How do I install Subagent-Driven Literature Review?
Download skills/05-kthorn-research-superpower/research/subagent-driven-review/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does Subagent-Driven Literature Review live?
The publisher keeps it at https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.
Which agents can use Subagent-Driven Literature Review?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Use parallel subagents for large-scale paper screening and deep dive analysis
name: Subagent-Driven Literature Review
description: Use parallel subagents for large-scale paper screening and deep dive analysis
when_to_use: Large literature searches (50+ papers), parallel paper screening, deep dive analysis on multiple papers, citation network exploration, when main context is getting full
version: 1.0.0
Subagent-Driven Literature Review
Overview
Core principle: Fresh subagent per batch + consolidation between batches = fast parallel screening with quality control
For large literature reviews (50+ papers), dispatching parallel or sequential subagents dramatically speeds up screening while maintaining quality through consolidation checkpoints.
When to Use
Use subagent-driven approach when:
Large searches: 50+ papers to screen
Parallelizable work: Papers are independent, can be screened separately
Deep dive tasks: Multiple papers need detailed extraction (data tables, methods, datasets)
Citation exploration: Following citation networks recursively
Context management: Main context getting full, need fresh context
Time pressure: Need results faster than sequential screening
Do NOT use when:
Small searches (<20 papers) - overhead not worth it
Need real-time user visibility into every paper
Papers require cross-comparison during screening
Simple, fast screening tasks
Use Cases
1. Parallel Paper Screening (Most Common)
Scenario: You have 100 papers from PubMed search to screen for relevance
Pattern:
Main agent:
1. Splits 100 papers into 5 batches of 20
2. Dispatches 5 subagents IN PARALLEL (single message, multiple Task calls)
3. Each subagent:
- Fetches abstracts for its batch
- Scores using rubric
- Returns JSON with results
4. Main agent consolidates results into papers-reviewed.json
Time savings: 5x faster than sequential!
Prompt template for subagent:
I need you to screen papers 1-20 from this PMID list for relevance to [QUERY].
PMIDs to screen: [PMID list]
Use the evaluating-paper-relevance skill to:
1. Fetch abstract for each PMID
2. Score 0-10 based on:
- Keywords: [list]
- Data types needed: [measurements, protocols, datasets, etc.]
3. Return JSON:
{
"screened_papers": [
{"pmid": "12345", "score": 8, "status": "relevant", "reason": "..."},
...
],
"stats": {"highly_relevant": 3, "relevant": 5, "not_relevant": 12}
}
Do NOT update papers-reviewed.json - return results only.
**Rate limiting (CRITICAL - PubMed limits are SHARED across all parallel subagents):**
- If you are the ONLY subagent running: Use 500ms delays (2 req/sec, safe)
- If running with OTHER parallel subagents: Use longer delays to share capacity
- You are 1 of 2 parallel: Use 1 second delays
- You are 1 of 3 parallel: Use 1.5 second delays
- You are 1 of 5 parallel: Use 2.5 second delays
- If you get HTTP 429 errors: Wait 5 seconds, then use 5-second delays for remaining requests
2. Deep Dive on Priority Papers
Scenario: Initial screening identified 15 highly relevant papers, need detailed data extraction from each
Pattern:
Main agent:
1. Creates TodoWrite with 15 tasks (one per paper)
2. For each paper, dispatches subagent to:
- Fetch full text (PMC, Unpaywall)
- Extract relevant data (tables, figures, methods)
- Identify key findings
- Return structured findings
3. Main agent consolidates into SUMMARY.md
4. Reviews and adds to papers-reviewed.json
Can dispatch in parallel (5 at a time) or sequentially
Prompt template for subagent:
Deep dive analysis for paper PMID [12345] / DOI [10.xxxx/yyyy]
Use evaluating-paper-relevance skill to:
1. Check for curated data sources (if applicable to domain)
2. Fetch full text (try PMC, then Unpaywall if paywalled)
3. Extract relevant data based on research domain:
- Data tables and measurements
- Methods and protocols
- Key results and findings
- Figures with relevant information
4. Return structured JSON:
{
"pmid": "12345",
"doi": "10.xxxx/yyyy",
"full_text_source": "PMC" or "Unpaywall" or "paywalled",
"data_sources": ["Table 1", "Figure 3", "Supplementary Data"],
"key_measurements": ["specific values or ranges found"],
"methods_summary": "Brief description of methods",
"key_findings": ["Finding 1", "Finding 2", ...],
"data_availability": "GEO: GSE12345" or "Code: github.com/..." or null
}
Do NOT update papers-reviewed.json - return findings only.
3. Citation Network Exploration
Scenario: Found one highly relevant paper, need to explore forward and backward citations
Pattern:
Main agent:
1. Dispatches two subagents IN PARALLEL:
- Subagent A: Fetch and screen forward citations
- Subagent B: Fetch and screen backward citations
2. Each returns list of promising PMIDs with scores
3. Main agent:
- Consolidates results
- Removes duplicates
- Adds to screening queue
- Updates papers-reviewed.json
Prompt template for subagent:
Find and screen forward citations for PMID [12345].
Use traversing-citations skill to:
1. Fetch forward citations from PubMed or OpenCitations
2. Screen abstracts for relevance to [QUERY]
3. Score each citation (0-10)
4. Return JSON with promising papers (score ≥7):
{
"seed_pmid": "12345",
"direction": "forward",
"citations_found": 45,
"relevant_citations": [
{"pmid": "67890", "score": 8, "title": "...", "reason": "..."},
...
]
}
Do NOT update papers-reviewed.json - return results only.
Mark source as "subagent" or "pubmed_search_batch1" etc.
Step 5: Review Quality
Main agent checks:
Scoring appears consistent across batches
No batch has dramatically different hit rate (could indicate problem)
Highly relevant papers make sense
Any papers needing manual re-review?
Red flags:
One batch found 10 relevant papers, others found 0-1 (inconsistent scoring?)
Papers marked "highly relevant" don't match keywords
Missing expected papers
If issues found: Re-screen problematic batch manually or with fresh subagent
Step 6: Generate Summary
Main agent:
Create SUMMARY.md with all highly relevant and relevant papers
Sort by score
Add statistics
Note which papers need deep dive
Step 7: Optional Deep Dive
For highly relevant papers (score ≥8):
Option A: Dispatch subagents sequentially
For each highly relevant paper:
- Dispatch one subagent per paper
- Subagent does deep dive extraction
- Main agent consolidates findings immediately
- Updates SUMMARY.md progressively
Option B: Dispatch subagents in parallel batches
Batch 1: Papers 1-5 (dispatch 5 subagents in parallel)
Wait for completion, consolidate
Batch 2: Papers 6-10 (dispatch 5 subagents in parallel)
Wait for completion, consolidate
...
Workflow: Citation Exploration
Step 1: Identify Seed Papers
Find 2-3 highly relevant papers from initial screening
Step 2: Dispatch Citation Subagents
For each seed paper, dispatch TWO subagents in parallel:
Forward citations (who cited this paper?)
Backward citations (what did this paper cite?)
Prompt each subagent with:
Seed PMID
Relevance criteria
Return only papers scoring ≥7
Step 3: Consolidate Citations
Main agent:
Collects all citation results
Removes duplicates
Removes papers already in papers-reviewed.json
Creates new screening queue
Step 4: Screen New Papers
Option A: Dispatch new batch screening subagents for citation results
Option B: Main agent screens smaller batch manually
Step 5: Iterate
If citation exploration found many new relevant papers:
Consider exploring citations from those papers too
Be careful of exponential growth!
Set stopping criteria (e.g., max 3 levels deep, max 200 total papers)
Integration with Other Skills
Works with:
evaluating-paper-relevance: Subagents use this for individual paper screening
traversing-citations: Subagents use this for citation exploration
finding-open-access-papers: Subagents check Unpaywall for paywalled papers
checking-chembl: Subagents can check curated databases (when applicable)
Combines with:
writing-plans: Create screening plan before dispatching subagents
TodoWrite: Track batches and consolidation progress
Consolidation Patterns
Pattern 1: JSON Aggregation
Subagents return structured JSON, main agent merges:
# Pseudo-code for consolidation
all_results = []
for subagent_output in subagent_results:
results = parse_json(subagent_output)
all_results.extend(results['screened_papers'])
# Sort by score
all_results.sort(key=lambda x: x['score'], reverse=True)
# Update papers-reviewed.json
for paper in all_results:
papers_reviewed[paper['doi']] = {
'pmid': paper['pmid'],
'status': paper['status'],
'score': paper['score'],
'source': f"subagent_batch_{paper['batch_id']}",
'timestamp': now()
}
Pattern 2: Progressive Consolidation
Consolidate after each subagent completes (sequential dispatch):
Advantage: See progress incrementally
Disadvantage: Slower than full parallel
Pattern 3: Batch Consolidation
Dispatch N subagents in parallel, consolidate batch, repeat:
Dispatch 5 subagents → wait for all 5 → consolidate → dispatch next 5 → ...
Advantage: Balance between speed and manageable consolidation
Disadvantage: More complex than full parallel or sequential
Common Mistakes
Not dispatching in parallel: Sending Task calls sequentially wastes time → Use single message with multiple Task calls
Subagents updating tracking files: Causes conflicts → Subagents return JSON only, main agent updates files
Inconsistent scoring: Different subagents use different rubrics → Provide clear rubric in prompt
No quality review: Blindly trusting subagent results → Always review consolidated results
Too many parallel subagents: Dispatching 20+ at once → Keep to 5-10 parallel max
Forgetting rate limits: Subagents hit API limits → Include rate limiting in prompts (500ms for single agent, 2.5 seconds for 5 parallel agents)
No source tracking: Can't tell which batch found which papers → Add batch_id or source field
Duplicate work: Multiple subagents screen same papers → Carefully split PMID lists with no overlap
Cost Considerations
Subagent usage has cost implications:
Token usage per subagent:
Screening 20 papers: ~10-15K tokens per subagent
Deep dive 1 paper: ~5-10K tokens per subagent
Citation exploration: ~8-12K tokens per subagent
Trade-off:
Parallel screening: Higher cost, much faster (5x speed)
Sequential screening: Lower cost, slower
Consider for time-sensitive research
Cost-saving strategies:
Use subagents for large batches only (50+ papers)
Screen manually for small searches (<20 papers)
Parallel dispatch for initial screening (speed matters)
Sequential dispatch for deep dive (can review progressively)
Examples
Example 1: Screen 80 Papers in Parallel
Initial state: Have 80 PMIDs from PubMed search
Main agent:
I'll dispatch 4 subagents in parallel to screen these 80 papers.
Batch 1: PMIDs 1-20 [dispatches subagent with prompt]
Batch 2: PMIDs 21-40 [dispatches subagent with prompt]
Batch 3: PMIDs 41-60 [dispatches subagent with prompt]
Batch 4: PMIDs 61-80 [dispatches subagent with prompt]
[Uses Task tool 4 times in single message]
Now waiting for all subagents to complete...
After subagents complete:
All 4 subagents have completed. Consolidating results:
Batch 1: 3 highly relevant, 5 relevant, 12 not relevant
Batch 2: 2 highly relevant, 7 relevant, 11 not relevant
Batch 3: 4 highly relevant, 6 relevant, 10 not relevant
Batch 4: 1 highly relevant, 4 relevant, 15 not relevant
Total: 10 highly relevant, 22 relevant, 48 not relevant
Updating papers-reviewed.json with all 80 papers...
Generating SUMMARY.md with 32 relevant papers...
Next: Deep dive on 10 highly relevant papers?
Example 2: Deep Dive on 12 Papers
Initial state: Have 12 highly relevant papers needing data extraction
Main agent:
I'll dispatch 12 subagents (in 3 batches of 4) to do deep dive analysis.
Batch 1 papers: PMID 12345, 23456, 34567, 45678
[Dispatches 4 subagents in parallel with deep dive prompts]
Waiting for batch 1 to complete...
[Consolidates batch 1 results into SUMMARY.md]
Batch 2 papers: PMID 56789, 67890, 78901, 89012
[Dispatches 4 subagents in parallel]
...
Result: All 12 papers analyzed with structured data in ~10-15 minutes vs 1-2 hours sequential
Example 3: Citation Exploration
Initial state: Found key paper PMID 12345
Main agent:
I'll explore citations from this key paper.
Dispatching 2 subagents in parallel:
1. Forward citations (who cited PMID 12345?)
2. Backward citations (what did PMID 12345 cite?)
[Uses Task tool twice in single message]
Waiting for citation exploration...
Forward citations: Found 34 citations, 8 appear relevant
Backward citations: Found 42 references, 6 appear relevant
New papers to screen: 14 (after removing duplicates)
Now dispatching screening subagent for these 14 papers...
Quick Reference
Task
Subagent Pattern
Parallel?
Consolidation
Screen 100 papers
5 batches of 20
Yes (5 parallel)
Merge JSON, update papers-reviewed.json
Deep dive on 15 papers
15 individual tasks
Yes (batches of 5)
Add findings to SUMMARY.md progressively
Citation exploration
2-3 citation tasks
Yes
Merge, dedupe, add to screening queue
Data extraction
1 per paper
Sequential or batched
Update papers-reviewed.json with findings
Decision Tree
Have literature review task?
├─ <20 papers?
│ └─ Screen manually (no subagents)
├─ 20-50 papers?
│ ├─ Time-sensitive? → Use subagents (2-3 batches)
│ └─ Not urgent? → Screen manually
└─ 50+ papers?
├─ Initial screening → Use parallel subagents (5-10 batches)
├─ Deep dive needed? → Use sequential or batched subagents
└─ Citation exploration? → Use parallel subagents per seed paper
Advanced: Recursive Citation Exploration
For exhaustive citation network analysis:
Level 0: Seed paper (PMID 12345)
├─ Level 1: Forward + backward citations (dispatch 2 subagents)
│ ├─ Find 12 relevant papers
│ └─ Add to papers-reviewed.json
├─ Level 2: For each of 12 papers, explore citations (dispatch 24 subagents)
│ ├─ Find 43 new relevant papers
│ └─ Add to papers-reviewed.json
└─ Level 3: For top 10 papers from Level 2, explore citations
├─ Find 28 new relevant papers
└─ STOP (reaching diminishing returns)
Total: 83 papers discovered through citation network
Stopping criteria:
Max depth (e.g., 3 levels)
Max total papers (e.g., 200)
Diminishing returns (fewer relevant papers per level)
Time/cost budget
Next Steps After Subagent Review
Review consolidated results for quality and consistency
Identify gaps - any expected papers missing?
Deep dive on highly relevant papers (if not already done)
Generate final summary with statistics and key findings
Plan next actions - citation exploration? Specific data extraction?
finding-open-access-papers: Unpaywall integration for paywalled papers
How do I install Subagent-Driven Literature Review?
Download skills/05-kthorn-research-superpower/research/subagent-driven-review/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does Subagent-Driven Literature Review live?
The publisher keeps it at https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.
Which agents can use Subagent-Driven Literature Review?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.