# Open Map Stack > Use textual agent instructions for GIS and geospatial work: source discovery and provenance, vector/raster/point-cloud pipelines, CRS and metric analysis, spatial SQL, routing and isochrones, QGIS projects, tile generation, and web maps. Use advanced tools and formats such as OSM, Overture, STAC, Sentinel/Landsat, LiDAR, GeoPackage, GeoParquet, COG, PMTiles, WMS/WFS/OGC APIs, GDAL, GeoPandas, Duc… - **Type:** Skill - **Install:** `agentstack add skill-jaakla-openmapstack-openmapstack` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [jaakla](https://agentstack.voostack.com/s/jaakla) - **Installs:** 0 - **Category:** [Databases](https://agentstack.voostack.com/c/databases) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [jaakla](https://github.com/jaakla) - **Source:** https://github.com/jaakla/openmapstack - **Website:** https://github.com/jaakla/openmapstack#readme ## Install ```sh agentstack add skill-jaakla-openmapstack-openmapstack ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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. ## Reproducible project-first contract > **Core principle: reasoning may be exploratory; the delivered analysis must be deterministic, inspectable, and reproducible.** For any material multi-stage GIS analysis, do not optimize for reaching the final map, dashboard, or answer quickly. A one-off polished dashboard is **not** the deliverable — a reproducible technical GIS project is. First establish a reusable project artifact, then derive the map/dashboard/report from it. Before treating an analysis as complete, you MUST compile (or maintain) a project like `examples/tartu-development`: a canonical `project.yaml` (`openmapstack-project/v1`), `pipeline.py`, and `README.md`; pinned sources with timestamps, selections and licensing; explicit assumptions; every manual addition or correction stored as real geodata; deterministic ordered steps with explicit CRS; machine-readable validation rules plus the report from the run; and output definitions with semantic presentation intent and provenance surfaced in the rendered view. **`references/project-spec.md` is the full schema — read it before compiling a project.** Workflow (agent may retry/experiment internally, but the accepted analysis is recompiled deterministically): ``` USER QUESTION ↓ interpretation / exploration (internal, may be ad-hoc) ↓ COMPILE GIS PROJECT project.yaml + pipeline + manifest + overrides + validation ↓ EXECUTE PROJECT ↓ validated derived datasets ↓ QGIS project / standardized web view ↓ FINAL ANALYSIS / DASHBOARD / ANSWER ``` The polished map/dashboard is a **view over the project**, not the canonical definition of the analysis. Hard rules for every material analysis — each is expanded in `references/project-spec.md`: * **Real source data mandatory; never hallucinate coordinates.** Fabricating coordinates or synthesizing baseline geometry is forbidden without explicit, informed user consent. Hypothetical or planned features go in `data/overrides/` with provenance, rationale, and evidence. * **Never mutate source data.** Immutable source + project override layer = effective input. Distinguish external facts, transformations, corrections, assumptions, and hypothetical data. * **Overrides must be executable and verified.** Target a real source feature; for attribute changes the asserted prior value must match. Evidence must be non-placeholder. Validation reports each override `applied`, `rejected`, or `not_testable` — listing one is not applying it. Scenario features stay labeled hypothetical and visually distinct from authoritative layers. * **Record, don't memoize on chat.** Encode every manual fix as data or pipeline logic. A fresh environment with the documented sources must reproduce the project; the transcript is not part of the dependency graph. * **Prove semantic predicates from data.** Ownership, active status, public access, legal designation: the source must expose an authoritative field or documented mapping. Preserve unknown as unknown; never default a missing value to the desired class. * **Bounded APIs must prove completeness.** Record `numberMatched`/equivalent and page until returned == matched. A response filled to the request limit is incomplete until proven otherwise. * **Validation is a pipeline stage**, not prose advice, with machine-readable results. Every declared check appears exactly once in the report; `warning`/`not_testable` propagate to run and project status; run IDs and hashes resolve to a real `runs/*.json` record. * **Run the project CLI when available.** Use `openmapstack validate project.yaml` before delivery and `openmapstack run project.yaml` for the canonical execution path. The CLI audits the manifest, provenance, graph, artifacts, report, and run record; it does not replace domain GIS checks performed by the pipeline. * **The manifest must resolve.** Every step input is a source key or an earlier step's output, spelled as the producer declared it; every `generated_by` names a real step (`manifest_graph_resolves`). * **One canonical implementation creates every declared output.** Convenience/E2E entrypoints may wrap `pipeline.py` but must not duplicate its processing, QGIS, or report logic. * **Build a layer- and style-perfect QGIS project (`project.qgz`)** mirroring the web view: matching layer-tree groups, identical categorized styles, `./path.gpkg|layername=name` datasources, and a regional tiled basemap. **Success means valid layers, not exit code 0** — pin the runtime, and when PyQGIS is available require every layer `isValid()`; otherwise record `not_testable`, never an implicit pass. * **Separate analysis semantics from rendering.** Declare semantic presentation roles; don't reinvent layout/colors/UX per run. * **Ship a reconfigurable view, and never let it misrepresent the run.** Organise the sidebar into tabs of collapsible sections, give every layer group an on/off control, and expose the analysis parameters and scenario overrides as live controls. Each control opens at the value declared in `presentation.controls` and returning there must reproduce the published numbers; any other position labels itself exploratory and offers a reset. The browser re-applies published rules to values the pipeline measured — it never measures geometry, and a control that changes a shape switches between buffers the pipeline materialised. * **Labels must match the operation.** A Euclidean buffer is a "2 km straight-line proxy", not a walking catchment, and column names must say so too. State the measurement basis (nearest edge vs centroid) as an assumption — it changes which features qualify. * Cheat-sheet: `references/project-spec.md` defines the full schema; `templates/` gives ready scaffolds; `examples/tartu-development` is a worked reference project matching the acceptance scenario. ## Modules — read the relevant reference(s) before starting work | If the task involves... | Read | |---|---| | Finding or sourcing data (OSM, Overture, Sentinel, Landsat, building footprints, regional portals, STAC catalogs, MCP-based discovery) | `references/data-sources.md` | | Choosing local processing vs online/hosted/SaaS services for global or continental scale; basemaps, elevation, routing, geocoding, place search, postcode lookup APIs | `references/services-and-scale.md` | | Choosing a format, converting between formats, or any CRS / projection / EPSG question | `references/formats-and-crs.md` | | Compiling a reproducible GIS project artifact (`project.yaml`, pipeline, overrides, validation, presentation) | `references/project-spec.md` + `templates/` | | Running GDAL/OGR, GeoPandas, xarray, DuckDB, PostGIS, or PDAL — the actual processing | `references/processing.md` | | Writing or reviewing spatial SQL / GeoSQL in DuckDB Spatial, PostGIS, BigQuery GIS, Snowflake, or Sedona | `references/spatial-sql.md` | | Vector analytics, raster analytics, terrain/hydrology, network analysis, point cloud workflows | `references/analytics.md` | | Tile generation (PMTiles, MVT), tile servers (Martin, TiTiler), delivered rendering (MapLibre, deck.gl), or exploration rendering (kepler.gl, lonboard) | `references/web-delivery.md` | | QGIS desktop, QGIS plugin ecosystem, QGIS MCP, PyQGIS scripting, Processing toolbox | `references/qgis.md` | | Reproducibility, validation, license attribution, tile smoke tests, deployment checks | `references/validation-and-ops.md` | 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). * **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 | Use case | Format | |---|---| | Cloud analytics on vector | GeoParquet | | Streaming vector over HTTP | FlatGeobuf | | Desktop interchange | GeoPackage | | Web map vector tiles | PMTiles (containing MVT) | | Raster archive / serving | COG | | n-dimensional raster (time series, climate) | Zarr or NetCDF | | Point cloud archive | COPC (cloud-optimized LAZ) | | API response payload (small only) | GeoJSON | | Legacy compatibility (input only) | Shapefile | ## Compute decision matrix | Scale / context | Use | |---|---| | 100M features, distributed | Apache Sedona | | Continental/global lookup/search/routing/elevation | Hosted API or SaaS where coverage, SLA, terms, and price fit | | Planet-scale basemap delivery | Prebuilt PMTiles/vector tiles or managed basemap service | | n-dim raster, lazy/dask-backed | xarray + rioxarray (+ odc-stac for STAC ingest) | | CLI batch jobs on raster | GDAL utilities (`gdalwarp`, `gdal_translate -of COG`) | | Point clouds | PDAL pipelines | | Terrain & hydrology beyond `gdaldem` | WhiteboxTools or GRASS | | Desktop styling, cartography, ad-hoc exploration | QGIS (see `qgis.md`) | ## 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 QGIS project that lacks the web dashboard's layers, omits basemaps, or uses broken OGR datasource syntax (`path.gpkg|layer` without `layername=`), causing layers to load as non-spatial attribute tables * 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 * Web Mercator (EPSG:3857) for area or distance calculations — it is not equal-area, and the units are not in meters except at the equator * 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…?") → start with `data-sources.md`. STAC search if raster; Overture or OSM if vector basemap. 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:` 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`. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [jaakla](https://github.com/jaakla) - **Source:** [jaakla/openmapstack](https://github.com/jaakla/openmapstack) - **License:** MIT - **Homepage:** https://github.com/jaakla/openmapstack#readme Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** yes *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-jaakla-openmapstack-openmapstack - Seller: https://agentstack.voostack.com/s/jaakla - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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