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Documentation Index

Fetch the complete documentation index at: https://docs.speckle.systems/llms.txt

Use this file to discover all available pages before exploring further.

These guides show how Speckle capabilities fit together to produce real outcomes. Capabilities describe what Speckle can do; the pages in this section show how to combine them in practice.
The Speckle website also uses Workflows for high-level solution areas—for example project dashboards and progress tracking, present and review models, and exchange design data. Here, each page is a detailed, repeatable workflow you can follow in the product.

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Common foundation

All workflows in this section assume a shared baseline for how data is stored and evolved:
Foundation: Data lives in Projects, Models, and Versions. Versions are immutable and form a complete history. Work proceeds by reading data, transforming it as needed, and publishing new versions.

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What is a workflow?

A workflow in this section is not a single feature or connector. It is a repeatable pattern that combines multiple capabilities to achieve a specific outcome in a defined context. Each workflow answers:
  • What outcome does this achieve?
  • What capabilities are composed?
  • In what context is this used?

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Workflow guides

Start from the outcome you want, not only the tool you use. Each guide below follows a pattern (observation, evaluation, transformation, extraction, or reconstruction): the first sentence describes the pattern; the rest is what that guide walks through. These are composable—you might chain evaluation into reconstruction, or transformation before extraction.

Design review

Observation — Understand and review model data without modifying it. Saved views, federation, markups, and issues for structured design reviews.

Model validation

Evaluation — Assess model data against rules or criteria to produce signal. Intelligence dashboards with Property Checker and Model Validation against your rulesets.

Grasshopper componentised pipeline

Transformation — Modify, restructure, or derive new data from existing models. Split large definitions into modules that hand off data through Speckle models.

Revit room data extraction

Extraction — Use model data outside Speckle for reporting, analytics, or integration. Pull room data from Revit with SpecklePy for Pandas, Power BI, or other systems.

SpecklePy model analytics

Extraction — Load model datasets into local DuckDB with SpecklePy and run repeatable analytics queries.

Mapping to native systems

Reconstruction — Reinterpret Speckle data into native elements in a target system. Map geometry and data to native families and types. Coming soon.
Last modified on May 8, 2026