aims-okf-pr-review

name	aims-okf-pr-review
description	Review changes touching OKF, generated knowledge content, adapter code, skills, docs, or CI.
disable-model-invocation	false

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 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