sci-geospatial

Expert-thinking subagents for molecular & cell biology: genetics, genomics, omics, biochemistry, biophysics, microbiology, immunology, virology, and structural/synthetic/systems biology.

63 agents

Expert-thinking subagents for clinical medical specialties: cardiology, oncology, radiology, pathology, neurology, surgery, anesthesiology, pharmacology, and related fields.

54 agents

Expert-thinking subagents for chemistry: organic, inorganic, physical, analytical, electrochemistry, catalysis, spectroscopy, mass spectrometry, and crystallography.

48 agents

Expert-thinking subagents for mechanical, civil, chemical, aerospace, and process engineering: fluid dynamics, structures, manufacturing, thermodynamics, and reliability.

48 agents

Expert-thinking subagents for earth, environmental, ocean, and atmospheric science: geology, geophysics, climate, oceanography, hydrology, remote sensing, and sustainability.

46 agents

Expert-thinking subagents for organismal, ecological, and evolutionary biology: zoology, botany, physiology, taxonomy, phylogenetics, conservation, and marine/field biology.

43 agents

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