreeQuick setup

nemo-curator

✓★ 11

by firecrawl

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

🔥🔥🔥✓ VerifiedFreeNeeds API keys

sentencepiece

✓★ 11

by firecrawl

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.

🔥🔥🔥✓ VerifiedFreeNeeds API keys

segment-anything-model

✓★ 11

by firecrawl

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

🔥🔥🔥FreeQuick setup

transformer-lens-interpretability

✓★ 11

by firecrawl

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

🔥🔥🔥FreeQuick setup

verl-rl-training

✓★ 11

by firecrawl

Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.

🔥🔥🔥FreeQuick setup

blip-2-vision-language

✓★ 11

by firecrawl

Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.

🔥🔥🔥FreeQuick setup

fine-tuning-with-trl

✓★ 11

by firecrawl

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

🔥🔥🔥FreeQuick setup

simpo-training

✓★ 11

by firecrawl

Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

🔥🔥🔥FreeQuick setup