aiwg
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Cognitive architecture for AI-augmented software development with structured memory, ensemble validation, and closed-loop correction. FAIR-aligned artifacts, 84% cost reduction via human-in-the-loop, standards adopted by 100+ organizations.
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Markdown
---
description: Generate corpus-wide GRADE quality distribution report
category: research-quality
---
# GRADE Report Command
Generate a report on the evidence quality distribution across the research corpus.
## Instructions
When invoked, analyze and report on corpus quality:
1. **Scan Quality Assessments**
- Load all assessments from `.aiwg/research/quality-assessments/`
- Load frontmatter quality fields from all sources
2. **Calculate Distribution**
- Count sources by GRADE level (HIGH, MODERATE, LOW, VERY LOW)
- Count sources by source type
- Identify unassessed sources
3. **Generate Report**
- Summary table: GRADE distribution
- Source type breakdown
- Unassessed sources list
- Hedging compliance summary (overclaiming count)
- Recommendations for corpus improvement
4. **Save Report**
- Display to user
- Optionally save to `.aiwg/reports/grade-report.md`
## Arguments
- `--brief` - Show summary only
- `--unassessed` - Show only unassessed sources
- `--save` - Save report to `.aiwg/reports/grade-report.md`
## References
- @.aiwg/research/docs/grade-assessment-guide.md - GRADE methodology
- @agentic/code/frameworks/sdlc-complete/agents/quality-assessor.md - Quality Assessor
- @agentic/code/frameworks/sdlc-complete/schemas/research/quality-dimensions.yaml - Quality schema