machine-learning
150.4K
continuous-learning
Claude Codeใปใใทใงใณใใๅๅฉ็จๅฏ่ฝใชใใฟใผใณใ่ชๅ็ใซๆฝๅบใใๅฐๆฅใฎไฝฟ็จใฎใใใซๅญฆ็ฟๆธใฟในใญใซใจใใฆไฟๅญใใพใใ
affaan-m
data-ai
sorting
์คํ
current ordering strategy
query
all entries
refine the visible subset
machine-learning
150.4K
continuous-learning-v2
ใใใฏใไปใใฆใปใใทใงใณใ่ฆณๅฏใใไฟก้ ผๅบฆในใณใขใชใณใฐไปใใฎใขใใใใฏใชใคใณในใใฃใณใฏใใไฝๆใใในใญใซ/ใณใใณใ/ใจใผใธใงใณใใซ้ฒๅใใใใคใณในใใฃใณใฏใใใผในใฎๅญฆ็ฟใทในใใ ใ
affaan-m
data-ai
machine-learning
150.4K
continuous-learning-v2
ํ ์ ํตํด ์ธ์ ์ ๊ด์ฐฐํ๊ณ , ์ ๋ขฐ๋ ์ ์๊ฐ ์๋ ์์์ ๋ณธ๋ฅ์ ์์ฑํ๋ฉฐ, ์ด๋ฅผ ์คํฌ/๋ช ๋ น์ด/์์ด์ ํธ๋ก ์งํ์ํค๋ ๋ณธ๋ฅ ๊ธฐ๋ฐ ํ์ต ์์คํ . v2.1์์๋ ํ๋ก์ ํธ ๊ฐ ์ค์ผ์ ๋ฐฉ์งํ๊ธฐ ์ํ ํ๋ก์ ํธ ๋ฒ์ ๋ณธ๋ฅ์ด ์ถ๊ฐ๋์์ต๋๋ค.
affaan-m
data-ai
machine-learning
150.4K
continuous-learning-v2
Hook'lar aracฤฑlฤฑฤฤฑyla oturumlarฤฑ gรถzlemleyen, gรผven skorlamasฤฑ ile atomik instinct'ler oluลturan ve bunlarฤฑ skill/command/agent'lara evriltiren instinct tabanlฤฑ รถฤrenme sistemi. v2.1 รงapraz proje kontaminasyonunu รถnlemek iรงin proje kapsamlฤฑ instinct'ler ekler.
affaan-m
data-ai
machine-learning
150.4K
continuous-learning
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
affaan-m
data-ai
machine-learning
150.4K
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
machine-learning
150.4K
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
machine-learning
54.3K
obliteratus
Remove refusal behaviors from open-weight LLMs using OBLITERATUS โ 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
machine-learning
54.3K
grpo-rl-training
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
NousResearch
data-ai
machine-learning
54.3K
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
machine-learning
54.3K
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
NousResearch
data-ai
machine-learning
54.3K
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
machine-learning
39.3K
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
machine-learning
39.3K
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
machine-learning
39.3K
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
machine-learning
39.3K
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
machine-learning
33.4K
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
machine-learning
33.4K
hugging-face-evaluation
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
patchy631
data-ai
machine-learning
33.4K
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
patchy631
data-ai
machine-learning
33.4K
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
wshobson
data-ai
machine-learning
33.4K
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
wshobson
data-ai
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