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.
This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
Guide users through obtaining and configuring free API keys for LLM providers. Use when the user wants to set up a free LLM API, get a free API key, connect to a free model provider, configure an OpenAI-compatible endpoint at no cost, or asks about free tiers for AI models. Triggers on "free API key", "free LLM", "set…
A workflow for fine-tuning MiniCPM5-1B and converting the result into a GGUF LoRA adapter. GGUF is a model file format used by tools such as llama.cpp, llama-server, and some desktop apps.
Below is an example TypeScript/Node.js folder structure replicating the Python package's layout. Each subfolder matches the Python counterpart (agent, agents, knowledge, etc.). All "LLM" or "litellm" references are replaced by aisdk usage.
Creating Headless CMS content models via code using the ModelFactory pattern. Use this skill when the developer wants to create, modify, or understand content model definitions, define fields and validators, set up reference fields between models, configure field layouts (including nested layouts inside object or…
Operate Magnitude local inference through its headless CLI, recommend hardware-fit local models, monitor acquisition and loading, and connect an agent harness. Use for Magnitude service, model, catalog, setup, or harness requests.
Optimize any scorable text artifact (prompts, code, configs, agent scaffolds, search solutions) with optimizeanything — one evaluator-driven API across the GEPA optimizer (reflective evolutionary search, the default), AutoResearch, and MetaHarness backends, with a best-of-N baseline for comparisons.
★not rated 7.3k▲
+83 2d agoA
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Use the reconciled source and exporter revisions recorded in VENDOR.md. Select both Blackwell inference entry points and retain their runtime dependency closure. Regenerate the complete src/sources/ tree, then compare every file against a second export. Do not combine independently pinned helper snapshots or modify…
Hosted-gateway LLM router — save 84% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 91 models from OpenAI, Anthropic, Google, DeepSeek, xAI, Z.AI, and more. Free open-weight models included. Also exposes realtime market data…
Guide for developing LLM agents based on PuerTsAgent framework — covers resource directory structure, system-prompt, skills, builtin modules, and best practices.
Skills for MLX-VLM: command-line and server inference, convert/quantize, adding new models, benchmarking, contributing, cached-model listing, and reproducible GitHub issues.
★not rated 5.7k▲
+10 4d agoA
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originalMIT
Install and configure AutoRAG, register its Lite MCP server for external agents, or repair its single search model, approved roots, indexes, datasources, and health checks without exposing credentials. Use when AutoRAG or MCP is missing, init/refresh/health fails, indexes are stale, or the user wants to add folders or…
Secure environment variable management with Varlock. Use when handling secrets, API keys, credentials, or any sensitive configuration. Ensures secrets are never exposed in terminal, logs, or LLM context. Provides guidance around integrating varlock into a project, reading/editing .env.schema and other .env files…
Build and maintain Ruby or Rails applications with the RubyLLM AI framework. Use for chats, agents, tools, MCP servers, structured output, judgments, evaluations, media generation, transcription, OCR, moderation, embeddings, reranking, tracing, Rails integration, and RubyLLM upgrades; not for contributing to the…
Use when fitting equations to data with PySR or SymbolicRegression.jl, when a user wants an interpretable formula, symbolic model, scaling law, or empirical relation discovered from numeric data, or when debugging a PySR search that is slow, stuck, or giving poor equations.
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: