{
  "markdown": "# aims\n\nAI Market Strategist\n\n[![CI](https://github.com/dceoy/aims/actions/workflows/ci.yml/badge.svg)](https://github.com/dceoy/aims/actions/workflows/ci.yml)\n\n## Static analysis site\n\nAnalysis results are saved as Hugo content under `content/results/` and rendered to the ignored `site/` directory.\nThe site uses the Congo Hugo theme via Hugo Modules, so local Hugo builds require a Go toolchain in addition to Hugo Extended.\n\nCreate a new result:\n\n```bash\nhugo new results/YYYY-MM-DD-market-analysis.md\n```\n\nSet `draft = false` in the generated front matter when the result is ready to publish, then build the static site:\n\n```bash\nhugo --gc --minify\n```\n\nPreview drafts locally with:\n\n```bash\nhugo server --buildDrafts\n```\n\n## Automated analysis pipeline\n\nDaily market analysis runs automatically via `.github/workflows/daily-market-analysis.yml`. It fetches market data, scores instruments, generates JSON artifacts and Hugo Markdown reports, builds the site, and creates a pull request with the results for review and merging.\n\nSee [OPERATIONS.md](OPERATIONS.md) for data sources, scoring methodology, required secrets, and operational runbooks.\n\n## OKF knowledge layer\n\nAIMS uses an OKF-first knowledge layer for durable repository knowledge. AIMS targets OKF v0.2 as described in the [OKF v0.2 specification](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md) and the [Google Cloud OKF announcement](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing):\n\n- `okf/` is the canonical source for stable concepts about architecture, data sources, scoring methodology, operations, and Agent Skills.\n- `data/analysis/*.json` remains authoritative for numeric market facts, scores, ranks, dates, risk gates, and data availability.\n- `content/results/` remains the public daily market-analysis report output.\n- `content/knowledge/` is generated Hugo shadow content derived from `okf/` and must not be hand-edited.\n- Repository-local Agent Skills under `.agents/skills/` guide OKF authoring, curation, Hugo generation, and PR review.\n\nAfter editing `okf/`, regenerate and validate the Hugo shadow content, then build the site:\n\n```bash\nuv run python tools/okf_hugo_adapter.py --src okf --dst content/knowledge --clean\nuv run python tools/okf_hugo_adapter.py --src okf --dst content/knowledge --check\nhugo --gc --minify\n```\n\nDo not hand-edit `content/knowledge/`. If Markdown formatting or other tooling changes the generated files, fix drift by regenerating from `okf/` with the adapter rather than editing the shadow content directly.\n\nThe adapter deterministically maps OKF reserved `index.md` files to Hugo `_index.md` files, accepts OKF v0.2 reserved files without concept front matter, and moves non-reserved OKF front matter `type` into `params.okf_type` so Hugo can use a stable `type: knowledge` presentation type. Provenance, trust, lifecycle, freshness, attested-computation, and AIMS extension fields are preserved as structured `params.okf_metadata`.\n",
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