What problem does it solve?
Activepieces steps return raw JSON that renders as an unstructured dump in the flow builder's data selector, making it hard for users and AI/MCP consumers to find the fields that matter. This Skill turns that raw output into a curated, labelled, typed tree by authoring outputSchema definitions grounded in real captured data.
Core Features & Use Cases
- Schema authoring from real output: Captures actual step output via the builder Test Step or the test-step API, then writes curated schemas in
output-schemas.ts with labels, formats, and relative paths.
- Full workflow coverage: Scopes which actions/triggers need schemas, handles top-level arrays, dynamic-key maps, shared field-sets, and wires schemas into actions, triggers, or trigger factory maps.
- Validation and release hygiene: Verifies every path resolves against captured JSON, bumps piece patch versions, rebuilds dev pieces, and runs lint checks.
- Use Case: A contributor adds a new action to a community piece and wants its output to render as a friendly tree in the builder; the Skill runs the step against a live connection, captures the JSON, and authors a validated schema.
Quick Start
Ask the AI to generate outputSchema for a specific Activepieces piece, providing the piece name and a working connection to use for capturing real step output.