OpenMapStack Toolkit

Production-grade geospatial workflows with an open-first stack and pragmatic hosted/SaaS choices when global scale, latency, SLA, or data quality makes local processing a poor fit. Cloud-native by default: STAC for discovery, GeoParquet + COG + PMTiles for storage, DuckDB and PostGIS for compute, MapLibre and Martin for delivery.

Choose a primary skill

Keep coupled source, compute, storage and delivery decisions together. Prefer one primary skill, with focused support only where the task needs it:

If a specialist is absent, use this skill's local references for the bounded task. Do not require an installation or activate every skill. Material analyses retain the complete project contract below, including in a generalist-only install. Product-specific help follows references/companion-skills.md; OpenMapStack retains GIS semantics and cross-stage correctness.

Reproducible project-first contract

For every material multi-stage GIS analysis, compile or maintain a deterministic, inspectable, reproducible technical project before delivery. A map, dashboard or report is a view over that project, not the canonical definition of the analysis.

Before compiling or delivering a material analysis, read references/project-workflow.md for the mandatory workflow and delivery rules, and references/project-spec.md for the full openmapstack-project/v1 schema. Use templates/ and the worked examples/tartu-development project. For CLI installation and the example's runtime requirements, read references/installation.md.

Keep authoritative sources real, immutable and pinned; never invent baseline geometry without explicit informed consent. Record assumptions and every manual correction as data or canonical pipeline logic, make overrides executable and verified, and preserve unknown semantic attributes as unknown. The project must have explicit CRS, deterministic ordered steps, machine-readable validation and run evidence, provenance, and a documented clean rerun without chat dependencies. Check whether the CLI is installed (openmapstack --version or python3 -m openmapstack --version, then use whichever form works for every command below); if it is, run openmapstack validate and the canonical openmapstack run path, then openmapstack verify project.yaml --rerun, which rebuilds the project from its declared inputs in an empty workspace. Make that verify --rerun your last step and do not deliver until it passes; a passing validate alone is not enough. The canonical pipeline must produce every file under data/derived, every selected deliverable, the run record, and the manifest's runs.latest and project.status, never a one-off script or a hand edit: a rerun writes a new run record, so a hand-patched pointer breaks it. Missing validation capability is not_testable, never an implicit pass. Generate durable Markdown findings from pipeline results. Follow the referenced workflow's presentation obligations for the selected targets. Declare the real author and toolkit; render linked credits with the canonical run time in every delivered view (see the workflow's credits requirement). Review the required attribution prose too; field checks cannot certify its meaning. Record delivery.schema: openmapstack-delivery/v1 for new projects, defaulting to the built-in dashboard. Respect explicit external-only or combined delivery; QGIS and Observable are optional targets. Existing manifests without delivery retain legacy v1 behavior until explicitly migrated. Source provenance, GIS correctness and clean reruns are required for every selection.

For dashboard basemaps, honor user choices and appropriate local/regional services; otherwise use the preconfigured goplex.ee Protomaps fallback in references/web-delivery.md, with light/dark switching and visible provider attribution.

For a bounded one-shot SQL, CRS or conversion question, the relevant domain reference is sufficient; a full project artifact and its methodology are not needed.

When the user has dlt, Dagster, dbt, QGIS or Observable in their stack, read references/established-stack.md for ownership and integration boundaries. Preserve their chosen tools; retain source pins, GIS correctness and reproducible evidence. Do not duplicate their SQL, ingestion or scheduler definitions.

Modules — read the relevant reference(s) before starting work

For simple one-shot questions (single CRS conversion, one ogr2ogr invocation), the relevant reference alone is sufficient — a full project artifact is not needed. For multi-stage pipelines, read data-sources.md and processing.md together, and see project-spec.md + templates/ to compile analysis into a rerunnable project. For end-to-end "from raw data to web map" tasks, also read web-delivery.md.

Global defaults — apply unless the user specifies otherwise

• Storage formats: GeoParquet (vector analytics), COG (raster), PMTiles (tile delivery), GeoPackage (desktop interchange). Never produce Shapefile as new output.
• CRS: WGS84 (EPSG:4326) for storage; Web Mercator (EPSG:3857) for web rendering; local projected CRS for any metric computation (distance, area, buffer). For Estonia, EPSG:3301 (L-EST97). For DuckDB, set connection axis order explicitly before reprojection or GDAL I/O (normally geometry_always_xy = true); keep always_xy := true on ST_Transform to make the x/y contract explicit. See references/spatial-sql.md for setup. Before delivery, check a transformed feature against its source and a known location; a correct CRS label does not prove correct coordinates. See references/formats-and-crs.md.
• Coordinate order: treat it as an interface contract, not a CRS guess. Internally prefer GIS x,y (lon,lat for geographic coordinates), but obey each external format/API contract; GeoJSON, MapLibre LngLatLike, and OpenLayers geographic arrays use lon,lat, while Leaflet LatLng and Google Maps LatLng use lat,lng. Never infer tuple order from EPSG:4326 alone. See references/formats-and-crs.md.
• Layer placement: declare the area of interest (project.aoi) and, after loading and after each reprojection, check that every local layer lies inside it. A layer far outside a city or country AOI signals an axis swap, a wrong CRS label or a wrong-area filter; report it, and fix it only with source evidence.
• Compute placement: push spatial joins and aggregations to DuckDB or PostGIS — not Python loops. R-tree / GIST / spatial indexing is mandatory at scale.
• Discovery first: check STAC catalogs (Microsoft Planetary Computer, Earth Search, Overture STAC) before downloading anything. Lazy load with odc-stac or stackstac and only materialize what's needed.
• Cloud-native access: prefer querying remote GeoParquet/COG over downloading. DuckDB with httpfs extension is the default pattern for Overture and similar S3-hosted datasets.
• Scale first: local tools are fine for city/state work; at continental/global scale prefer cloud-native partitioned datasets, precomputed tiles, hosted APIs, or SaaS when they are more reliable than local batch processing.
• License hygiene: preserve license metadata through every transformation. OSM is ODbL (share-alike); Overture varies by source; Sentinel is free-with-attribution; national data varies.
• Runtime hygiene: prefer conda-forge environments or containers for GDAL/PROJ/GEOS/QGIS stacks. Avoid pip-only geospatial environments unless the project already proves they work.

Format decision matrix

Compute decision matrix

Universal anti-patterns — flag and correct

• Hallucinating or fabricating mock coordinates and geometries instead of retrieving real source data (unless the user gave explicit, informed consent for a synthetic mock test)
• Generating a selected QGIS project that disagrees with the shared presentation layers, omits basemaps, or uses broken OGR datasource syntax (path.gpkg|layer without layername=), causing layers to load as non-spatial attribute tables
• Writing .qgs XML by hand with no <srs>, an auth-id-only CRS block, or no ProjectionsEnabled, or copying the manifest's layer order straight into the layer tree — these produce a project where every layer is valid and every datasource resolves, yet the map shows the wrong place or silently hides a layer
• Producing Shapefile as new output (column truncation, 2GB limit, no UTF-8, multi-file)
• Calling .distance(), .buffer(), or .area on geographic CRS (EPSG:4326) — degrees are not meters; unless specific tool explicitly supports wgs84 based geodesic calculations
• Treating Web Mercator (EPSG:3857) coordinates as undistorted ground measurements — its coordinate units are metres, but ground-distance and area distortion vary with latitude
• Spatial joins in Python loops when DuckDB / PostGIS / R-tree-backed sjoin is one line away
• Using bbox containment for area queries when features can cross the boundary — use bbox overlap as the scan gate, then an exact spatial predicate
• Downloading entire datasets when STAC + cloud-native formats allow lazy/range-request access
• Running planet-scale local processing for lookup/search problems when reliable hosted services or precomputed global products already exist
• Treating MBTiles as the default for new web deployments — PMTiles is the modern default
• Using GeoTIFF when COG is one flag away (-of COG)
• Mixing CRS silently — every join must assert matching CRS
• Hand-rolling routing or geocoding when OSRM, Valhalla, or Nominatim are one Docker pull away
• Pinning data to "latest" in a reproducible pipeline — pin Overture release version and STAC item IDs, not just collections. For Overture, verify the pinned release is still available or mirror it.

Quick triage — recognize the request type

Before diving into a task, classify it:

1. Discovery ("what data exists for…?", "is there a dataset of…?", "read this catalog") → start with data-sources.md. STAC search if raster; Overture or OSM if vector basemap; for a Portolan catalog read its AGENTS.md before querying.
2. Conversion / CRS ("convert this to…", "reproject to…", "the projection looks wrong") → formats-and-crs.md. Usually one ogr2ogr or gdalwarp call.
3. Analysis ("what's the average elevation in…", "how many buildings within 500m of…", "where are the hotspots?") → analytics.md and likely processing.md. Push to DuckDB/PostGIS first.
4. Delivery ("publish this as a web map", "generate tiles for…") → web-delivery.md. PMTiles + Martin + MapLibre is the default.
5. Desktop / cartography ("style this in QGIS", "make a print map", "automate this in QGIS") → qgis.md. Consider QGIS MCP for agentic workflows.

Most real tasks span 2–3 of these — read the relevant references in order.

Reproducibility checklist for any pipeline you produce

• Pin dataset versions (Overture release, STAC item IDs, OSM extract dates) — never "latest"
• Document CRS at every stage; never assume
• Use conda-forge envs or pinned container images (ghcr.io/osgeo/gdal:alpine-small-latest for GDAL, qgis/qgis:<tag> for PyQGIS); pip-only geospatial envs break frequently
• Validate outputs: gpq for GeoParquet, rio-cogeo validate for COG, pmtiles show for PMTiles, is_valid for geometries
• Preserve license metadata in column or sidecar JSON and carry required attribution into maps/APIs

Command-level detail for each of these lives in references/validation-and-ops.md.