MLOps
Operacions d’aprenentatge automàtic
fiftyone-embeddings-visualization
voxel51
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
llm-fine-tuning-guide
qodex-ai
Master fine-tuning of large language models for specific domains and tasks. Covers data preparation, training techniques, optimization strategies, and evaluation methods. Use when adapting models for specialized applications, reducing inference costs, or improving domain-specific performance.
baoyu-image-gen
Quriosity-agent
AI image generation with OpenAI, Google, DashScope, Replicate and fal.ai APIs. Supports text-to-image, reference images, aspect ratios. Sequential by default; parallel generation available on request. Use when user asks to generate, create, or draw images.
native-cli
Quriosity-agent
Run QCut's native TypeScript pipeline CLI for AI content generation, video analysis, transcription, YAML pipelines, ViMax agentic video production, and project management. Also use for editor HTTP automation tasks (state snapshots, events, transactions, and notification bridge control) when user needs deterministic state-aware control.
soul
saadshahd
Use when starting any task, thinking through confidence, verifying work, or asking "what could go wrong". Triggers on every non-trivial request, "how confident", "verify this", "think through".
israeli-pension-advisor
skills-il
Navigate the Israeli pension and savings system including pension funds (keren pensia), manager's insurance (bituach menahalim), training funds (keren hishtalmut), and retirement planning. Use when user asks about Israeli pension, "pensia", "keren hishtalmut", retirement savings, "bituach menahalim", pension contributions, or tax benefits from savings. Covers mandatory pension, voluntary savings, and withdrawal rules. Do NOT provide specific investment recommendations or fund performance comparisons.
litellm
BbgnsurfTech
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
aiconfig-update
launchdarkly
Update, archive, and delete LaunchDarkly AI Configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them.
trigger-agents
triggerdotdev
AI agent patterns with Trigger.dev - orchestration, parallelization, routing, evaluator-optimizer, and human-in-the-loop. Use when building LLM-powered tasks that need parallel workers, approval gates, tool calling, or multi-step agent workflows.
simpo
wundercorp
Reference-free preference alignment, simpler than DPO.
vscode-custom-agents
aktsmm
"VS Code カスタムエージェントの配置ルール・アクセス制御・マルチエージェント設計のベストプラクティス。Triggers on 'custom agent', 'agent not found', 'subagent', 'user-invokable', 'エージェント設計', 'ピッカー'."
slime
wundercorp
RL post-training for LLMs with Megatron and SGLang.
pytorch-fsdp
wundercorp
Fully sharded data-parallel training for large models.
segment-anything-model
wundercorp
"SAM: zero-shot image segmentation via points, boxes, masks."
lambda-labs
wundercorp
On-demand GPU cloud instances for ML training.
actual-setup
wundercorp
Set up Actual Computer (actual.inc) inference in Loki.
stable-diffusion
wundercorp
Text-to-image generation, inpainting, and img2img.
peft
wundercorp
Fine-tune large LLMs with LoRA on limited GPU memory.
huggingface-tokenizers
wundercorp
Fast BPE/WordPiece tokenization and custom vocab training.
weights-and-biases
wundercorp
"W&B: log ML experiments, sweeps, model registry, dashboards."
trl-fine-tuning
wundercorp
"TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF."
nemo-curator
wundercorp
"Curate LLM training data: dedupe, filter, PII redaction."
pytorch-lightning
wundercorp
Clean training loops with built-in distributed support.
torchtitan
wundercorp
Pretrain LLMs at scale with PyTorch 4D parallelism.