dekc-grade

name	dekc-grade
description	Grade reverse-engineering quality with automated rubric scores and adversarial judge protocol. Use after walks, before claiming RE complete.

DEKC grade (adversarial RE quality)

Automated baseline

python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_grade.py" --repo . --bundle knowledge
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_grade.py" --repo . --bundle knowledge --json
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_grade.py" --repo . --bundle knowledge --write

Default scores DEKC nouns only (29 types). Mixed SAC+PKC brains (~15k Module/Function nodes) will not dominate.

# Walk-scoped
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_grade.py" --repo . --bundle knowledge \
  --prefix semantic,tables/gold-,reports,dashboards,ingestion
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_grade.py" --repo . --bundle knowledge --tag fabric-walk-2026-08-24
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_doctor.py" --repo . --bundle knowledge   # DEKC technical
python3 "${CLAUDE_PLUGIN_ROOT}/scripts/dekc_doctor.py" --repo . --bundle knowledge --all  # bundle-wide OKF

Dict-valued description fields (bad YAML / mixed bundles) are coerced; grade does not traceback.

Adversarial loop (required for acceptance)

1. Spawn lineage-skeptic, business-skeptic, stream-job-skeptic, coverage-skeptic
2. Aggregate with re-adversary-judge against evaluation/reverse-engineering-rubric.md (threshold 0.75)
3. On fail → orchestrator revises (capture evidence or retract claims) → re-grade
4. On pass → dekc_index.py build

Orchestrators: data-lake-walker, reverse-engineering-orchestrator.

Rubrics

• evaluation/reverse-engineering-rubric.md
• evaluation/lineage-integrity-rubric.md
• evaluation/business-fidelity-rubric.md
• evaluation/stream-job-landing-rubric.md