Guides Python3 programs that access databases and filesystems to detect possible personal or sensitive data using regex and ML, aligned with LGPD, GDPR and similar norms. Use when implementing or reviewing PII/sensitive data detection, compliance scanning,…
Use when deciding how to run lint, tests, commit/PR, or Docker homelab steps. Prefer repo scripts (check-all, lint-only, quick-test, commit-or-pr, docker-lab-build, docker-hub-pull, docker-prune-local) over ad-hoc commands to save tokens and keep behaviour…
Checklist for enforcing NASA-style active voice, LCM Level 1/2 verbs, and canonical Data Boar lexicon stability in code, docs, commit messages, and PR reviews. Use when writing or reviewing docstrings, module comments, PowerShell CBH, Python function names,…
Apply when implementing or reviewing Python/markdown for quality and security. Ensures SonarQube-style rules (S3981, S3776, S4423, S5706, S1192) and CodeQL-relevant patterns are followed, and that the project quality tests are run and pass.
Bounded EXIF/metadata and optional Tesseract OCR for one media file via scripts/image-inspect.ps1 and scripts/image_inspect.py (same stack as connectors/rich_media_sample).
Extract sparse PNG frames from operator video on Windows using scripts/video-frame-samples.ps1 and ffprobe; use L1-L3 taxonomy in docs/ops/VIDEO_FRAME_EXTRACT_FOR_AGENT.md.
Use when editing docs/plans (PLANS_TODO, PLANS_HUB, PLAN_*), docs/MAP*, docs/README hub rows, docs/adr index, or AGENTS ADR pointer—keep hubs aligned with repo truth and ADR policy.
After local Data Boar Docker smoke tests, keep at most one or two named containers, reuse one local image tag (e.g. data_boar:lab) instead of many smoke-* tags, prune old images, and document cleanup. Use when creating containers, running lab validation, or…