14,252 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.
Use when installing Anthropic's Claude Science on a geo-blocked machine (e.g. China) or running it without a Claude account. Downloads the binary from the unblocked downloads.claude.ai CDN, sets up the claude-science-api-bridge local proxy to route requests to ANY OpenAI- or Anthropic-compatible API (Volcengine…
★not rated 29▲
+1 4mo agoA✓ AI review115 tokens
originalMIT
Expert guidance for building real-time streaming systems on Confluent Cloud using Infrastructure-as-Code (Terraform), Apache Flink SQL, and Python producers. Adapts to any streaming use case (IoT, finance, retail, healthcare, logistics) while maintaining production-ready quality.
A configuration-driven process for compiling ONNX or BC machine-learning models for J6 HBDK hardware. It creates a YAML configuration, waits for approval by default, then compiles and checks the output.
Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, Slack workspaces, Google Drive, X (Twitter), and packages with Nia AI. Includes auth bootstrapping, Oracle autonomous research, GitHub live search, Tracer agent, dependency analysis, context sharing, code advisor…
Use when the user wants to analyze their communication style with AI from Claude Code logs — asks for a "prompt warrior" profile, an "AI profile", "wrapped", a psychological portrait of how they prompt, statistics of their sessions/messages, or personalized prompt-engineering recommendations based on their chat…
Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.
MCP server for DeepSeek AI with chat, reasoning, sessions, function calling, and cost tracking. Runs locally from the @arikusi/deepseek-mcp-server npm package. Needs 1 environment variable to run.
A Python software development kit for sending large-model training and inference work to the remote TRIO service. Your local code prepares data and controls the process while the service performs model calculations and saves weights.
MCP server for RAGStack knowledge base - search, chat, upload, and scrape. Runs locally from the ragstack-mcp Python package. Needs 2 environment variables to run.
★not rated 25 2d agoA
tokens not measured
originalApache-2.0
Sample, evaluate, and post-train Inkling and Inkling-Small from Pi. Use when the user mentions Inkling, tml-renderers, TMLv0, thinking effort, Inkling-Small, or Inkling audio/images. Load before writing Inkling training or eval code.
Compress images, long prompts, conversations, and RAG documents locally via Ollama before consuming LLM tokens. Reduces token usage by 80% on images and 85% on text. Use proactively when: the user uploads images, processes long context, works with multiple RAG sources, or when approaching token limits.
A set of instructions for controlling LiteLLM usage and spending. It covers virtual keys, which can identify users or applications, along with budgets, limits, and request labels.
Assess whether a dataset or data domain is ready for AI, analytics, or modeling before committing project budget. Use when triaging table schemas, missingness, label coverage, ownership, freshness, and PII risk to issue a go, fix-first, or stop decision.
MLX Swift LM - Run LLMs and VLMs on Apple Silicon using MLX. Covers local inference, streaming, wired memory coordination, tool calling, LoRA fine-tuning, embeddings, and model porting.
MCP server "dataset-mcp" as configured in boleyn/fastgpt-dataset-mcp. Runs locally from the dataset-mcp Python package.
★not rated 24 1y agoA
tokens not measured
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: