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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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--- description: Perform GRADE quality assessment on a research source category: research-quality --- # Quality Assess Command Perform a GRADE evidence quality assessment on a research source or finding. ## Instructions When invoked, perform systematic GRADE assessment: 1. **Load Source** - Accept REF-XXX identifier or file path - Load frontmatter metadata - Determine source type and baseline quality 2. **Apply GRADE Framework** - Evaluate 5 downgrade factors (bias, inconsistency, indirectness, imprecision, publication bias) - Evaluate 3 upgrade factors (large effect, dose-response, confounding) - Calculate final GRADE level 3. **Generate Assessment** - Fill assessment template per @.aiwg/research/docs/grade-assessment-guide.md - Include hedging language recommendations - Include applicability notes for AIWG context 4. **Save Assessment** - Save to `.aiwg/research/quality-assessments/{ref-id}-assessment.yaml` - Update frontmatter in source document if --update-frontmatter 5. **Report** - Display GRADE level with confidence statement - Show allowed vs forbidden hedging language - Flag any existing citations that violate the assessed quality level ## Arguments - `[ref-id or file-path]` - Source to assess (required) - `--output [yaml|markdown]` - Output format (default: yaml) - `--update-frontmatter` - Update source document frontmatter with assessment - `--check-citations` - Also check existing citations of this source for GRADE compliance ## References - @.aiwg/research/docs/grade-assessment-guide.md - GRADE methodology - @agentic/code/frameworks/sdlc-complete/agents/quality-assessor.md - Quality Assessor agent - @agentic/code/frameworks/sdlc-complete/schemas/research/quality-dimensions.yaml - Quality schema - @.claude/rules/citation-policy.md - Hedging language rules