machine-learning
150.4K
continuous-learning
Claude Codeセッションから再利用可能なパターンを自動的に抽出し、将来の使用のために学習済みスキルとして保存します。
affaan-m
data-ai
live ranking
Top Skills by Category
Switch categories to browse the 20 highest-starred skills in each capability cluster.
active category
Machine Learning
1987 स्किल्स
ranking size
20
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parent domain
Data & AI
63 tracked categories
top 3 spotlight
Machine Learning
Training models and neural networks.
stars-based
#1
#2
machine-learning
150.4K
continuous-learning-v2
フックを介してセッションを観察し、信頼度スコアリング付きのアトミックなインスティンクトを作成し、スキル/コマンド/エージェントに進化させるインスティンクトベースの学習システム。
affaan-m
data-ai
#3
machine-learning
150.4K
continuous-learning-v2
훅을 통해 세션을 관찰하고, 신뢰도 점수가 있는 원자적 본능을 생성하며, 이를 스킬/명령어/에이전트로 진화시키는 본능 기반 학습 시스템. v2.1에서는 프로젝트 간 오염을 방지하기 위한 프로젝트 범위 본능이 추가되었습니다.
affaan-m
data-ai
full ranking
Machine Learning
Ranking is based on GitHub stars within the currently selected category.
1987 स्किल्सस्टार
04
machine-learning
continuous-learning-v2
Hook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.
affaan-m
data-ai
05
machine-learning
continuous-learning
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
affaan-m
data-ai
06
machine-learning
continuous-learning-v2
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
affaan-m
data-ai
07
machine-learning
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
affaan-m
data-ai
08
machine-learning
obliteratus
Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
NousResearch
data-ai
09
machine-learning
axolotl
Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
NousResearch
data-ai
10
machine-learning
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
NousResearch
data-ai
11
machine-learning
peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
NousResearch
data-ai
12
machine-learning
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
NousResearch
data-ai
13
machine-learning
fine-tuning-with-trl
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.
NousResearch
data-ai
14
machine-learning
unsloth
Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
NousResearch
data-ai
15
machine-learning
filters-and-postfx
Use this skill when applying visual filters or post-processing effects in Phaser 4. Covers bloom, blur, glow, color matrix, barrel distortion, displacement, custom shaders, and the filter pipeline. Triggers on: filter, post-processing, shader, bloom, blur, glow, color effects.
phaserjs
data-ai
16
machine-learning
geometry-and-math
Use this skill when using Phaser 4 math and geometry utilities. Covers vectors, rectangles, circles, triangles, polygons, random number generation, angles, distance, interpolation, and snapping. Triggers on: Vector2, Rectangle, Circle, math, distance, angle, random, lerp.
phaserjs
data-ai
17
machine-learning
tweens
Use this skill when animating properties over time in Phaser 4. Covers tweens, tween chains, easing functions, stagger, yoyo, repeat, callbacks, number tweens, and the TweenManager. Triggers on: tween, ease, animate, this.tweens.add, tween chain, stagger.
phaserjs
data-ai
18
machine-learning
v4-new-features
Use this skill when learning about new features, game objects, components, and rendering capabilities added in Phaser 4. Covers Filters, RenderNodes, CaptureFrame, Gradient, Noise, SpriteGPULayer, TilemapGPULayer, Lighting component, RenderSteps, and new tint modes. Triggers on: new in v4, Phaser 4 features, RenderNode, SpriteGPULayer, CaptureFrame, Gradient game object, Noise game object, new tint modes. For migrating v3 code to v4, see the v3-to-v4-migration skill instead.
phaserjs
data-ai
19
20
machine-learning
hugging-face-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
patchy631
data-ai