Best use case
research-gap is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Analyze gaps in research coverage
Teams using research-gap should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/research-gap/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How research-gap Compares
| Feature / Agent | research-gap | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze gaps in research coverage
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Research Gap Command
Analyze research corpus coverage gaps and suggest literature to fill them.
## Scope Boundary
`research-gap` is **intellectual** — it analyzes what the corpus is missing in terms of knowledge:
- What topics lack adequate coverage?
- What claims lack supporting evidence?
- What contradictions are unresolved?
- What time periods, source types, or methodologies are underrepresented?
For **structural** health checks — orphan files, broken references, missing frontmatter, schema violations — use `corpus-health` / `research-status` instead.
For **declarative rule checking** (automated, CI-ready), use `research-lint` which runs the `research` lint ruleset.
## Instructions
When invoked, perform systematic gap analysis:
1. **Load Corpus Inventory**
- Scan all papers in `.aiwg/research/`
- Extract topics, themes, publication years
- Build coverage map
2. **Identify Gaps**
- **Topic Gaps** - Underrepresented areas
- **Temporal Gaps** - Missing time periods
- **Source Type Gaps** - Bias toward certain publication types
- **Quality Gaps** - Insufficient HIGH GRADE sources
- **Methodological Gaps** - Missing research approaches
- **Perspective Gaps** - Lack of diverse viewpoints
3. **Analyze Existing Coverage**
- Compare current corpus to AIWG needs
- Identify critical vs nice-to-have gaps
- Assess impact of gaps on framework quality
- Calculate coverage scores by area
4. **Generate Search Queries**
- Suggest specific search queries to fill gaps
- Prioritize by urgency and AIWG relevance
- Include database recommendations
5. **Report Findings**
- Display gap analysis with visualizations
- Prioritize gaps by impact
- Provide actionable recommendations
- Export as markdown report for review
## Arguments
- `[topic]` - Specific topic to analyze (optional, default: all)
- `--suggest-queries` - Generate search queries for gaps
- `--min-papers [n]` - Minimum papers for adequate coverage (default: 5)
- `--critical-only` - Show only critical gaps
- `--export [markdown|json|yaml]` - Export gap analysis report
- `--prioritize-by [impact|urgency|feasibility]` - Prioritization criteria (default: impact)
## Examples
```bash
# Full corpus gap analysis
/research-gap
# Analyze specific topic
/research-gap "agent security"
# Generate search queries for all gaps
/research-gap --suggest-queries
# Show only critical gaps
/research-gap --critical-only
# Export detailed report
/research-gap --export markdown --suggest-queries
```
## Expected Output
### Full Analysis
```
Research Gap Analysis
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Current Corpus: 47 papers
Analysis Date: 2026-02-03T15:00:00Z
Topic Coverage Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Topic Papers Target Status Gap
───────────────────────────────────────────────────────────────────
agentic-workflows 24 15 ✓ ADEQUATE +9
llm-evaluation 18 10 ✓ ADEQUATE +8
human-in-the-loop 14 10 ✓ ADEQUATE +4
multi-agent-systems 12 10 ✓ ADEQUATE +2
cognitive-scaffolding 9 8 ✓ ADEQUATE +1
prompt-engineering 8 8 ✓ ADEQUATE =0
tool-use 7 8 ⚠ MINIMAL -1
reproducibility 6 10 ⚠ SIGNIFICANT -4
fair-principles 5 8 ⚠ SIGNIFICANT -3
test-generation 4 10 ⚠ CRITICAL -6
agent-security 2 10 ⚠ CRITICAL -8
cost-optimization 1 8 ⚠ CRITICAL -7
error-handling 0 8 🚨 MISSING -8
Critical Gaps (Target - Current >= 5):
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Error Handling (Missing: 8 papers)
Priority: CRITICAL
Impact: HIGH - Core framework capability
Rationale: Zero papers on agent error handling patterns,
recovery strategies, or failure modes. This is a
fundamental gap affecting reliability.
Suggested Search Queries:
• "error handling strategies LLM agents"
• "failure recovery agentic systems"
• "agent robustness fault tolerance"
• "graceful degradation AI systems"
Recommended Databases: ACM, IEEE, arXiv
2. Agent Security (Missing: 8 papers)
Priority: CRITICAL
Impact: HIGH - Production readiness requirement
Rationale: Only 2 papers on agent security. Insufficient
coverage of prompt injection, data leakage,
adversarial attacks, sandboxing.
Suggested Search Queries:
• "LLM agent security vulnerabilities"
• "prompt injection attacks defense"
• "agent sandboxing isolation"
• "adversarial robustness language models"
Recommended Databases: arXiv, IEEE S&P, USENIX Security
3. Cost Optimization (Missing: 7 papers)
Priority: CRITICAL
Impact: MEDIUM - Economic viability
Rationale: Only 1 paper on cost management. Need research
on token optimization, caching strategies, model
selection, and cost-performance tradeoffs.
Suggested Search Queries:
• "LLM inference cost optimization"
• "token-efficient prompting strategies"
• "agent computational resource management"
• "cost-effective AI agent deployment"
Recommended Databases: arXiv, MLSys, cloud vendor research
4. Test Generation (Missing: 6 papers)
Priority: HIGH
Impact: MEDIUM - Code quality assurance
Rationale: Only 4 papers on automated test generation.
Need more on LLM-based test creation, coverage
strategies, test quality assessment.
Suggested Search Queries:
• "LLM automated test generation"
• "AI-assisted unit test creation"
• "test case generation language models"
• "intelligent test suite augmentation"
Recommended Databases: ACM, IEEE, ICSE, ISSTA
Significant Gaps (Target - Current 3-4):
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
5. Reproducibility (Missing: 4 papers)
Priority: HIGH
Impact: MEDIUM - Research rigor
Current: 6 papers (need 10)
Suggested Queries:
• "reproducibility LLM agent experiments"
• "deterministic AI system execution"
• "agent workflow reproducibility"
6. FAIR Principles (Missing: 3 papers)
Priority: MEDIUM
Impact: MEDIUM - Data governance
Current: 5 papers (need 8)
Suggested Queries:
• "FAIR principles AI/ML artifacts"
• "research data management machine learning"
• "metadata standards AI models"
Temporal Gap Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Year Papers Distribution
────────────────────────────────────────────────
2024 18 ██████████████████
2023 15 ███████████████
2022 6 ██████
2021 4 ████
2020 3 ███
2019 1 █
2018 0
2017 0
Temporal Gaps Identified:
⚠ Pre-2020 Coverage: Only 4 papers (9%)
- Lacks historical context for established practices
- Missing foundational research on pre-LLM agent systems
- Recommendation: Add 5-10 foundational papers (2015-2019)
⚠ 2018 Gap: Zero papers
- Complete absence of 2018 research
- May miss important transitional work
Source Type Gap Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Source Type Papers Target Status
─────────────────────────────────────────────────────────────────
Peer-reviewed Journal 15 20 ⚠ BELOW TARGET
Peer-reviewed Conference 22 20 ✓ ADEQUATE
Preprint 8 5 ⚠ ABOVE TARGET
Technical Report 2 2 ✓ ADEQUATE
Source Type Issues:
⚠ Journal Under-representation
- Only 32% journals vs 50% target
- Affects GRADE distribution (fewer HIGH sources)
- Recommendation: Prioritize journal articles in next searches
⚠ Preprint Over-reliance
- 17% preprints vs 10% target
- Many may have been published; check for updates
- Recommendation: Review preprints for journal versions
GRADE Quality Gap Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
GRADE Level Papers Target Status
───────────────────────────────────────────
HIGH 12 15 ⚠ BELOW TARGET (-3)
MODERATE 18 20 ⚠ BELOW TARGET (-2)
LOW 14 10 ⚠ ABOVE TARGET (+4)
VERY LOW 3 2 ⚠ ABOVE TARGET (+1)
Quality Issues:
⚠ Insufficient HIGH Quality Sources
- Only 26% HIGH vs 32% target
- Limits confidence in evidence-based claims
- Recommendation: Seek systematic reviews, meta-analyses, RCTs
⚠ Too Many LOW Quality Sources
- 30% LOW vs 21% target
- Consider replacing with higher quality alternatives
Methodological Gap Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Methodology Papers Status
──────────────────────────────────────────────
Experimental Evaluation 28 ✓ ADEQUATE
Systematic Review 4 ⚠ MINIMAL
Case Study 8 ✓ ADEQUATE
Theoretical Framework 12 ✓ ADEQUATE
Meta-analysis 2 ⚠ MINIMAL
Empirical User Study 5 ⚠ MINIMAL
Methodological Gaps:
⚠ Lack of Systematic Reviews
- Only 4 systematic reviews (need 8-10)
- Reduces ability to synthesize evidence across studies
- Recommendation: Prioritize systematic reviews and meta-analyses
⚠ Limited Empirical User Studies
- Only 5 user studies
- Missing human factors, usability, practitioner perspectives
- Recommendation: Include HCI and empirical SE research
Coverage Score by AIWG Component:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Component Coverage Status Priority
────────────────────────────────────────────────────────────────
Agent Orchestration 85% ✓ GOOD Maintain
HITL Gates 80% ✓ GOOD Maintain
Reproducibility 60% ⚠ FAIR Improve
Provenance Tracking 70% ⚠ FAIR Improve
Test Generation 40% ⚠ POOR Critical
Error Handling 0% 🚨 MISSING Critical
Security 20% ⚠ POOR Critical
Cost Management 10% 🚨 MINIMAL Critical
Overall Corpus Coverage Score: 68/100 (FAIR)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Breakdown:
Topic Coverage: 70/100 (FAIR)
Temporal Coverage: 55/100 (POOR)
Source Type Balance: 65/100 (FAIR)
Quality Distribution: 60/100 (FAIR)
Methodological Mix: 75/100 (GOOD)
Prioritized Action Plan:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Phase 1: Critical Gaps (Next 2 weeks)
1. /research-discover "error handling LLM agents"
2. /research-discover "agent security vulnerabilities"
3. /research-discover "LLM cost optimization"
4. /research-discover "automated test generation LLM"
Phase 2: Significant Gaps (Next month)
5. /research-discover "reproducibility AI experiments"
6. /research-discover "FAIR principles ML"
7. Upgrade preprints to journal versions where available
Phase 3: Quality Improvement (Ongoing)
8. Add systematic reviews (target: 4 more)
9. Add journal articles (target: 5 more)
10. Add foundational pre-2020 papers (target: 6 more)
Estimated Effort:
Phase 1: ~20 papers, 15-20 hours
Phase 2: ~10 papers, 8-10 hours
Phase 3: ~10 papers, 8-10 hours
Total: ~40 papers, 30-40 hours
Suggested Search Queries (Full List):
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Priority 1 (Critical):
• "error handling strategies LLM agents"
• "failure recovery agentic systems"
• "LLM agent security vulnerabilities"
• "prompt injection attacks defense"
• "LLM inference cost optimization"
• "token-efficient prompting strategies"
• "LLM automated test generation"
Priority 2 (High):
• "reproducibility LLM agent experiments"
• "deterministic AI system execution"
• "FAIR principles AI/ML artifacts"
• "research data management machine learning"
Priority 3 (Medium):
• "systematic review LLM applications"
• "meta-analysis language model effectiveness"
• "empirical study AI developer tools"
• "foundational agent architectures 2015-2019"
Export Commands:
# Export full report
/research-gap --export markdown > .aiwg/research/reports/gap-analysis-20260203.md
# Execute discovery workflows
/research-workflow discovery-to-corpus --input gap-phase1-queries.yaml
```
### Topic-Specific Analysis
```
/research-gap "agent security"
Gap Analysis: Agent Security
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Current Coverage: 2 papers (Target: 10)
Gap Severity: CRITICAL (-8 papers)
Priority: URGENT
Existing Papers:
────────────────────────────────────────────────────────────────────
REF-034: Security Considerations for LLM-Based Systems (2023)
GRADE: MODERATE
Coverage: General security overview, threat landscape
REF-042: Prompt Injection Attacks and Mitigations (2024)
GRADE: LOW
Coverage: Specific attack vector (prompt injection)
Coverage Gaps:
────────────────────────────────────────────────────────────────────
🚨 MISSING: Data leakage and exfiltration
🚨 MISSING: Agent sandboxing and isolation
🚨 MISSING: Adversarial robustness
🚨 MISSING: Access control for agent operations
⚠ MINIMAL: Prompt injection (1 paper, need 3+)
⚠ MINIMAL: Model security and safety
Impact Assessment:
────────────────────────────────────────────────────────────────────
Production Readiness: BLOCKED
- Cannot deploy agents to production without security validation
- Lacks guidelines for secure agent implementation
- Missing threat models for agent architectures
Framework Completeness: 35/100
- Security gates incomplete
- Security auditor agent has limited research foundation
- No evidence-based security patterns
User Confidence: LOW
- Developers will have security concerns
- Compliance requirements unmet
Suggested Search Queries:
────────────────────────────────────────────────────────────────────
High Priority:
1. "LLM agent security vulnerabilities systematic review"
Why: Comprehensive overview of threat landscape
2. "agent sandboxing isolation techniques"
Why: Core security requirement for production agents
3. "data leakage prevention language models"
Why: Critical for handling sensitive information
4. "adversarial robustness LLM agents"
Why: Defensive capabilities against attacks
Medium Priority:
5. "secure prompt engineering best practices"
6. "access control agentic systems"
7. "security testing LLM applications"
8. "threat modeling AI agent architectures"
Recommended Actions:
────────────────────────────────────────────────────────────────────
Immediate (This Week):
1. /research-discover "LLM agent security systematic review" --limit 20
2. Review top 5 results for acquisition
Short Term (Next 2 Weeks):
3. Acquire 8-10 papers to reach target coverage
4. Generate security synthesis report
5. Update security-auditor agent with findings
Medium Term (Next Month):
6. Create security testing guidelines
7. Implement security gates based on research
8. Document secure agent patterns
Next Steps:
────────────────────────────────────────────────────────────────────
# Execute discovery
/research-discover "LLM agent security systematic review" --limit 20
# Or run automated workflow
/research-workflow discovery-to-corpus --input '{"query": "LLM agent security", "max_results": 10}'
```
## References
- @$AIWG_ROOT/agentic/code/frameworks/research-complete/agents/workflow-agent.md - Gap analysis
- @$AIWG_ROOT/src/research/services/gap-analysis-service.ts - Gap detection implementation
- @.aiwg/research/README.md - Corpus structure and targets
- @.aiwg/research/reports/ - Generated gap analysis reportsRelated Skills
research-workflow
Execute multi-stage research workflows
research-status
Show research corpus health and statistics
research-query
Search the local research corpus, read matching findings, and synthesize an answer with inline citations to REF-XXX sources. The "query" operation for the research pipeline.
research-quality
Assess source quality using GRADE methodology
research-quality-audit
Audit research corpus for shallow stubs, incomplete sections, missing source files, and doc depth issues. Detects docs written from abstracts rather than full papers and optionally auto-dispatches expansion agents.
research-provenance
Query provenance chains and artifact relationships
research-lint
Run the research corpus lint ruleset to detect structural and referential integrity issues — orphan notes, missing frontmatter, broken references, missing GRADE assessments.
research-gap-detect
Build the mutual citation graph, find connected components, identify isolated clusters, and optionally search for bridge candidates and file gap issues. Automates the manual cluster analysis workflow.
research-document
Generate summaries and literature notes from research papers
research-discover
Search for research papers across academic databases
research-cite
Generate properly formatted citation from research corpus
research-archive
Package research artifacts for long-term archival