Extract PRD content from WS-BPEL 2.0 sources into JSON summaries.
npx skills add https://github.com/megamanics/bpel-agent --skill bpel-prd
Please help me install this Agent Skill. Skill: bpel-prd Source: https://github.com/megamanics/bpel-agent/tree/main/skills/bpel-prd Command: npx skills add https://github.com/megamanics/bpel-agent --skill bpel-prd
Analyze Oracle BPEL processes and produce comprehensive, implementable PRDs that enable re-implementation in modern stacks (Python/Temporal, Node.js/Camunda, Go/Cadence) with full feature parity. Use when transforming BPEL XML, extracting business logic from SOA processes, or generating implementation-ready specs from WS-BPEL 2.0 sources.
Provide the BPEL XML between <<<BPEL>>> and <<<END>>>, then run the transformer to produce a complete PRD.
High-intent search queries and answers about installing and using this skill.
To convert Oracle BPEL processes into a PRD, provide the WS-BPEL 2.0 XML source to extract business logic, data flows, fault handling, and external integrations, generating a comprehensive Markdown PRD for modern stacks.
Extracting business logic from SOA processes is best achieved by analyzing the BPEL XML to map explicit data flows and compensation sets, yielding machine-readable JSON summaries and zero-ambiguity narratives for Python or Node.js migration.
Yes, this BPEL transformation explicitly supports WS-BPEL 2.0 sources, analyzing fault handling, compensation, correlation sets, and external integrations to ensure full feature parity when re-implementing workflows.
You can generate machine-readable JSON summaries from BPEL XML by processing the source between defined markers, structuring the extracted data contracts and workflow logic for direct use in automation pipelines.
To ensure zero ambiguity when migrating BPEL workflows, the transformation documents explicit gaps and assumptions, delivering comprehensive data contracts and implementation-ready specs for target stacks like Go or Camunda.
When transforming BPEL processes with missing data flows, the transformation addresses limitations by explicitly documenting gaps and assumptions, ensuring the resulting PRD maintains zero ambiguity despite incomplete source integrations.
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