| 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.
- Do not hand-edit
content/knowledge/; regenerate it from okf/.
- Keep custom code small and deterministic.
OKF primary references