dceoy AIMS OKF PR review
Check that okf/ is canonical, content/knowledge/ is generated, generated content is up to date, and no vector database, external RAG service, custom CMS, or server runtime was introduced.
AIMS paths and commands
- Canonical OKF source:
okf/ - Generated Hugo shadow content:
content/knowledge/ - Daily report output:
content/results/ - Numeric analysis artifacts:
data/analysis/*.json - Generate:
uv run python tools/okf_hugo_adapter.py --src okf --dst content/knowledge --clean - Check:
uv run python tools/okf_hugo_adapter.py --src okf --dst content/knowledge --check - Build:
hugo --gc --minify
Guardrails
- Do not let LLM-authored OKF prose become the source of truth for scores, ranks, dates, prices, risk gates, or data availability.
- Reject legacy
timestamp, body-level# Citations, non-v0.2 lifecycle values, and dropped or flattened OKF v0.2 metadata. - Do not hand-edit
content/knowledge/; regenerate it fromokf/. - Keep custom code small and deterministic.
OKF primary references
- Google Cloud announcement: https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing
- OKF v0.2 specification: https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md