plugins/research/skills/research-document/SKILL.md
Generate summaries and literature notes from research papers
npx skillsauth add jmagly/aiwg research-documentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Generate structured summaries and literature notes from acquired research papers.
When invoked, create comprehensive documentation:
Load Paper
.aiwg/research/sources/Extract Content
Analyze Relevance
Generate Documentation
Create Synthesis Notes
.aiwg/research/literature-notes/REF-XXX-notes.mdUpdate Index
[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| 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) |
# 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
Documenting: REF-022 - AutoGen: Enabling Next-Gen LLM Applications
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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
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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
Documentation includes automatic quality checks:
Documentation follows AIWG voice guidelines:
Avoids:
data-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.