aims-okf-site

name	aims-okf-site
description	Generate Hugo shadow content from OKF and troubleshoot Hugo publication.
disable-model-invocation	false

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 from okf/.
• 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