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Subagent-Driven Literature Rev...

Agent skill

Subagent-Driven Literature Review

Use parallel subagents for large-scale paper screening and deep dive analysis

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SKILL.md

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.

4. Domain-Specific Extraction

Examples by domain:

Genomics:

Subagent extracts:
- GEO/SRA/ENA accessions
- Sample sizes and conditions
- Sequencing methods (RNA-seq, WGS, etc.)
- Analysis pipelines
- Differential expression results

Computational methods:

Subagent extracts:
- Algorithm descriptions
- Code repositories (GitHub, GitLab, etc.)
- Benchmark datasets used
- Performance metrics
- Implementation details

Clinical research:

Subagent extracts:
- Study design (RCT, cohort, etc.)
- Sample size and demographics
- Intervention details
- Primary outcomes
- Statistical methods

Ecology/Environmental:

Subagent extracts:
- Study sites and coordinates
- Sampling methods
- Species/taxa studied
- Environmental measurements
- Data repositories

Workflow: Parallel Screening

Step 1: Plan and Split

Main agent tasks:

Load PMID list from search results
Decide on batch size (typically 15-25 papers per subagent)
Create TodoWrite with batches
Prepare subagent prompts

Example TodoWrite:

- Screen papers batch 1 (PMIDs 1-20)
- Screen papers batch 2 (PMIDs 21-40)
- Screen papers batch 3 (PMIDs 41-60)
- Screen papers batch 4 (PMIDs 61-80)
- Screen papers batch 5 (PMIDs 81-100)
- Consolidate all subagent results
- Generate SUMMARY.md from consolidated data

Step 2: Dispatch Subagents

CRITICAL: Dispatch all subagents in PARALLEL using single message with multiple Task calls

Example:

I'm dispatching 5 subagents in parallel to screen 100 papers.

[Uses Task tool 5 times in single message]

Why parallel: 5x speed improvement vs sequential!

Step 3: Collect Results

Main agent:

Wait for all subagents to complete
Collect JSON results from each
Validate format and completeness

Check for:

All PMIDs were screened
Scoring rubric was applied consistently
No papers missing

Step 4: Consolidate

Main agent:

Merge all subagent results
Remove duplicates (if any overlap between batches)
Sort by relevance score
Add ALL papers to papers-reviewed.json:

json

{
  "10.1234/example.2023": {
    "pmid": "12345",
    "status": "highly_relevant",
    "score": 9,
    "source": "pubmed_search_batch1",
    "screened_by": "subagent",
    "timestamp": "2025-10-11T14:30:00Z",
    "found_data": ["measurements", "methods", "datasets"]
  }
}

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:

python

# 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):

Dispatch subagent 1 → wait → consolidate → dispatch subagent 2 → wait → consolidate → ...

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?

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SKILL.md

Subagent-Driven Literature Review

Overview

When to Use

Use Cases

1. Parallel Paper Screening (Most Common)

2. Deep Dive on Priority Papers

3. Citation Network Exploration

4. Domain-Specific Extraction

Workflow: Parallel Screening

Step 1: Plan and Split

Step 2: Dispatch Subagents

Step 3: Collect Results

Step 4: Consolidate

Step 5: Review Quality

Step 6: Generate Summary

Step 7: Optional Deep Dive

Workflow: Citation Exploration

Step 1: Identify Seed Papers

Step 2: Dispatch Citation Subagents

Step 3: Consolidate Citations

Step 4: Screen New Papers

Step 5: Iterate

Integration with Other Skills

Works with:

Combines with:

Consolidation Patterns

Pattern 1: JSON Aggregation

Pattern 2: Progressive Consolidation

Pattern 3: Batch Consolidation

Common Mistakes

Cost Considerations

Examples

Example 1: Screen 80 Papers in Parallel

Example 2: Deep Dive on 12 Papers

Example 3: Citation Exploration

Quick Reference

Decision Tree

Advanced: Recursive Citation Exploration

Next Steps After Subagent Review

See Also

Recommended Agent Skills

Getting Started with Research Superpowers

Cleaning Up Research Sessions

Building Paper Screening Rubrics

Searching Scientific Literature

Checking ChEMBL for Structured SAR Data

Answering Research Questions