aims-okf-author

name	aims-okf-author
description	Create or update canonical OKF concept documents from repository evidence without inventing numeric market facts.
disable-model-invocation	false

AIMS OKF authoring

Author only okf/ files first. Use data/analysis/*.json for numeric facts and cite generated reports only as publication outputs, not canonical facts. Regenerate content/knowledge/ with the adapter before finishing.

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.
• Author OKF v0.2 metadata: use generated: { by, at }, front matter sources, and lifecycle status values draft, stable, or deprecated.
• Do not use legacy timestamp, structured self-referential resource mappings, or body-level # Citations sections.
• 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