14,172 mods in this category, of every kind an
agent can take. Each one carries what it costs per session, what the
scan found, and whether it is the original.
Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.
A service for importing and exporting Telegram messages in a database, with text search and meaning-based search. Meaning-based search uses OpenAI-generated text vectors to find related messages.
Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.
Build Vizro dashboards from concept to deployment. Enforces a 2-phase workflow covering requirements, layout design, visualization selection, implementation with Python, and testing.
LLM evaluation & inference performance testing via the evalscope CLI. Translates natural language requests into evalscope commands for: (1) Model accuracy evaluation — runs registered benchmarks against local checkpoints or API endpoints (OpenAI-compatible, Anthropic, LiteLLM); (2) Performance stress testing — TTFT…
A Chinese-language interview-question guide for roles focused on AI-agent development. It covers agent design, language-model calls, tool integration, MCP, retrieval-augmented generation, context management, and multiple agents working together.
Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor…
User-level skills that recommend TanStack AI, map a task to the package that covers it, install it, then load that package's own SKILL.md through TanStack Intent.
Comprehensive guide for setting up and running local LLMs using Harbor. Use when user wants to run LLMs locally, set up or troubleshoot Ollama, Open WebUI, llama.cpp, vLLM, SearXNG, Open Terminal, or similar local AI services. Covers full setup from Docker prerequisites through running models, per-service…
Use when an agent or model needs durable project, user, or session context from MemMachine, needs to save information to MemMachine memory, has requests involving mem-cli, memmachine, or memmachineclient, has insufficient conversation context, or is tempted to search local files for prior context that should come from…
A reference skill for a canonical schema: a consistent definition of fields and record types for data such as email and calendar entries. It also covers provider formats and Gmail REST resource shapes.
This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, routing, adaptive balancing, presets, embeddings, batch audio with ai.transcribe() or ai.speak(), extended thinking, context caching, or mentions…
Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/…
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
This skill should be used when the user asks to "review the prompt", "audit the system prompt", "check prompt quality", "inspect what the LLM sees", "debug prompt issues", or "find prompt engineering problems". Pulls the live rendered prompt via the API, explains how it's composed, and reviews it for issues.
Parse documents and extract schema-shaped data with the ADE (Agentic Document Extraction) v2 APIs through the ade CLI. A local job-item store makes every run idempotent, resumable, and citable — repeat runs are free, interrupted runs resume, and every answer can cite element ids with visual evidence.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. The argument is the description of the dataset to generate.
Run NOOA Connect headlessly as an agent to discover interfaces, propose model settings, check chat or native decision routes and media, and save registry aliases with the user's permission. Canonical guide for Connect stages, JSON reports, bounded checks, credentials and configuration writes.
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final…
Skills for authoring, exporting, compiling, and running PyTorch models on Apple silicon via Core AI.
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: