hunter-log

To save product discovery pipeline output as Markdown files, you persist structured JSON into an Obsidian vault using automated frontmatter management and cross-linking. This ensures data integrity and discoverability for signal scans, decisions, personas, and offer specifications.

Markdown frontmatter management for product decision logging works by applying predefined schemas to structured pipeline outputs. This automatically organizes file paths, tags, and cross-links within your vault, creating a traceable provenance chain between signals, decisions, and personas.

The best way to track a product discovery pipeline's progress in Obsidian is by updating session logs and Kanban boards automatically. This provides real-time visibility into your workflow as you save scans, decisions, and offer specifications as structured Markdown files.

Yes, you need a predefined vault structure to log decisions and signal scans. Adhering to established frontmatter schemas and file path conventions is required to maintain data integrity, manage cross-linking, and ensure reliable persistence of your pipeline outputs.

Yes, you can use cross-linking to connect signal scans with subsequent decision logs. When a signal scan is saved, subsequent skills like decision-log can reference it, establishing a traceable provenance chain across personas and offer specifications.

Automated persistence requires specific frontmatter schemas for Markdown files to guarantee data integrity and discoverability. Without these predefined structures, the system cannot reliably manage file paths, tags, and cross-links for your product discovery pipeline outputs.