Claude Skill

geoai-orchestrator

Route genuinely ambiguous or multi-stage geospatial work across specialist skills while enforcing shared CRS, validity, leakage, units, verification, and reproducibility rules. Use for requests spanning multiple stages such as acquisition, imagery, modeling, analysis, and map del

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Part of muend/geoai-skills — 18 skills

Install

skills CLI npx skills add https://github.com/muend/geoai-skills/tree/main/skills/geoai-orchestrator
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install muend-geoai-skills@llmmart
Git git clone https://github.com/muend/geoai-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole muend/geoai-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

GeoAI Orchestrator

The hub of an 18-skill geospatial module. Activate it for routing or pipeline composition, not as a mandatory wrapper around every spatial task. Its job: (1) diagnose what kind of spatial problem the user actually has, (2) design the pipeline across stages, (3) route each stage to the right specialist skill, and (4) enforce the module-wide invariants that every stage must obey.

Routing gate — read before producing any output

This orchestrator routes by invoking, never by naming. The gate below overrides every other section of this document, including the pipeline template.

  1. Invoke, do not list. Every specialist you select must be invoked with the Skill tool in the same response that selects it. Naming a skill in a table, plan, or prose sentence is not a handoff. A response that identifies the right specialist but does not invoke it has failed this skill's core function, no matter how accurate the diagnosis is.
  2. Route every correction, not the first one. When a request contains multiple findings, defects, or stages, each one gets its own routing decision and its own invocation. Routing one item and handling the rest inline is a partial failure; the count of routed items must equal the count of items found.
  3. Never make routing conditional on permission. Do not write "say the word and I'll route", "I can hand this off if you want", "let me know and I'll bring in the specialist", or any equivalent. Offering to route later is the single most common failure of this skill. If you have identified the specialist, invoke it now.
  4. Clarification is not a substitute for routing. Missing detail about scope (which deliverable, which study area) does not block routing of the stages you have already identified. Ask the scope question and route in the same response. Only a request whose entire domain is undetermined may be routed-free, and then you must say which specialist becomes available under each candidate answer.
  5. Audit requests are deliver requests. "Audit this plan", "review this pipeline", "what is wrong with this workflow" require the completed audit, the routed corrections, and the revised plan in one response. Do not return findings and hold the corrections back for a follow-up turn.

If you cannot satisfy the gate, do not activate this skill — route the request directly to the single narrowest specialist instead.

Module map — route by problem type

Stage / problem Specialist skill
Data acquisition, formats, CRS, tiling, pipelines geo-data-engineering
Satellite/aerial imagery, spectral indices, classification remote-sensing-analysis
Planetary-scale archives, GEE Python API, cloud compositing google-earth-engine
CNN/U-Net/ViT on EO data, segmentation, detection geo-deep-learning
Autocorrelation, hotspots, clusters, spatial regression spatial-statistics
Site selection, suitability, AHP/weighted overlay mcda-suitability-analysis
Interpolation from point samples, kriging, variograms geostatistics-interpolation
DEM, slope, watersheds, flow, viewshed terrain-hydrology
LiDAR / point clouds, DTM/DSM/CHM, PDAL point-cloud-lidar
Routing, service areas, accessibility, OD matrices network-accessibility-analysis
GPS tracks, trajectories, stops/trips, map matching movement-trajectory
Multi-temporal comparison, land cover change, trends change-detection
Map design, choropleths, web maps, publication figures cartography-geoviz
Spatial SQL, PostGIS, large-scale spatial joins postgis-spatial-sql
Local ArcGIS Pro, ArcPy, .aprx, or .gdb execution arcgis-pro-automation

This table selects specialists; it does not hand off to them. Every row you select must be invoked under the routing gate. For cross-cutting method standards (leakage, metrics, reproducibility), invoke ml-experiment-standards and swe-devops-standards when their rules apply.

Pipeline design protocol

For any multi-stage request, produce a short pipeline plan BEFORE writing code, then invoke the specialists that plan names in the same response:

## Pipeline: <goal>
1. <stage> → <skill> → output: <artifact> → check: <verification criterion>
2. ...
Success criterion: <what the user can inspect to accept the result>

The plan is a routing manifest, not a proposal awaiting approval. Publishing the plan and stopping there is the failure mode this skill exists to prevent. Do not wait for confirmation before routing; confirmation is only ever sought for scope (which deliverable, which extent, which decision), and it is requested alongside the routed stages, never instead of them.

Every stage ends with a verification criterion. Spatial work fails silently (wrong CRS, empty joins, inverted axes produce plausible-looking garbage), so a stage without a check is not a stage.

Module-wide invariants (enforced in every stage)

  1. CRS is explicit, always. Report the CRS of every input on first contact. Never compute area/distance/buffer in a geographic (degree) CRS — reproject to an appropriate projected CRS (local UTM zone by default via gdf.estimate_utm_crs(); equal-area such as EPSG:6933 for global area statistics). If a CRS is undefined, stop and resolve it; never guess silently.
  2. Axis order discipline. GeoJSON is lon/lat; many APIs and humans say lat/lon. Verify with a known landmark before pipeline-scale processing.
  3. Geometry validity before analysis. Check is_valid; repair with shapely.make_valid (not buffer(0), which can silently drop parts).
  4. Row-count accounting. After every join/overlay/filter, report rows in vs rows out. Silent duplication or loss is the top geospatial bug.
  5. Spatial autocorrelation awareness. Random train/test splits on spatial data leak. Any ML stage follows the canonical protocol in ml-experiment-standards → references/spatial-cv-protocol.md.
  6. Units in column names. area_ha, dist_km, elev_m — never bare area. Unit confusion survives code review; column names don't lie.
  7. Visual + numeric verification. Every spatial output gets both a summary table AND a quick map check (.explore(), a PNG, or GIS software). A confusion matrix cannot show spatially clustered errors.
  8. Reproducibility. Pin package versions, seed randomness, log parameters. Intermediate artifacts go to GeoPackage or GeoParquet, never shapefile (10-char column truncation, 2 GB limit, no proper encoding).

Internationalization note

Attribute tables in non-ASCII locales break naive string handling. Canonical example: Turkish dotted/dotless I — 'İ'.lower() yields a 2-character string in Python. Before any string matching on attributes, apply a locale-aware normalization step and show value_counts() of cleaned categorical fields. Prefer UTF-8 formats; legacy shapefiles may carry cp1252/cp125x mojibake silently.

Choosing the stack

Default to the open Python stack: GeoPandas + Shapely 2 + Rasterio + xarray/rioxarray + PyProj. Route to PostGIS when data exceeds comfortable memory (~millions of features) or needs concurrent/repeated querying; to Earth Engine when the data is a planetary archive rather than local files. Use GDAL CLI for bulk format conversion. If the user works in ArcGIS Pro or QGIS, generate headless-runnable scripts (arcpy / PyQGIS) rather than click instructions, and keep the analysis logic portable.

Anti-patterns to catch early

  • Buffering in degrees ("0.01 degree buffer") — reproject first.
  • EPSG:4326 → Web Mercator area statistics — Mercator distorts area massively away from the equator.
  • Joining datasets from different CRS without alignment.
  • Treating a DEM's nodata value (-9999, 3.4e38) as real elevation.
  • Classifying imagery without checking cloud/shadow masks.
  • Reporting model accuracy without a spatially independent test set.

Execution contract

  • Workflow: clarify objective and deliverable; decompose the multi-stage problem; route each stage to the narrowest skill by invoking it with the Skill tool; declare handoffs and invariants; integrate and verify the final artifact.
  • Decision rules: invoke this orchestrator only for ambiguous or cross-domain work; route a single well-scoped task directly to its specialist skill.
  • Verification protocol: require stage-level acceptance checks, count and CRS handoff assertions, end-to-end provenance, and final-product review against the original question. Before returning, confirm that every specialist named in the response was actually invoked and that the number of routed corrections equals the number of findings.
  • Failure modes: pause when ownership, units, CRS, temporal alignment, evidence standards, or stage interfaces remain ambiguous; never hide unresolved specialist failures. Never substitute an offer to route for an invocation, and never defer routed corrections to a later turn.
  • Deliverables: pipeline plan, skill-routing table, stage inputs and outputs, verification gates, risk register, and final integration checklist.
  • Source freshness: consult the authoritative source registry and the selected specialists' registries before fixing interfaces.
Files (geoai-skills)
  • agents
    • openai.yaml 207 B
      interface:
        display_name: "GeoAI Orchestrator"
        short_description: "Route complex GeoAI workflows safely"
        default_prompt: "Use $geoai-orchestrator to plan and route this multi-stage geospatial project."
      
  • references
    • authoritative-sources.md 740 B
      # Authoritative sources
      
      - Last verified: 2026-07-19
      - Review cadence: every 6 months
      - Refresh triggers: OGC standard revision or a routed skill changes its interface contract
      
      ## Canonical sources
      
      - [OGC standards catalog](https://www.ogc.org/standards/) — current geospatial interoperability standards.
      - [EPSG Guidance Notes](https://epsg.org/guidance-notes.html) — authoritative coordinate reference and operation guidance.
      - [Agent Skills specification](https://agentskills.io/specification) — portable skill structure and progressive disclosure.
      
      The orchestrator does not replace specialist sources. At every handoff, load the routed skill's source registry and propagate its version, freshness, and validation requirements.
      
  • SKILL.md 9.9 KB
    ---
    name: geoai-orchestrator
    description: >-
      Route genuinely ambiguous or multi-stage geospatial work across specialist
      skills while enforcing shared CRS, validity, leakage, units, verification,
      and reproducibility rules. Use for requests spanning multiple stages such as
      acquisition, imagery, modeling, analysis, and map delivery, or for an
      explicit end-to-end pipeline. Never invoke for one domain merely because a
      parameter is unclear. Code implementation/review, backend or platform
      choice, and production-readiness review are direct specialist tasks. Do not
      add this skill as a layer around one specialist.
    license: MIT
    metadata:
      author: Muhammed Enes Duran
    ---
    
    # GeoAI Orchestrator
    
    The hub of an 18-skill geospatial module. Activate it for routing or pipeline
    composition, not as a mandatory wrapper around every spatial task. Its job:
    (1) diagnose what kind of
    spatial problem the user actually has, (2) design the pipeline across
    stages, (3) route each stage to the right specialist skill, and (4) enforce
    the module-wide invariants that every stage must obey.
    
    ## Routing gate — read before producing any output
    
    This orchestrator routes by **invoking**, never by naming. The gate below
    overrides every other section of this document, including the pipeline
    template.
    
    1. **Invoke, do not list.** Every specialist you select must be invoked with
       the `Skill` tool in the same response that selects it. Naming a skill in a
       table, plan, or prose sentence is not a handoff. A response that identifies
       the right specialist but does not invoke it has failed this skill's core
       function, no matter how accurate the diagnosis is.
    2. **Route every correction, not the first one.** When a request contains
       multiple findings, defects, or stages, each one gets its own routing
       decision and its own invocation. Routing one item and handling the rest
       inline is a partial failure; the count of routed items must equal the count
       of items found.
    3. **Never make routing conditional on permission.** Do not write "say the
       word and I'll route", "I can hand this off if you want", "let me know and
       I'll bring in the specialist", or any equivalent. Offering to route later is
       the single most common failure of this skill. If you have identified the
       specialist, invoke it now.
    4. **Clarification is not a substitute for routing.** Missing detail about
       *scope* (which deliverable, which study area) does not block routing of the
       stages you have already identified. Ask the scope question and route in the
       same response. Only a request whose entire domain is undetermined may be
       routed-free, and then you must say which specialist becomes available under
       each candidate answer.
    5. **Audit requests are `deliver` requests.** "Audit this plan", "review this
       pipeline", "what is wrong with this workflow" require the completed audit,
       the routed corrections, and the revised plan in one response. Do not return
       findings and hold the corrections back for a follow-up turn.
    
    If you cannot satisfy the gate, do not activate this skill — route the request
    directly to the single narrowest specialist instead.
    
    ## Module map — route by problem type
    
    | Stage / problem | Specialist skill |
    |---|---|
    | Data acquisition, formats, CRS, tiling, pipelines | `geo-data-engineering` |
    | Satellite/aerial imagery, spectral indices, classification | `remote-sensing-analysis` |
    | Planetary-scale archives, GEE Python API, cloud compositing | `google-earth-engine` |
    | CNN/U-Net/ViT on EO data, segmentation, detection | `geo-deep-learning` |
    | Autocorrelation, hotspots, clusters, spatial regression | `spatial-statistics` |
    | Site selection, suitability, AHP/weighted overlay | `mcda-suitability-analysis` |
    | Interpolation from point samples, kriging, variograms | `geostatistics-interpolation` |
    | DEM, slope, watersheds, flow, viewshed | `terrain-hydrology` |
    | LiDAR / point clouds, DTM/DSM/CHM, PDAL | `point-cloud-lidar` |
    | Routing, service areas, accessibility, OD matrices | `network-accessibility-analysis` |
    | GPS tracks, trajectories, stops/trips, map matching | `movement-trajectory` |
    | Multi-temporal comparison, land cover change, trends | `change-detection` |
    | Map design, choropleths, web maps, publication figures | `cartography-geoviz` |
    | Spatial SQL, PostGIS, large-scale spatial joins | `postgis-spatial-sql` |
    | Local ArcGIS Pro, ArcPy, `.aprx`, or `.gdb` execution | `arcgis-pro-automation` |
    
    This table selects specialists; it does not hand off to them. Every row you
    select must be invoked under the routing gate. For cross-cutting method
    standards (leakage, metrics, reproducibility), invoke `ml-experiment-standards`
    and `swe-devops-standards` when their rules apply.
    
    ## Pipeline design protocol
    
    For any multi-stage request, produce a short pipeline plan BEFORE writing
    code, then invoke the specialists that plan names in the same response:
    
    ```
    ## Pipeline: <goal>
    1. <stage> → <skill> → output: <artifact> → check: <verification criterion>
    2. ...
    Success criterion: <what the user can inspect to accept the result>
    ```
    
    The plan is a routing manifest, not a proposal awaiting approval. Publishing
    the plan and stopping there is the failure mode this skill exists to prevent.
    Do not wait for confirmation before routing; confirmation is only ever sought
    for *scope* (which deliverable, which extent, which decision), and it is
    requested alongside the routed stages, never instead of them.
    
    Every stage ends with a verification criterion. Spatial work fails silently
    (wrong CRS, empty joins, inverted axes produce plausible-looking garbage),
    so a stage without a check is not a stage.
    
    ## Module-wide invariants (enforced in every stage)
    
    1. **CRS is explicit, always.** Report the CRS of every input on first
       contact. Never compute area/distance/buffer in a geographic (degree)
       CRS — reproject to an appropriate projected CRS (local UTM zone by
       default via `gdf.estimate_utm_crs()`; equal-area such as EPSG:6933 for
       global area statistics). If a CRS is undefined, stop and resolve it;
       never guess silently.
    2. **Axis order discipline.** GeoJSON is lon/lat; many APIs and humans say
       lat/lon. Verify with a known landmark before pipeline-scale processing.
    3. **Geometry validity before analysis.** Check `is_valid`; repair with
       `shapely.make_valid` (not `buffer(0)`, which can silently drop parts).
    4. **Row-count accounting.** After every join/overlay/filter, report rows
       in vs rows out. Silent duplication or loss is the top geospatial bug.
    5. **Spatial autocorrelation awareness.** Random train/test splits on
       spatial data leak. Any ML stage follows the canonical protocol in
       `ml-experiment-standards` → `references/spatial-cv-protocol.md`.
    6. **Units in column names.** `area_ha`, `dist_km`, `elev_m` — never bare
       `area`. Unit confusion survives code review; column names don't lie.
    7. **Visual + numeric verification.** Every spatial output gets both a
       summary table AND a quick map check (`.explore()`, a PNG, or GIS
       software). A confusion matrix cannot show spatially clustered errors.
    8. **Reproducibility.** Pin package versions, seed randomness, log
       parameters. Intermediate artifacts go to GeoPackage or GeoParquet, never
       shapefile (10-char column truncation, 2 GB limit, no proper encoding).
    
    ## Internationalization note
    
    Attribute tables in non-ASCII locales break naive string handling.
    Canonical example: Turkish dotted/dotless I — `'İ'.lower()` yields a
    2-character string in Python. Before any string matching on attributes,
    apply a locale-aware normalization step and show `value_counts()` of
    cleaned categorical fields. Prefer UTF-8 formats; legacy shapefiles may
    carry cp1252/cp125x mojibake silently.
    
    ## Choosing the stack
    
    Default to the open Python stack: GeoPandas + Shapely 2 + Rasterio +
    xarray/rioxarray + PyProj. Route to PostGIS when data exceeds comfortable
    memory (~millions of features) or needs concurrent/repeated querying; to
    Earth Engine when the data is a planetary archive rather than local files.
    Use GDAL CLI for bulk format conversion. If the user works in ArcGIS Pro or
    QGIS, generate headless-runnable scripts (arcpy / PyQGIS) rather than click
    instructions, and keep the analysis logic portable.
    
    ## Anti-patterns to catch early
    
    - Buffering in degrees ("0.01 degree buffer") — reproject first.
    - `EPSG:4326 → Web Mercator` area statistics — Mercator distorts area
      massively away from the equator.
    - Joining datasets from different CRS without alignment.
    - Treating a DEM's nodata value (-9999, 3.4e38) as real elevation.
    - Classifying imagery without checking cloud/shadow masks.
    - Reporting model accuracy without a spatially independent test set.
    
    ## Execution contract
    
    - **Workflow:** clarify objective and deliverable; decompose the multi-stage problem; route each stage to the narrowest skill by invoking it with the `Skill` tool; declare handoffs and invariants; integrate and verify the final artifact.
    - **Decision rules:** invoke this orchestrator only for ambiguous or cross-domain work; route a single well-scoped task directly to its specialist skill.
    - **Verification protocol:** require stage-level acceptance checks, count and CRS handoff assertions, end-to-end provenance, and final-product review against the original question. Before returning, confirm that every specialist named in the response was actually invoked and that the number of routed corrections equals the number of findings.
    - **Failure modes:** pause when ownership, units, CRS, temporal alignment, evidence standards, or stage interfaces remain ambiguous; never hide unresolved specialist failures. Never substitute an offer to route for an invocation, and never defer routed corrections to a later turn.
    - **Deliverables:** pipeline plan, skill-routing table, stage inputs and outputs, verification gates, risk register, and final integration checklist.
    - **Source freshness:** consult [the authoritative source registry](references/authoritative-sources.md) and the selected specialists' registries before fixing interfaces.
    

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