aiwg/agentic/code/frameworks/research-complete/inception/solution-profile.md

Deployment tool and support utility for AI context. Copies agents, skills, commands, rules, and behaviors into the paths each AI platform reads (Claude Code, Codex, Copilot, Cursor, Warp, OpenClaw, and 6 more) so one source of truth works across 10 platfo

# Solution Profile: AIWG Research Framework

**Framework ID**: research-complete
**Version**: 1.0.0
**Date**: 2026-01-25
**Status**: Inception
**Document Type**: Solution Profile

## References

- @.aiwg/research/research-framework-findings.md - Comprehensive research foundation
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md - SDLC framework patterns
- @$AIWG_ROOT/agentic/code/frameworks/media-marketing-kit/README.md - Media curator patterns

---

## 1. Solution Overview

### 1.1 Vision

The AIWG Research Framework provides AI coding assistants with specialized workflows, agents, and artifact management for managing research through its full lifecycle: discovery, acquisition, documentation, integration, and archival. It transforms ad-hoc research management into a structured, reproducible, FAIR-compliant process.

### 1.2 Problem Statement

**Current State**:
- Manual PDF management with no systematic discovery or validation
- Ad-hoc research organization lacking provenance tracking
- No automated gap analysis or quality assessment
- Claims lack citations and backing evidence
- Research workflows not reproducible
- No systematic progression from literature review to knowledge synthesis

**Pain Points Identified**:
- matric-memory professionalization (Issue #154, #155): documentation needs citations
- matric-eval benchmarking: requires research corpus of evaluation papers
- Research-papers repo: lacks automated discovery and integration tools
- No quality scoring mechanism for sources
- Manual citation network analysis

### 1.3 Proposed Solution

A complete framework (`agentic/code/frameworks/research-complete/`) providing:

1. **Structured Lifecycle Management**: PRISMA-inspired discovery through OAIS-compliant archival
2. **Specialized Agent System**: 8+ agents for discovery, acquisition, documentation, citation, quality assessment, provenance tracking, archival, and workflow orchestration
3. **Comprehensive Artifact Structure**: `.aiwg/research/` directory with discovery protocols, source metadata, knowledge graphs, quality assessments, provenance logs, and workflow definitions
4. **Quality Assurance**: GRADE-inspired evidence scoring, FAIR compliance validation, reproducibility tracking
5. **AI-Powered Capabilities**: LLM-assisted summarization (LitLLM patterns), citation network analysis, automated gap detection, knowledge graph construction

### 1.4 Success Vision

- Researchers spend less time on manual processes, more time on synthesis
- Every claim backed by traceable, quality-assessed sources
- Research workflows fully reproducible by others
- Automated discovery surfaces relevant sources proactively
- Knowledge graphs reveal connections not visible in isolation
- FAIR principles enforced automatically, not as afterthought

---

## 2. Target Users & Personas

### 2.1 Primary Persona: Academic Researcher

**Context**: Graduate student or faculty conducting literature reviews for papers, dissertations, or grant proposals

**Needs**:
- Systematic discovery of relevant papers (PRISMA protocol)
- Quality assessment of evidence (GRADE scoring)
- Citation network analysis for comprehensive coverage
- Reproducible search strategies for transparent reporting
- Export to reference managers (Zotero, BibTeX)

**Success Metrics**:
- 60%+ reduction in manual screening time (per LLM review research)
- >90% recall on relevant papers
- All sources FAIR-compliant with provenance
- Literature review replicable by independent researcher

### 2.2 Secondary Persona: AI Coding Assistant

**Context**: Claude Code, GitHub Copilot, or similar agents performing development tasks requiring research backing

**Needs**:
- Quick access to relevant research for claims
- Automated citation formatting and integration
- Gap analysis: "What research is missing for this feature?"
- Quality scoring: "Is this source credible?"
- Knowledge graph: "What concepts relate to this implementation?"

**Success Metrics**:
- Every architectural decision backed by research
- Claims linked to quality-scored sources
- Automated gap detection surfaces missing research
- Knowledge graph reveals implementation patterns from literature

### 2.3 Tertiary Persona: Documentation Specialist

**Context**: Technical writer or documentation maintainer professionalizing informal docs (matric-memory use case)

**Needs**:
- Terminology mapping (informal to professional)
- Citation integration for claims
- Multi-audience documentation levels (informal to academic)
- Automated literature note generation
- Reference standardization

**Success Metrics**:
- All professional claims cited
- Terminology glossary backed by authoritative sources
- Documentation meets academic publication standards
- Citations formatted consistently across corpus

### 2.4 Persona Priorities

| Priority | Persona | Key Value |
|----------|---------|-----------|
| 1 | Academic Researcher | Reproducible systematic reviews |
| 2 | AI Coding Assistant | Research-backed development |
| 3 | Documentation Specialist | Professional citation integration |

---

## 3. Key Capabilities

### 3.1 Discovery (PRISMA-Style)

**Capability**: Systematic, reproducible research discovery

**Features**:
- Semantic Scholar API integration for 200M+ papers
- PRISMA-inspired search strategy templates
- Explicit eligibility criteria definition
- Relevance ranking with LLM assistance
- Automated gap detection via citation network analysis
- Preregistration support (open science best practice)

**Artifacts**:
- `.aiwg/research/discovery/search-strategies/` - Reproducible queries
- `.aiwg/research/discovery/screening-results/` - Decisions with rationale
- `.aiwg/research/discovery/gap-analysis/` - Automated reports
- `.aiwg/research/discovery/preregistration/` - Research plans

**Research Basis**:
- PRISMA Statement (27-item checklist)
- LLM-enhanced systematic reviews (60%+ screening reduction)
- Semantic Scholar, Research Rabbit, Litmaps tools

### 3.2 Acquisition (PDF Download, Metadata, FAIR Validation)

**Capability**: Automated, FAIR-compliant source acquisition

**Features**:
- PDF download from open access repositories
- Metadata extraction (authors, DOI, abstract, citations)
- FAIR compliance validation (F-UJI pattern)
- Persistent identifier assignment (REF-XXX, DOI linking)
- Integrity verification (checksums)
- Zotero integration for reference management

**Artifacts**:
- `.aiwg/research/sources/pdfs/` - Original files
- `.aiwg/research/sources/REF-XXX-metadata.json` - FAIR-compliant metadata
- `.aiwg/research/sources/REF-XXX-quality.json` - Quality scores
- `.aiwg/research/provenance/checksums.json` - Integrity verification

**Research Basis**:
- FAIR Principles (Findable, Accessible, Interoperable, Reusable)
- FAIRification process (Bhat & Wani 2025)
- F-UJI assessment tool patterns
- Zotero metadata extraction (9,000+ citation styles)

### 3.3 Documentation (Summaries, Extraction, Zettelkasten Notes)

**Capability**: AI-assisted knowledge extraction and note-taking

**Features**:
- LLM-powered summarization (LitLLM pattern, RAG-based to reduce hallucination)
- Structured data extraction from papers
- Literature notes (processed insights with attribution)
- Permanent notes (refined ideas building on literature)
- Progressive summarization (distill core insights)
- Maps of Content (MoCs) for topic organization

**Artifacts**:
- `.aiwg/research/sources/REF-XXX-summary.md` - AI summaries
- `.aiwg/research/sources/REF-XXX-extraction.json` - Structured data
- `.aiwg/research/knowledge/literature-notes/` - Processed insights
- `.aiwg/research/knowledge/permanent-notes/` - Refined ideas
- `.aiwg/research/knowledge/maps-of-content/` - Topic MoCs

**Research Basis**:
- Zettelkasten Method (atomic notes, bidirectional linking, emergence)
- Building a Second Brain / CODE cycle (Collect, Organize, Distill, Express)
- LitLLM (RAG-based review generation, no hallucinated sources)
- Elicit (99.4% extraction accuracy)
- Obsidian PKM patterns (8,000 notes / 64,000 links scale)

### 3.4 Integration (Citations, Knowledge Graphs)

**Capability**: Knowledge synthesis and application

**Features**:
- Citation formatting and integration (9,000+ styles via Zotero)
- Citation context analysis (supported/contradicted via Scite pattern)
- Knowledge graph construction (concepts, papers, authors)
- Citation network visualization (co-citation, bibliographic coupling)
- Automated claim backing (link claims to sources)
- Concept relationship discovery via LLMs

**Artifacts**:
- `.aiwg/research/knowledge/claims-index.md` - Claims needing/having citations
- `.aiwg/research/networks/citation-network.json` - Citation relationships
- `.aiwg/research/networks/concept-graph.json` - Knowledge graph
- `.aiwg/research/networks/author-network.json` - Collaboration networks
- BibTeX/RIS export for reference managers

**Research Basis**:
- VOSviewer / CitNetExplorer (bibliometric network analysis)
- Scite (citation classification: supported/contradicted/mentioned)
- Knowledge Graph + LLM integration (300-320% ROI on KG implementations)
- Connected Papers (co-citation analysis)
- Litmaps (citation chain visualization)

### 3.5 Archival (OAIS-Compliant, Versioning)

**Capability**: Long-term digital preservation

**Features**:
- OAIS-inspired information packages (SIP/AIP/DIP equivalents)
- Version control for all research artifacts
- Backup and integrity verification
- Preservation planning (format migration monitoring)
- Lifecycle management (active to archived)
- Export packages for repositories

**Artifacts**:
- `.aiwg/research/workflows/reproducibility-packages/` - Containerized environments
- `.aiwg/research/provenance/transformations/` - Artifact history
- `.aiwg/research/config/lifecycle-rules.yaml` - Automation rules
- Versioned exports (ZIP, tar.gz with checksums)

**Research Basis**:
- OAIS Reference Model (ISO 14721:2025)
- OAIS v3 (December 2024 revision)
- ISO 16363 (Trustworthy digital repository certification)
- DCC Lifecycle Model

---

## 4. Technical Architecture Summary

### 4.1 Agent Specializations (8+ Agents)

| Agent | Lifecycle Stage | Key Capabilities | Technologies |
|-------|----------------|------------------|--------------|
| **Discovery Agent** | Discovery | API search, PRISMA protocol, gap detection, preregistration | Semantic Scholar API, LLM ranking |
| **Acquisition Agent** | Acquisition | PDF download, metadata extraction, FAIR validation | Zotero patterns, F-UJI |
| **Documentation Agent** | Documentation | Summarization, extraction, literature/permanent notes | LitLLM (RAG), Zettelkasten |
| **Citation Agent** | Integration | Formatting, context analysis, claim backing | Scite patterns, BibTeX |
| **Archival Agent** | Archival | Version control, OAIS compliance, preservation | OAIS model, checksums |
| **Quality Agent** | Cross-cutting | GRADE scoring, FAIR assessment, reproducibility checks | GRADE framework, F-UJI |
| **Provenance Agent** | Cross-cutting | W3C PROV tracking, lineage graphs, audit trails | W3C PROV, ULS patterns |
| **Workflow Agent** | Cross-cutting | Pipeline orchestration, DAG management, containers | Snakemake/Nextflow patterns |

**Additional Specialized Agents** (as needed):
- Knowledge Graph Agent: Graph construction, community detection, centrality analysis
- Gap Analysis Agent: Systematic gap identification, prioritization
- Screening Agent: LLM-assisted eligibility screening (60%+ time reduction)

### 4.2 Artifact Structure (.aiwg/research/)

```
.aiwg/research/
├── discovery/
│   ├── search-strategies/           # PRISMA-style reproducible queries
│   ├── screening-results/           # Screening decisions with rationale
│   ├── gap-analysis/                # Automated gap detection reports
│   └── preregistration/             # Research plans (open science)
├── sources/
│   ├── pdfs/                        # Original source files
│   ├── REF-XXX-metadata.json        # FAIR-compliant structured metadata
│   ├── REF-XXX-summary.md           # AI-generated summaries (LitLLM pattern)
│   ├── REF-XXX-extraction.json      # Structured data extraction
│   └── REF-XXX-quality.json         # GRADE-inspired quality scores
├── knowledge/
│   ├── literature-notes/            # Processed insights (Zettelkasten)
│   ├── permanent-notes/             # Refined ideas
│   ├── maps-of-content/             # Topic MoCs
│   └── claims-index.md              # Claims needing/having citations
├── networks/
│   ├── citation-network.json        # Citation relationships
│   ├── concept-graph.json           # Knowledge graph
│   └── author-network.json          # Collaboration networks
├── analysis/
│   ├── quality-scores.md            # Source quality assessments
│   ├── fair-compliance.md           # FAIR compliance reports
│   └── reproducibility-status.md    # Replication readiness
├── provenance/
│   ├── operations.log               # All research operations (W3C PROV)
│   ├── lineage/                     # Data lineage graphs
│   ├── transformations/             # Artifact transformation history
│   └── checksums.json               # Integrity verification
├── workflows/
│   ├── pipelines/                   # Research workflow definitions
│   ├── dag-visualizations/          # Process graphs
│   └── reproducibility-packages/    # Containerized environments
└── config/
    ├── search-config.yaml           # Search preferences
    ├── quality-criteria.yaml        # GRADE-based evaluation rules
    ├── fair-requirements.yaml       # FAIR compliance rules
    └── lifecycle-rules.yaml         # Automation rules
```

### 4.3 Integration Points

| External System | Integration Type | Purpose | Implementation |
|-----------------|------------------|---------|----------------|
| Semantic Scholar | REST API | Paper search, citation data | JSON responses, rate limiting |
| Zotero | File import/export | PDF management, metadata | BibTeX/RIS interchange |
| Obsidian | Markdown sync | PKM integration, note templates | Bidirectional linking, graph view |
| research-papers repo | Git submodule/reference | Shared corpus management | Cross-project references |
| Knowledge Graph DB | JSON export | Concept relationships | Neo4j/RDF export optional |
| CI/CD | GitHub Actions | Automated pipelines | Workflow definitions |
| BibTeX/RIS | File export | Reference manager export | Standard formats |

### 4.4 Technology Stack

**Core**:
- Markdown + YAML for human-editable artifacts
- JSON for structured data (metadata, extraction, networks)
- Git for version control and provenance
- LLM (Claude) for summarization, extraction, analysis

**External APIs**:
- Semantic Scholar API (discovery)
- Scite API (citation context) - optional
- DOI resolution services (CrossRef)

**Optional Integrations**:
- Zotero command-line tools
- Neo4j for knowledge graph queries
- Snakemake/Nextflow for workflow orchestration
- Docker for reproducibility packages

---

## 5. Differentiators vs. Media Curator Framework

| Aspect | Media Curator | Research Framework | Impact |
|--------|---------------|--------------------|--------|
| **Methodology** | Casual discovery | PRISMA-inspired protocol with preregistration | Reproducible, transparent research |
| **Quality Assessment** | Basic validation | GRADE-inspired multi-dimensional scoring | Evidence-based confidence levels |
| **Discovery** | Manual search | Automated via APIs + gap analysis | Comprehensive coverage |
| **Linking** | Manual tagging | AI-powered citation networks + knowledge graphs | Emergence of hidden connections |
| **Gap Analysis** | Manual review | Automated detection with prioritization | Proactive research planning |
| **Provenance** | Basic tracking | W3C PROV-compatible with full lineage | Audit trail for reproducibility |
| **Note-Taking** | Flat structure | Zettelkasten (literature + permanent notes) | Knowledge synthesis |
| **Validation** | Basic checks | Simulation-based verification + FAIR compliance | Research integrity |
| **Workflows** | Ad-hoc | Snakemake/CWL-style reproducible pipelines | Process repeatability |
| **Output Formats** | Single format | Multiple (Obsidian, BibTeX, OAIS packages) | Interoperability |
| **Target Audience** | Content curators | Academic researchers, developers | Professional standards |
| **Lifecycle** | Acquisition → Organization | Discovery → Archival (5 stages) | Comprehensive management |

**Key Distinction**: Media Curator is for organizing found content; Research Framework is for systematic discovery, quality assessment, and knowledge synthesis following academic best practices.

---

## 6. Success Metrics

### 6.1 Efficiency Metrics

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Screening time reduction | 60%+ | Compare manual vs. LLM-assisted screening |
| FAIR compliance rate | 100% | Automated F-UJI assessment on all sources |
| Gap detection recall | >90% | Expert validation of automated gap reports |
| Citation formatting accuracy | 99%+ | Comparison to reference managers |
| Search reproducibility | 100% | Independent researcher can replicate results |

### 6.2 Quality Metrics

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| GRADE quality scores | >80% high/moderate | Track score distribution across corpus |
| Provenance completeness | 100% | All operations logged per W3C PROV |
| Citation backing | 100% of claims | Claims-index.md shows all citations |
| Knowledge graph density | ~8 links/note | Following Zettelkasten best practices |
| Reproducibility status | >90% replicable | Track pipelines with containerization |

### 6.3 Coverage Metrics

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Sources with metadata | 100% | All REF-XXX have metadata.json |
| Sources with summaries | 100% | All REF-XXX have summary.md |
| Citation network coverage | >95% | Graph includes all cited/citing papers |
| Gap analysis coverage | All major topics | MoCs map to gap reports |

### 6.4 User Satisfaction Metrics

| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| Time to find relevant paper | <5 min | User testing vs. manual search |
| Confidence in source quality | High | Survey of quality score utility |
| Ease of citation integration | High | Survey of claim backing workflow |
| Framework learning curve | <1 hour | Time to proficiency on basic tasks |

---

## 7. Risk Factors

### 7.1 Technical Risks

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| **LLM Hallucination** | High | High | Use RAG-based tools (LitLLM pattern), require citation backing, human verification for critical extractions |
| **API Rate Limits** | Medium | Medium | Implement caching, rate limiting, fallback to manual search |
| **PDF Extraction Failures** | Medium | Medium | Multiple extraction methods, manual fallback, validate extracted metadata |
| **Knowledge Graph Complexity** | Medium | Low | Start with simple citation networks, progressive enhancement to full KG |
| **FAIR Compliance Overhead** | Low | Medium | Automate compliance checks, provide templates, incremental FAIRification |

### 7.2 Process Risks

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| **Learning Curve** | High | Medium | Provide templates, quick-start guides, incremental adoption |
| **Over-Engineering** | Medium | Medium | Start with MVP (discovery + acquisition), add stages incrementally |
| **Workflow Disruption** | Medium | High | Integrate with existing tools (Zotero, Obsidian), don't force migration |
| **Maintenance Burden** | Low | Medium | Automate updates (gap analysis, quality rescoring), scheduled reviews |

### 7.3 Adoption Risks

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| **Too Academic for Developers** | Medium | High | Provide simplified workflows for dev use cases, hide complexity |
| **Too Informal for Academics** | Low | Medium | Follow PRISMA/GRADE standards, export to academic formats |
| **Requires Manual Effort** | High | High | Maximize automation (API discovery, LLM summarization, gap detection) |
| **Corpus Size Scalability** | Medium | Low | Test at scale (8K+ notes), optimize graph queries, provide pruning tools |

### 7.4 Research Reproducibility Risks

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| **Incomplete Provenance** | Low | High | Automate provenance tracking, log all operations, checksums for integrity |
| **External Dependency Changes** | Medium | Medium | Version pin external tools, containerize workflows, document alternatives |
| **Search Result Drift** | Low | Medium | Timestamp searches, archive query results, use persistent IDs (DOIs) |
| **Format Obsolescence** | Low | Low | Use standard formats (Markdown, JSON, BibTeX), OAIS preservation planning |

### 7.5 Quality Assurance Risks

| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| **Low-Quality Sources** | Medium | High | GRADE scoring, automated quality checks, human review for critical sources |
| **Citation Errors** | Low | Medium | Validate against DOI/metadata, use established tools (Zotero) |
| **Gap Analysis Blind Spots** | Medium | Medium | Multiple gap detection methods, expert review, community feedback |
| **Bias in LLM Assistance** | Medium | Medium | Transparent prompts, human verification, diverse training data |

### 7.6 Risk Summary

**Highest Priority Risks** (High Likelihood AND High Impact):
1. LLM Hallucination - Mitigate with RAG patterns and human verification
2. Requires Manual Effort - Maximize automation at every stage

**Medium Priority Risks** (Either High):
3. Workflow Disruption - Integrate with existing tools
4. Incomplete Provenance - Automate tracking
5. Low-Quality Sources - GRADE scoring and validation

**Monitor** (Lower priority but track):
- API rate limits, PDF extraction, learning curve, corpus scalability

---

## 8. Implementation Strategy

### 8.1 MVP Scope (Phase 1)

**Goal**: Prove value with minimal viable capabilities

**Included**:
- Discovery Agent: Semantic Scholar API search, basic gap detection
- Acquisition Agent: PDF download, metadata extraction, REF-XXX assignment
- Basic artifact structure: discovery/, sources/
- Simple quality scoring (1-5 scale, manual)
- Git-based provenance (commit history)

**Out of Scope**:
- Full GRADE scoring
- Knowledge graph construction
- Workflow orchestration
- OAIS compliance
- LLM summarization (use manual summaries)

**Success Criteria**:
- Can discover papers via API
- Can acquire PDFs and metadata
- Gap analysis identifies missing topics
- Users find value in organized structure

### 8.2 Phase 2: Documentation & Quality

**Added**:
- Documentation Agent: LLM summarization, extraction
- Quality Agent: GRADE-inspired scoring
- FAIR compliance validation
- Literature notes / permanent notes (Zettelkasten)
- Claims-index.md for citation tracking

**Success Criteria**:
- AI summaries reduce reading time
- Quality scores inform source selection
- Zettelkasten notes enable synthesis

### 8.3 Phase 3: Integration & Synthesis

**Added**:
- Citation Agent: Formatting, context analysis, claim backing
- Knowledge Graph Agent: Citation networks, concept graphs
- Integration with Zotero, Obsidian
- BibTeX/RIS export

**Success Criteria**:
- Citations auto-generated and accurate
- Knowledge graph reveals connections
- Integrates with existing research tools

### 8.4 Phase 4: Advanced Workflows

**Added**:
- Workflow Agent: Pipeline orchestration, DAG visualization
- Archival Agent: OAIS compliance, preservation planning
- Reproducibility packages (containerized)
- Full provenance tracking (W3C PROV)

**Success Criteria**:
- Research workflows fully reproducible
- Artifacts preserved long-term
- Audit trail for all operations

---

## 9. Alignment with AIWG Strategy

### 9.1 Framework Ecosystem Position

| Framework | Scope | Target Workflow | Research Framework Fit |
|-----------|-------|-----------------|------------------------|
| sdlc-complete | Software development lifecycle | Requirements → Deployment | Research informs architecture, testing |
| media-marketing-kit | Content creation, marketing | Ideation → Distribution | Different methodology (casual vs. academic) |
| **research-complete** | **Research management** | **Discovery → Archival** | **Foundational for evidence-based development** |

**Positioning**: Research Framework provides the evidence layer underlying SDLC and marketing decisions.

### 9.2 Cross-Framework Integration

**SDLC → Research**:
- Architecture decisions cite research (REF-XXX)
- ADRs backed by evidence
- Non-functional requirements informed by research
- Test strategies based on research best practices

**Research → Marketing**:
- Content backed by authoritative sources
- Thought leadership grounded in research
- White papers with proper citations
- Credibility through evidence

**Marketing → Research**:
- Competitor analysis informs gap analysis
- Audience insights guide research topics
- Content calendar drives research priorities

### 9.3 Strategic Benefits

1. **Differentiation**: Only AI coding framework with academic-grade research management
2. **Credibility**: Evidence-backed development and content
3. **Professionalization**: Enables maturation of informal projects (matric-memory use case)
4. **Efficiency**: Automates tedious research tasks (60%+ time savings)
5. **Reproducibility**: Aligns with open science movement, research crisis solutions

---

## 10. Next Steps

### 10.1 Immediate (Inception Phase)

- [ ] **Stakeholder Review**: Present this solution profile, gather feedback
- [ ] **Use Case Specification**: Create UC-001 through UC-005 for core capabilities
- [ ] **Agent Definitions**: Design 8 core agents with tools and expertise
- [ ] **Architecture Draft**: SAD for framework architecture
- [ ] **Template Design**: Create artifact templates (search strategy, metadata, quality score)

### 10.2 Near-Term (Elaboration Phase)

- [ ] **MVP Implementation**: Phase 1 scope (discovery + acquisition)
- [ ] **API Integration**: Semantic Scholar API client
- [ ] **Artifact Schema**: JSON schemas for metadata, quality scores
- [ ] **Testing Strategy**: Validate on research-papers corpus
- [ ] **Documentation**: User guides, quick-start, examples

### 10.3 Medium-Term (Construction Phase)

- [ ] **Phase 2 Implementation**: Documentation + quality agents
- [ ] **LLM Integration**: Summarization, extraction pipelines
- [ ] **FAIR Validation**: F-UJI-inspired compliance checks
- [ ] **Zettelkasten Support**: Literature/permanent note templates
- [ ] **Gap Analysis**: Automated detection and reporting

### 10.4 Long-Term (Transition Phase)

- [ ] **Phase 3 Implementation**: Integration + synthesis agents
- [ ] **Knowledge Graph**: Citation network + concept graph
- [ ] **Provenance System**: W3C PROV-compatible tracking
- [ ] **Workflow Orchestration**: Reproducible pipeline definitions
- [ ] **OAIS Compliance**: Archival agent and preservation planning

---

## 11. Conclusion

The AIWG Research Framework addresses a critical gap: systematic, reproducible, quality-assured research management for AI-assisted development. By combining traditional methodologies (PRISMA, GRADE, Zettelkasten, OAIS) with modern AI capabilities (LLM summarization, automated gap detection, knowledge graphs), the framework enables researchers and developers to manage research at academic standards while maintaining development velocity.

**Key Value Proposition**: Transform ad-hoc research into structured, reproducible, FAIR-compliant knowledge management that scales from individual developers to academic research teams.

**Differentiation**: Only framework integrating full research lifecycle with AI coding assistants, bridging the gap between casual content curation and academic rigor.

**Strategic Fit**: Provides evidence foundation for SDLC and marketing frameworks, professionalizes informal projects, aligns with open science movement.

**Risk Mitigation**: Incremental adoption (MVP to full phases), automation to reduce manual burden, integration with existing tools to minimize disruption.

**Next Step**: Stakeholder review of this profile, then proceed to detailed use case and agent specifications.

---

**Document Status**: Draft for Review
**Reviewers**: Project Stakeholders, Business Process Analyst, Product Strategist
**Automation Output**: Solution profile ready for use case elaboration