openmapstack

AI agent skills for geospatial decisions, data discovery, spatial SQL and reproducible analysis projects.

Explore the project homepage and two interactive example dashboards. The static Pages site is assembled from the generated NYC and Tartu dashboards with python3 scripts/build_project_pages.py; no warehouse credentials are needed to serve it.

Install (assuming you have git and nodejs with npx already installed):

npx skills@1.5.26 add jaakla/openmapstack-skills --skill open-map-stack -g

OpenMapStack gives your favorite AI agent: Claude Code, Codex, Cursor, OpenCode, PI and 50+ other agents not just a one-off analysis, but a well-defined workflow project (project.yaml) defining from data discovery through reusable analysis (pipeline.py) to interactive web (dashboard.html) and GIS deliverables (QGIS project.qgz). The workflow becomes inspectable and repeatable as a clearly specified project with pinned sources, explicit assumptions and CRS choices and deterministic processing in a python script. It allows also isolated overrides, machine-readable validation, and surfaced provenance.

It is open-first (both data and code-wise) and cloud-native by default, built on shoulders of the established Open GIS stack: STAC for discovery; GeoParquet, COG, and PMTiles for storage and delivery; DuckDB and PostGIS for compute; and QGIS, MapLibre, and Martin for presentation. It also teaches to deploy GDAL/OGR, GeoPandas, xarray/rioxarray, PDAL, routing engines, spatial SQL, and pragmatic hosted services when scale or reliability requires them.

What's in this repo

The skill collection has four independently installable skills:

Skill	Use it for
open-map-stack	Ambiguous or multi-stage GIS work; choose sources, compute, storage and delivery together.
reproducible-gis-project	Compile or maintain the project manifest, canonical pipeline, pinned sources, corrections, validation and reruns.
geospatial-data-discovery	Find and assess open geospatial data under known requirements.
spatial-sql	Write, review or optimize spatial SQL on an already chosen engine.

Each skill has a small entry point and local references loaded as needed. The generalist retains bounded-task support and material-analysis requirements when installed alone. Optional product help is documented in the installed companion reference.

Open Map Stack - general guidance

Includes all the skills.

• generalist SKILL.md — the skill entry point: triggers, global defaults, format and compute decision matrices, anti-patterns, and a quick triage guide.
• references/data-sources.md - lists OSM, Overture, Sentinel/Landsat, regional portals, STAC catalogs and others.
• references/services-and-scale.md - depending on case use local installs or hosted/SaaS services for global-scale basemaps, elevation, routing, geocoding, place search, and postcodes.
• references/user-data-sources.md - the user's own warehouse data: credentials by reference, read-only discovery, approval-gated snapshots, and the pin classes that make a warehouse table reproducible.
• references/formats-and-crs.md - how to choose formats, conversions, projections, EPSG codes.
• references/processing.md - when and how to use GDAL/OGR, GeoPandas, xarray, DuckDB, PostGIS, PDAL and other open geo processing tools.
• references/analytics.md — do vector/raster analytics, terrain, hydrology, network, point clouds, geocoding etc.
• references/web-delivery.md — renderer selection for maps, PMTiles, MVT, Martin, TiTiler, MapLibre, deck.gl, kepler.gl, and lonboard formats and engines.
• references/qgis.md — QGIS desktop, plugins, PyQGIS, Processing, QGIS MCP.
• references/validation-and-ops.md — validation, manifests, attribution, and deployment checks, including the machine-readable reproducible-project contract.
• references/project-spec.md — the specificopenmapstack-project/v1 schema: compiling any material analysis into a reproducible GIS project (project.yaml, pipeline, source provenance, overrides, validation, semantic presentation, QGIS output).
• references/project-workflow.md — the mandatory material-analysis workflow and delivery rules, loaded when a task needs a reproducible project.
• templates/ — ready scaffolds (project.yaml, pipeline.py, presentation.yaml, validation.yaml) for new projects.

Reproducible GIS project

Use reproducible-gis-project when the analysis method and stack are chosen and another analyst needs to inspect, execute and verify the result without the original conversation. It guides the agent to produce:

• A project.yaml manifest with pinned sources, CRS, assumptions, ordered steps and outputs, so the inputs and decisions are explicit.
• One canonical pipeline with corrections and scenarios recorded as data or executable logic, so derived results can be rebuilt without manual edits.
• Machine-readable validation and run evidence, including a clean rerun, so failures and reproducibility claims can be checked.
• QGIS and web presentation derived from the same project, so maps and reports reflect the validated analysis.

The skill includes its own project templates and these local references:

• project-workflow.md — required steps and delivery rules for a material analysis.
• project-spec.md — the openmapstack-project/v1 manifest and artifact contract.
• data-sources.md — public data discovery and source assessment.
• user-data-sources.md — warehouse access and reproducible snapshots.
• formats-and-crs.md — format choices and coordinate-system correctness.
• qgis.md — QGIS project delivery and desktop integration.
• validation-and-ops.md — checks, provenance and operational validation.
• installation.md — CLI setup and local example requirements.
• companion-skills.md — optional product-specific help.

Installing the other skills is optional. If the source, compute or delivery architecture is still undecided, start with open-map-stack.

Geospatial data discovery

Use geospatial-data-discovery when the data requirements are known and the task is to find and assess suitable public datasets or the user's own geospatial data. It guides the agent to:

• Match coverage, time, feature meaning, attributes and resolution to the question, so a convenient dataset is not mistaken for the right one.
• Check current provider metadata, access and license terms, so availability and permission claims have evidence.
• Assess completeness and distinguish a sample or partial catalog from a full extract, so missing features are not misread as absent features.
• Report a reproducible version or snapshot strategy, retrieval details and limitations, so a later analysis can use the same source.

The standalone skill includes these local references:

• data-sources.md — public data providers, catalogs and discovery methods.
• user-data-sources.md — read-only warehouse discovery and snapshot rules.
• formats-and-crs.md — formats, coordinate systems and spatial extents.
• companion-skills.md — optional product-specific help.

A source assessment needs no project templates, worked example or OpenMapStack CLI. Use open-map-stack when source selection depends on unresolved compute, storage or delivery choices; add reproducible-gis-project if the user wants to turn a chosen analysis into a full project.

Spatial SQL

Use spatial-sql to write, review, debug or optimize a spatial query on an already chosen engine, such as PostGIS or DuckDB Spatial. It guides the agent to:

• Check the engine, geometry types, CRS, input grain and expected result, so the query uses supported functions and returns the intended entities.
• Choose spatial predicates and boundary behavior deliberately, including how null, invalid or multiply matched geometries affect the result.
• Use appropriate metric or geodesic operations for distances, areas and buffers, so a numeric threshold has the intended units.
• Review indexes, candidate filters and query plans, then run small controls when the engine is available, so performance changes preserve the result.

The standalone skill includes these local references:

• spatial-sql.md — engine-specific spatial SQL patterns and query review.
• formats-and-crs.md — coordinate, unit and format semantics.
• companion-skills.md — optional product-specific help.

A bounded query review needs no project templates, worked example or OpenMapStack CLI. Use open-map-stack when engine selection or cross-system architecture is undecided; add reproducible-gis-project if the user wants to compile a chosen analysis into a full project.

Additional materials:

• examples/tartu-development/ — a fully-worked reproducible project matching the acceptance scenario: source provenance + timestamps, explicit assumptions, two verified project overrides (a scenario attribute change with prior-value verification, and hypothetical scenario geometry), deterministic pipeline, machine-readable validation, and semantic presentation.
• evals/ — the eval suite grading whether an agent reaches the right analytical answer, respects the GIS-method guardrails, and reruns reproducibly, with the openmapstack-project/v1 contract as the substrate that makes those independently checkable: python evals/run.py --mode fixture runs deterministic, no-LLM checks against real generated artifacts (analytical correctness against known geospatial truth, metric CRS, source immutability, schema, overrides, validation integrity, presentation contract, and clean reruns), plus adversarial cases and a pluggable live-agent benchmark (Claude Code, Codex, and any OpenAI-compatible API such as OpenRouter — URL and model via OPENAI_COMPATIBLE_* env, API key as a secret).
• openmapstack/ — the installable openmapstack validate/run/inspect CLI for auditing and executing openmapstack-project/v1 projects, plus openmapstack/checks/: the reusable, semantic check library. All but five of its checks are oracle-free, so the same functions that grade the eval suite also grade a user's own project on data this repository has never seen.
• docs/openmapbench-interop.md — the narrow, versioned contract a benchmark harness such as OpenMapBench consumes: openmapstack checks / check / api-info (openmapstack-check-api/v1), the packaged result schemas, skill snapshots, arm provenance, and exported task bundles.
• .claude-plugin/ — Claude Code plugin and marketplace manifests, so the repository can also be installed with /plugin install. Validated in CI by .github/workflows/plugin.yml.

Some my local Estonia-specific guidance (Maa- ja Ruumiamet, ETAK, EPSG:3301 / L-EST97) is included for convenience. But most of the major global sources are included for world-wide coverage.

Install

The 0.4.0 collection provides four independently installable skills. The OpenMapStack CLI is a separate installation for project execution and validation. From a checkout, install the generalist and CLI for project work:

npx skills@1.5.26 add . --skill open-map-stack -a codex -y
python -m pip install '.[geo]'

Use -a claude-code for Claude Code, -g for global installation and --copy for independent copies. Select additional skills by repeating --skill NAME; --skill '*' selects the full collection. open-map-stack and reproducible-gis-project include local project templates, the project schema, a trimmed worked Tartu example and CLI setup instructions. The two bounded specialists ship their task guidance and local references; they need no CLI or installed sibling. Generated example outputs and downloaded source data are omitted; the example pipeline needs network access and its documented GIS environment.

Once the release tag and Python package are published, pin both parts of the coordinated release:

npx skills@1.5.26 add https://github.com/jaakla/openmapstack-skills/tree/v0.4.0/skills/open-map-stack -a codex -y
python -m pip install 'openmapstack[geo]==0.4.0'

For a floating install use npx skills@1.5.26 add jaakla/openmapstack-skills --skill open-map-stack. Check installed skills with npx skills@1.5.26 list, update with npx skills@1.5.26 update open-map-stack --project, and remove with npx skills@1.5.26 remove open-map-stack. Use --global instead of --project for a global update; match the scope for list/remove too. Updating a floating install advances its version; release-pinned installs should be replaced with an explicitly chosen release.

Claude Code plugin

/plugin marketplace add jaakla/openmapstack-skills
/plugin install open-map-stack@open-map-stack

The plugin retains its identity and discovers skills/<name>/SKILL.md. For local development use claude --plugin-dir /path/to/checkout; metadata validation and component inventory require no paid model run.

Manual installation and migration

Copy skills/<name>/ into your agent's skill directory. Do not copy the whole collection checkout into a single skill folder. If replacing a 0.3.0 root installation, retain the open-map-stack name and replace its installed payload; do not keep both root and nested copies. Inspect your actual installer lockfile and scope before changing anything. Legacy open-gis is a different identifier: remove it explicitly if it is an unwanted duplicate, rather than silently rewriting its lock entry.

The CLI is separate from skill installation and is needed only for project execution or validation. When installing it, verify openmapstack --version against the project skill's metadata.version. Before publication, a wheel or pinned Git commit from the matching checkout is the supported alternative.

Use

Agents discover the installed skill descriptions and select relevant guidance. Selection depends on the agent; explicitly naming a skill can help when you want a particular owner. Installing the collection does not require loading all four skills for every request.

• “We need regional analysis and browser maps for a billion building records; choose the architecture.” → open-map-stack.
• “Keep this analysis and stack, but make it reproducible and auditable.” → reproducible-gis-project.
• “Find authoritative Estonian building footprints; explain coverage, license and a reproducible pin.” → geospatial-data-discovery.
• “Review this PostGIS query for a 500-metre distance test on SRID 4326 geometries.” → spatial-sql.

The primary skill can use focused support. A bounded lookup or query review does not require a project; material multi-stage analyses retain the complete reproducibility contract. Casual place lookups and ordinary non-spatial coding are outside the collection's scope.

See the 0.4.0 release notes for executed validation and installation checks, consumer migration and known limitations.

Project CLI

The key innovation of the skill is not just do the work every time again and then forget it, but to create special well-defined project with data and process descriptions and rerunnable scripts, so the whole process becomes investigatable and repeatable.

To help with that we have special CLI to work with the projects.

The CLI operates on an openmapstack-project/v1 manifest. A project directory may be supplied in place of its project.yaml file.

# Audit the complete artifact, including outputs, report, and run record.
openmapstack validate path/to/project.yaml

# Check the produced artifacts without requiring a golden answer.
openmapstack verify path/to/project.yaml

# Run the one canonical pipeline, then validate what it produced.
openmapstack run path/to/project.yaml

# Review sources, versions, overrides, ordered steps, outputs, and latest run.
openmapstack inspect path/to/project.yaml

# Copy SKILL.md, references/, and templates/ into a hashed, inspectable snapshot.
openmapstack skill-snapshot --out /tmp/oms-skill --json
openmapstack skill-snapshot --inspect /tmp/oms-skill

# Read-only discovery of a warehouse source, then an approval-gated snapshot.
openmapstack source discover path/to/project.yaml --source parcels
openmapstack source snapshot path/to/project.yaml --source parcels \
  --query "SELECT id, geom FROM cadastre.parcels" --destination data/source/parcels.parquet --approve

Useful automation options:

openmapstack validate project.yaml --json --output validation/cli-report.json
openmapstack validate project.yaml --strict       # warnings also return non-zero
openmapstack validate project.yaml --preflight    # skip not-yet-generated artifacts
openmapstack run project.yaml --dry-run        # print the command, execute nothing
openmapstack run project.yaml --json
openmapstack inspect project.yaml --json

run reads Python dependencies from runtime.environment in project.yaml and installs them into a reusable project virtualenv, including when invoked through uvx. It warns if the CLI's Python version differs from the declared version: python: '3.12' accepts any 3.12 patch release. Select a matching interpreter with uvx --python 3.12 openmapstack run project.yaml.

Native dependencies such as GDAL, PROJ, QGIS, PostgreSQL, and PostGIS require external setup. run emits installation and verification guidance for declared native dependencies; their availability remains unverified. A GDAL installation may need particular drivers and matching Python bindings in the pipeline's runtime. PostGIS must be installed on the database server and enabled in the target database; installing a Python client is insufficient. Use a prepared system, Conda, or container runtime via runtime.implementation.command when needed. The declaration gdal: DuckDB spatial extension does not request a system GDAL installation: the pipeline must install/load DuckDB's spatial extension. See the runtime contract for dependency syntax and execution behavior.

Runtime advisories appear on stderr, or in the JSON warnings array, including for --dry-run. They are non-blocking and separate from artifact validation; --strict continues to apply to validation, not these advisories. An explicit launch command owns its interpreter, so the CLI does not compare that interpreter against its own Python version.

Sampled runs — nail it before you scale it

A wide-area analysis can run for hours before a late step fails. A sampled run executes the same pipeline over a deliberately smaller slice, so failure arrives in minutes:

openmapstack run project.yaml --sample                     # the manifest's declared sample
openmapstack run project.yaml --sample-area 26.6,58.3,26.8,58.4
openmapstack run project.yaml --sample-rows 5000
openmapstack run project.yaml --sample-fraction 1.0

Each flag binds a runtime.implementation.parameters entry that declares the matching role; sampling a project that declares none is refused, naming what the manifest must add. The canonical run still passes nothing.

A sampled run proves the pipeline executes; it does not establish the result. Clipping to a test AOI breaks neighbourhood operations at the cut and row sampling destroys the spatial coherence a join needs, so sampled counts are not answers. That is enforced, not merely advised: a sampled run record is marked mode: sampled, must record what it realized rather than only what was requested, and can never become runs.latest — openmapstack validate reports this as runs.sample_isolation, and run --sample fails outright if a pipeline promotes its own sampled run — by moving runs.latest, by rewriting the record it already points at, or by leaving no sampled record behind at all. See references/project-spec.md.

openmapstack verify — check the analysis, not just the paperwork

validate audits the manifest and its bookkeeping. verify runs the check library in openmapstack/checks/ against what the pipeline actually produced: geometry read back through DuckDB Spatial, dataset CRS read from the artifact rather than the manifest's claim, validation evidence recomputed from the geodata it summarises, non-spatial kind: table outputs read back as tables (declared columns and types, unique key, CSV/XLSX downloads holding the same rows), and QGIS project structure and runtime loading where PyQGIS is available.

openmapstack verify path/to/project.yaml
openmapstack verify path/to/project.yaml --rerun     # + rebuild from source and compare
openmapstack verify path/to/project.yaml --metamorphic   # + run declared no-oracle relations
openmapstack verify path/to/project.yaml --json --output validation/verify-report.json
openmapstack verify path/to/project.yaml --strict    # warnings and not-testable also return 1

These checks require no repository-owned golden answer, so they work on data neither this repository nor the model has seen. They establish bounded structural, provenance, artifact, and reproducibility predicates; they do not prove every project-specific analytical answer.

--rerun is the strongest signal available without a known answer. It rebuilds the project in an empty workspace from only the manifest, the declared immutable inputs, and the declared dependencies, runs the one canonical entrypoint, re-hashes the sources, and compares the outputs semantically. A pipeline that cannot reproduce itself, or that mutates its own declared immutable inputs, is not trustworthy whatever its numbers say.

The check plan is derived from the manifest rather than configured, so a project cannot opt out of a check by omitting it: a declared output is a checked output. A check whose dependency is missing reports not_testable and is counted separately — never a silent pass. A mixture of executed and not_testable checks has aggregate status warning, and every report includes applicable, executed, and execution_rate coverage. Install openmapstack[geo] for the DuckDB-backed geodata checks and for reading XLSX (DuckDB excel extension) and Parquet tables; CSV and JSON tables need nothing extra. PyQGIS comes from a system QGIS install.

See the applicability reference for the exact plan conditions, dependencies, current regression evidence, and deliberate exclusions. In particular, browser/dashboard checks are not yet part of the automatic verify plan.

Project-specific known answers can be declared under validation.expectations[]. The five allowlisted checks cover row count, feature presence/absence, one feature-field value, and field range. New expectations start as attestation.status: unverified; they produce a warning and are not executed. The JSON report supplies the exact expected_expectation_sha256 an independent reviewer must bind, together with the current runs.latest.inputs_hash. Changing the expected check, arguments, inputs, or a retained local evidence file invalidates the attestation and returns it to warning status. See the project contract.

Where no golden answer exists at all, validation.metamorphic[] declares relations that must hold under a controlled perturbation: shuffle a source and the result must not change, duplicate every feature and a keyed set must not change, widen an inclusion buffer and no candidate may disappear. Each relation states the precondition that makes it valid, is executed by verify --metamorphic in an isolated copy against the project's own pipeline, and reports not_testable with the reason when the precondition does not hold on the actual data. See the project contract.

openmapstack source is the connector pilot for the user's own data (DuckDB local files and PostGIS). Credentials are referenced, never stored; discovery is read-only with a statement timeout; a snapshot is a dry run until --approve, is limited by rows and bytes, lands only under data/source/, and hands back the pin block that makes the source reproducible. A warehouse table with only a timestamp is not pinned; an expired backend snapshot is reported as not_reproducible. See user data sources.

validate checks manifest structure, source retrieval/version/licensing data, CRS declarations, processing graph resolution, override provenance and files, output existence, validation-report parity/status propagation, override application results, and run-record identity/hashes. GIS-specific checks such as geometry validity remain the pipeline's responsibility; the CLI verifies that each declared check appears exactly once with an explicit result.

Normal validation warnings return exit code 0 so known limitations remain representable. Failures return 1; malformed invocation or an unstartable runtime returns 2. --strict makes warnings return 1.

What this skill will and won't do

Will:

• Recommend modern, cloud-native formats (GeoParquet, COG, PMTiles) and flag legacy patterns (Shapefile output, MBTiles for new deployments).
• Push spatial joins to DuckDB / PostGIS instead of Python loops.
• Discover data via STAC before downloading.
• Preserve license metadata (OSM ODbL, Overture per-source, Sentinel attribution).
• Pin dataset versions for reproducibility (Overture releases, STAC item IDs, OSM extract dates).
• Compile material multi-stage analysis into a reproducible GIS project (project.yaml + pipeline + overrides + validation), deriving the final map/dashboard from it.

Won't:

• Trigger on simple location lookups ("what city is this?") or casual map references with no analytical work.
• Default to proprietary services when an open/self-hosted option fits the scale, quality, privacy, and budget.

License

Licensed under the MIT License.

Contributing

Issues and PRs welcome at github.com/jaakla/openmapstack-skills. When adding a new tool or workflow, place it in the matching reference file and add a one-row entry to the relevant decision matrix in generalist SKILL.md.