Live crypto data for AI agents: nine tools for token search, prices, token safety, token research, wallets, derivatives positioning, the market overview, DeFi yields and prediction markets, each answer citing its sources. Bundles the hosted Hive MCP server with the hive and hive-setup skills.
Plugin hive from marketplace hive in hive-intel/hive-sdk. Commands read from the repo's manifest — yesterday.
hive-setupUse this skill to install, connect, sign in to, verify or remove Hive in an AI client or shell, or to get an API key for a server or script. Run it yourself with /hive-setup.
hiveUse this skill for any live crypto question. Hive answers with nine tools, one per research job, and cites a source for every section. Covers token prices, which token a name or ticker means, token due diligence and contract safety, wallet holdings and activity, derivatives positioning (funding, ope
hiveconnects to: https://mcp.hiveintelligence.xyz/mcp?via=plugin
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Automates browser interactions for web testing, form filling, screenshots, and data extraction
12 skills
Install the "AAS Data Analytics & Business Intelligence" workflow plugin from Agentic Awesome Skills.
16 skills
Databricks skills for the CLI, Apps, Lakebase, Model Serving, Lakeflow Jobs, Spark Declarative Pipelines, Declarative Automation Bundles (DABs), and classic-to-serverless migration.
37 skills · 2 commands · hooks
The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.
1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
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