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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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--- 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