# Aims Okf Pr Review

> Review changes touching OKF, generated knowledge content, adapter code, skills, docs, or CI.

- Skill: `dceoy/aims-okf-pr-review` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dceoy/aims-okf-pr-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dceoy/aims-okf-pr-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dceoy (https://skillmd.com/u/dceoy)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dceoy/aims-okf-pr-review

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# 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>