dceoy AIMS OKF site generation
Run uv run python tools/okf_hugo_adapter.py --src okf --dst content/knowledge --clean, then hugo --gc --minify. Keep content/results/ and content/knowledge/ separate in URLs and navigation.
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.
- Preserve OKF v0.2 provenance, trust, lifecycle, freshness, attested-computation, and extension fields as structured Hugo 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