What problem does it solve?
It prevents research entries from becoming inconsistent or untraceable by defining a canonical structure, frontmatter schema, linking conventions, and provenance requirements for everything under the repo’s research corpus.
Core Features & Use Cases
- Standardized research lifecycle layout: enforces the question → sources → extracts → graphs → synthesis → decision → conclusion progression using stable directory and filename conventions.
- Machine-discoverable metadata: requires consistent YAML frontmatter fields (like
id, title, type, status, confidence, sources, related) so tooling can index and traverse entities reliably.
- Provenance-first claim discipline: mandates that claim-bearing docs trace back to
sources:, with confidence rules for uncitable or inferred content.
- Linking and identifier conventions: defines wiki-style
[[id]] linking plus frontmatter parallel references, including ULID/slug rules and deterministic cross-reference behavior.
- Validation and lifecycle governance: provides explicit status and confidence lifecycles, plus validation rules to catch unresolved references and schema mismatches early.
Quick Start
Ask the AI to validate or create a new research file against the research-standard by confirming the required frontmatter keys, identifier rules, and that every claim-bearing section includes valid sources: references.
