parallel-geospatial

Run pre-trained AI models on geospatial imagery. Detect buildings, cars, ships, solar panels, agriculture fields, or use text-prompted segmentation with GroundedSAM. Requires GPU for best performance.

1 skill

This skill should be used when working with GDAL command line tools for raster and vector geospatial processing. Covers inspection, reprojection, clipping, raster conversion, mosaics, tiling, rasterization, and COG creation.

1 skill

This skill should be used when working with gpio for GeoParquet inspection, validation, optimization, and distribution. Covers GeoParquet best practices using gpio CLI and DuckDB.

1 skill

This skill should be used when building map-centric demo webapps with MapLibre globe, DuckDB-WASM, and a warm dark HUD aesthetic. Covers satellite imagery retrieval demos, embedding visualization tools, geospatial search UIs, and map-first data exploration frontends.

1 skill

This skill should be used when viewing, previewing, or inspecting geospatial files from the command line. Covers interactive raster viewing (viewtif), interactive vector viewing (viewgeom), and non-interactive terminal-inline viewing (viewinline).

1 skill

GeoZarr conventions for geospatial Zarr stores: CF grid_mapping, coordinate arrays, Zarr v2/v3 dimension declarations, multiscales, proj:/spatial: namespaces, and xarray/rioxarray/GDAL interop.

1 skill

Generate natively editable PPTX from PDF / DOCX / URL / Markdown — real DrawingML shapes, text boxes, charts, and animations. Note: run `pip install -r requirements.txt` inside the installed plugin directory for the Python post-processing scripts to work.

1 skill

Action-first, ADHD-friendly output shaping for Claude Code.

1 skill · hooks

DuckDB-powered skills for Claude Code: read any data file, attach and query DuckDB databases, search DuckDB/DuckLake docs, search past session logs, and install/update DuckDB extensions.

9 skills

Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams

6 skills · 7 commands · 4 agents

Multi-agent collaboration — spawn N parallel subagents that compete on code optimization, content drafts, research approaches, or any task that benefits from diverse solutions. 7 slash commands (/hub-init, /agenthub:spawn, /hub-status, /agenthub:eval, /agenthub:merge, /agenthub:board, /hub-run), agent templates, DAG-based orchestration, LLM judge mode, message board coordination.

8 skills · 1 agent · hooks

Comprehensive context engineering and harness engineering skills for production-grade AI agent systems: fundamentals, degradation patterns, compression, optimization, self-managed context (model-controlled live context editing), multi-agent coordination, long-horizon prompting (pseudo-formal task briefs for long-running and parallel agents), memory systems, tool design, filesystem context, hosted agents, evaluation, autonomous harnesses, self-improvement loops (RSI, meta-harnesses, evolutionary scaffold search), latent briefing (KV cache sharing between agents), project development, and cognitive architecture. Ships with a researcher operating system (rubrics, mechanism registry, claim provenance, run state machine, adversarial benchmarks, continuous loop).

19 skills