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.
Transforme tes données clients en personas vivants, simulables et évolutifs. Crée, valide, simule et affine des personas synthétiques à partir de transcriptions AI Notetaker et données clients réelles.
Use when user asks to improve, refine, or rewrite a prompt for an AI system, or when framing a request that feels vague, overloaded, or logically unsound. Triggers: "improve my prompt", "rewrite this prompt", "make this prompt better", "help me ask this question properly", "this prompt feels off".
Run a TabPFN classification baseline, generate the first submission, rapidly probe features, then optimize with GBT ensembles, threshold tuning, and calibration. Use after tabpfn-explore has prepared the data and CV folds.
A quiet prompting coach. Scores how you ask, names one thing to sharpen, and remembers your recurring patterns across sessions so the feedback compounds. Works for anyone using AI — not tied to any one domain.
★not rated 5 4mo agoA
tokens not measured
originalMIT
AI-Based Mangrove Forest Monitoring & Conservation — Professional-grade harness for evidence-backed mangrove forest analysis with remote sensing, deforestation detection, health assessment, blue carbon estimation, and restoration planning. Use this skill when users mention mangrove forests, coastal ecosystems…
Autonomous biomedical data analysis using a multi-agent pipeline inspired by BioMedAgent (Nature Biomedical Engineering, 2026). Handles omics, precision medicine, machine learning, statistics, and visualization from natural language instructions.
★not rated 5 7mo agoA
tokens not measured
originalMIT
Rescues messy, typo-ridden, or half-formed prompts so Claude understands you the first time. Confirms its reading with a one-line '✓ understood · …' and keeps reviewable history and stats.
★not rated 5 5mo agoA
tokens not measured
originalMIT
A locally run MCP-based retrieval-augmented generation service. Retrieval-augmented generation, or RAG, lets an AI use information retrieved from a separate knowledge source when generating answers.
Author interactive reactive Python notebooks with marimo. Covers interactive cell execution, reactive UI, SQL, state management, deployment, and export. For interactive notebooks, NOT Cell.run() API.
MCP server for retrieving context from a Qdrant vector database. Runs locally from the mcp-server-qdrant Python package. Needs 4 environment variables to run.
Agent user-scale for user vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage.
Academic paper knowledge-base MCP tool with ingest, retrieval and chat over your document corpus. Runs locally from the scholar-rag-mcp Python package. Needs 4 environment variables to run.
★not rated 5 2mo agoA
tokens not measured
originalMIT
Extract knowledge graphs (subject-relation-object triplets) from document folders, with schema enforcement, resumable chunked extraction, and Cypher generation for Neo4j/Memgraph.
★not rated 5 3mo agoA
tokens not measured
originalMIT
A guide for running Kaggle competitions, an online platform where people build models against shared datasets, and organizing research in a searchable NotebookLM knowledge base.
Plan and build a production customer-support agent — a RAG product-Q&A bot that drafts replies, posts them for human review, and graduates to controlled autonomy. Use when someone wants to build an in-house support agent, a Slack-native or support-platform AI bot, a ticket-answering / customer-Q&A agent, or wants to…
★not rated 5 5mo agoA123 tokens
originalMIT
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: