Best use case
research-document 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.
Generate summaries and literature notes from research papers
Teams using research-document 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-document/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How research-document Compares
| Feature / Agent | research-document | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate summaries and literature notes from research papers
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 Document Command
Generate structured summaries and literature notes from acquired research papers.
## Instructions
When invoked, create comprehensive documentation:
1. **Load Paper**
- Verify REF-XXX exists in `.aiwg/research/sources/`
- Load PDF and existing finding document
- Load metadata from frontmatter
2. **Extract Content**
- Parse PDF sections (abstract, introduction, methodology, results, conclusion)
- Identify key findings, claims, and evidence
- Extract figures, tables, and important quotes
- Note limitations and future work
3. **Analyze Relevance**
- Assess applicability to AIWG framework
- Identify which components/agents could use these findings
- Determine implementation priority
- Map findings to existing use cases or requirements
4. **Generate Documentation**
- Fill finding document template sections:
- Executive Summary
- Key Findings (with metrics)
- Methodology
- AIWG Relevance
- Implementation Notes
- References
- Use domain-appropriate voice
- Include specific metrics and quotes
5. **Create Synthesis Notes**
- Generate literature note in `.aiwg/research/literature-notes/REF-XXX-notes.md`
- Connect to related research (cross-references)
- Identify gaps or contradictions with existing corpus
- Suggest follow-up research questions
6. **Update Index**
- Add to topic-based indices
- Update cross-reference map
- Flag for synthesis report inclusion
## Arguments
- `[ref-id]` - REF-XXX identifier (required)
- `--depth [brief|standard|comprehensive]` - Documentation depth (default: standard)
- `--focus [section]` - Focus on specific section (methodology, results, implications)
- `--update-only` - Update existing documentation rather than regenerate
- `--include-citations` - Extract all citations from paper for potential acquisition
## Depth Levels
| Level | Content |
|-------|---------|
| `brief` | Executive summary + key findings only (~500 words) |
| `standard` | Full finding document with all sections (~1500 words) |
| `comprehensive` | Full document + literature notes + citation extraction (~3000 words) |
## Examples
```bash
# Standard documentation
/research-document REF-022
# Brief summary for quick review
/research-document REF-022 --depth brief
# Comprehensive with citation extraction
/research-document REF-022 --depth comprehensive --include-citations
# Update existing documentation
/research-document REF-022 --update-only
# Focus on methodology only
/research-document REF-022 --focus methodology
```
## Expected Output
```
Documenting: REF-022 - AutoGen: Enabling Next-Gen LLM Applications
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Step 1: Loading paper
✓ PDF loaded (27 pages)
✓ Metadata parsed
✓ Existing finding document found
Step 2: Extracting content
✓ Abstract extracted
✓ Sections parsed: Introduction, Framework, Evaluation, Discussion
✓ 4 key findings identified
✓ 12 figures/tables extracted
✓ 3 direct quotes captured
Step 3: Analyzing AIWG relevance
✓ High relevance to agent orchestration
✓ Applicable to: Conversable Agent Interface, Auto-Reply Chains
✓ Implementation priority: HIGH
✓ Maps to: UC-174, UC-183
Step 4: Generating documentation
✓ Finding document updated: .aiwg/research/findings/REF-022-autogen.md
✓ Sections populated:
- Executive Summary (150 words)
- Key Findings (4 findings, metrics included)
- Methodology (multi-agent conversational framework)
- AIWG Relevance (applicable components listed)
- Implementation Notes (integration patterns)
- Limitations (scalability concerns noted)
- References (45 citations)
Step 5: Creating synthesis notes
✓ Literature note: .aiwg/research/literature-notes/REF-022-notes.md
✓ Connected to: REF-001, REF-013, REF-057
✓ Synthesis themes: agent collaboration, HITL patterns
✓ Follow-up questions: 3 identified
Step 6: Updating indices
✓ Added to topic indices: agentic-workflows, multi-agent-systems
✓ Cross-reference map updated
✓ Flagged for next synthesis report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Documentation complete!
Finding: .aiwg/research/findings/REF-022-autogen.md (1,847 words)
Literature Note: .aiwg/research/literature-notes/REF-022-notes.md (623 words)
Next Steps:
1. /research-quality REF-022 - Assess evidence quality
2. /research-cite REF-022 - Generate citations
3. Review AIWG integration opportunities in UC-174, UC-183
```
## Quality Checks
Documentation includes automatic quality checks:
- [ ] All key findings have metrics or specific claims
- [ ] AIWG relevance section is concrete (not generic)
- [ ] Implementation priority justified
- [ ] No invented citations or page references
- [ ] Quotes are exact with page numbers
- [ ] Limitations section populated
- [ ] Cross-references to related research included
## Voice and Tone
Documentation follows AIWG voice guidelines:
- **Technical Authority** for methodology sections
- **Analytical Precision** for findings
- **Pragmatic** for implementation notes
- **Balanced** when noting limitations
Avoids:
- Marketing language ("revolutionary", "game-changing")
- Overconfident claims beyond evidence
- Generic summaries without specifics
## References
- @$AIWG_ROOT/agentic/code/frameworks/research-complete/agents/documentation-agent.md - Documentation Agent
- @$AIWG_ROOT/src/research/services/documentation-service.ts - Documentation implementation
- @$AIWG_ROOT/agentic/code/frameworks/research-complete/templates/finding-template.md - Finding template
- @$AIWG_ROOT/agentic/code/addons/voice-framework/voices/technical-authority.md - Voice profile
- @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/rules/citation-policy.md - Citation requirementsRelated 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
Analyze gaps in research coverage
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-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