kthorn/research-superpower/answering-research-questions

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Main orchestration workflow for systematic literature research - search, evaluate, traverse, synthesize

Install

npx vskill@latest install kthorn/research-superpower/answering-research-questions

SKILL.md

The same SKILL.md was published earlier as drchronx/ai-agent-research-starter-kit/answering-research-questions.

Answering Research Questions

Overview

Orchestrate the complete research workflow from query to findings.

Core principle: Systematic, trackable, comprehensive. Search → Evaluate → Traverse → Synthesize.

Announce at start: "I'm using the Answering Research Questions skill to find [specific data] about [topic]."

The Process

Phase 1: Parse Query

Extract from user's request:

Keywords:

  • Main concepts (e.g., "BTK inhibitor", "selectivity")
  • Synonyms and alternatives (e.g., "Bruton tyrosine kinase")
  • Related terms (e.g., "off-target", "kinase panel")

Data types needed:

  • Specific measurements (IC50, KD, EC50, etc.)
  • Methods or protocols
  • Structures or sequences
  • Results or conclusions

Constraints:

  • Date ranges
  • Specific compounds/targets
  • Organisms or systems
  • Publication types

Ask clarifying questions if needed:

  • "Are you looking for in vitro or in vivo data?"
  • "Any specific time frame?"
  • "Which kinases are you most interested in?"
  • "What email address should I use for Unpaywall API requests?" (Required for finding open access papers)
Phase 2: Initialize Research Session

Propose folder name:

research-sessions/YYYY-MM-DD-brief-description/

Example: research-sessions/2025-10-11-btk-inhibitor-selectivity/

Show proposal to user:

📁 Creating research folder: research-sessions/2025-10-11-btk-inhibitor-selectivity/
   Proceed? (y/n)

Create folder structure:

bash
mkdir -p "research-sessions/YYYY-MM-DD-description"/{papers,citations}

Initialize files:

Core files (always create these):

papers-reviewed.json:

json
{}

citations/citation-graph.json:

json
{}

SUMMARY.md:

markdown
# Research Query: [User's question]
 
**Started:** YYYY-MM-DD HH:MM
**Keywords:** keyword1, keyword2, keyword3
**Data types sought:** IC50 values, selectivity data, synthesis methods
 
---
 
## Highly Relevant Papers (Score ≥ 8)
 
Papers scored using `evaluating-paper-relevance` skill:
-Score 0-10 based on: Keywords (0-3) + Data type (0-4) + Specificity (0-3)
-Score ≥ 8: Highly relevant with significant data
-Score 7: Relevant with useful data
-Score 5-6: Possibly relevant
-Score < 5: Not relevant
 
(Papers will be added here as found)
 
Example format:
### [Paper Title](https://doi.org/10.1234/example)
**DOI:** [10.1234/example](https://doi.org/10.1234/example) | **PMID:** [12345678](https://pubmed.ncbi.nlm.nih.gov/12345678/)
 
---
 
## Relevant Papers (Score 7)
 
(Papers will be added here as found)
 
---
 
## Possibly Relevant Papers (Score 5-6)
 
(Noted for potential follow-up)
 
---
 
## Search Progress
 
-Initial PubMed search: X results
-Papers reviewed: Y
-Papers with relevant data: Z
-Citations followed: N
 
---
 
## Key Findings
 
(Synthesized findings will be added as research progresses)

CRITICAL: Always use clickable markdown links for DOIs and PMIDs

Auxiliary files (for large searches >100 papers):

See evaluating-paper-relevance skill for guidance on when to create:

  • README.md - Project overview, methodology, file inventory
  • TOP_PRIORITY_PAPERS.md - Curated priority list organized by tier
  • evaluated-papers.json - Rich structured data for programmatic access

For small searches (<50 papers), stick to core files only. For large searches (>100 papers), auxiliary files add significant organizational value.

Phase 3: Search Literature

Use searching-literature skill:

  1. Construct PubMed query from keywords
  2. Execute search (start with 100 results)
  3. Save results to initial-search-results.json
  4. Report: "🔎 Found N papers matching query"
Phase 4: Evaluate Papers

Use evaluating-paper-relevance skill:

For each paper:

  1. Check papers-reviewed.json (skip if already processed)
  2. Stage 1: Score abstract (0-10)
  3. If score ≥ 7: Stage 2 deep dive
  4. Extract findings to SUMMARY.md
  5. Download PDF and supplementary if available
  6. Update papers-reviewed.json (for ALL papers, even low-scoring ones)
  7. If score ≥ 7: proceed to Phase 5 for this paper

CRITICAL: Add every paper to papers-reviewed.json regardless of score. This prevents re-review and tracks complete search history.

Report progress for EVERY paper:

📄 [15/100] Screening: "Paper Title"
   Abstract score: 8 → Fetching full text...
   ✓ Found IC50 data for 8 compounds
   → Added to SUMMARY.md
 
📄 [16/100] Screening: "Another Paper"
   Abstract score: 3 → Skipping (not relevant)
 
📄 [17/100] Screening: "Third Paper"
   Abstract score: 7 → Relevant, adding to queue...

Every 10 papers, give summary update

Phase 5: Traverse Citations

Use traversing-citations skill:

For papers scoring ≥ 7:

  1. Get references (backward)
  2. Get citations (forward)
  3. Filter for relevance (score ≥ 5)
  4. Add to processing queue
  5. Evaluate queued papers (return to Phase 4)

Report progress:

🔗 Following citations from highly relevant paper
   → Found 12 relevant references
   → Found 8 relevant citing papers
   → Adding 20 papers to queue
Phase 6: Checkpoint

Check after:

  • Every 50 papers reviewed
  • Every 5 minutes of processing
  • Queue exhausted

Ask user:

⏸️  Checkpoint: Reviewed 50 papers, found 12 relevant
    Papers with data: 7
    Continue searching? (y/n/summary)

Options:

  • y - Continue processing
  • n - Stop and finalize
  • summary - Show current findings, then decide
Phase 7: Synthesize Findings

When stopping (user says no or queue empty):

Option A: Manual synthesis (small research sessions)

  1. Review SUMMARY.md - Organize by relevance and topic
  2. Extract key findings - Group by data type
  3. Add synthesis section:
markdown
## Key Findings Summary
 
### IC50 Values for BTK Inhibitors
-Compound A: 12 nM (Smith et al., 2023)
-Compound B: 45 nM (Doe et al., 2024)
-[More compounds...]
 
### Selectivity Data
-Compound A shows >80-fold selectivity vs other kinases
-Tested against panel of 50 kinases (Jones et al., 2023)
 
### Synthesis Methods
-Lead compounds synthesized via [method]
-Yields: 30-45%
-Full protocols in [papers]
 
### Gaps Identified
-No data on selectivity vs [specific kinase]
-Limited in vivo data
-Few papers on resistance mechanisms
  1. Update search progress stats
  2. List all files downloaded

Option B: Script-based synthesis (large research sessions >50 papers)

For large research sessions, consider creating a synthesis script:

create `generate_summary.py`:

  • Read evaluated-papers.json from helper scripts
  • Aggregate findings by priority and scaffold type
  • Generate comprehensive SUMMARY.md with:
  • Executive summary with statistics
  • Papers grouped by relevance score
  • Priority recommendations for next steps
  • Methodology documentation
  • Include timestamps and reproducibility info

Benefits:

  • Consistent formatting across sessions
  • Easy to regenerate as more papers added
  • Can customize grouping/filtering logic
  • Documents complete methodology

Final report:

✅ Research complete!
 
📊 Summary:
   - Papers reviewed: 127
   - Relevant papers: 18
   - Highly relevant: 7
   - Data extracted: IC50 values for 45 compounds, selectivity data, synthesis methods
 
📁 All findings in: research-sessions/2025-10-11-btk-inhibitor-selectivity/
   - SUMMARY.md (organized findings)
   - papers/ (14 PDFs + supplementary data)
   - papers-reviewed.json (complete tracking)
Phase 8: Final Consolidation

CRITICAL: Always consolidate findings at the end

1. Create relevant-papers.json

Filter papers-reviewed.json to extract only relevant papers (score ≥ 7):

python
# Read papers-reviewed.json
with open('papers-reviewed.json') as f:
    all_papers = json.load(f)
# Filter for relevant papers (score >= 7)
relevant_papers = {
    doi: data for doi, data in all_papers.items()
    if data.get('score', 0) >= 7
}
# Save to relevant-papers.json
with open('relevant-papers.json', 'w') as f:
    json.dump(relevant_papers, f, indent=2)

Format:

json
{
  "10.1234/example1.2023": {
    "pmid": "12345678",
    "title": "Paper title",
    "status": "highly_relevant",
    "score": 9,
    "source": "pubmed_search",
    "timestamp": "2025-10-11T16:00:00Z",
    "found_data": ["IC50 values", "synthesis methods"],
    "chembl_id": "CHEMBL1234567"
  },
  "10.1234/example2.2023": {
    "pmid": "23456789",
    "title": "Another paper",
    "status": "relevant",
    "score": 7,
    "source": "forward_citation",
    "timestamp": "2025-10-11T16:15:00Z",
    "found_data": ["MIC data"]
  }
}
2. Enhance SUMMARY.md with Methodology Section

Add these sections to the TOP of existing SUMMARY.md (before paper listings):

markdown
# Research Query: [User's question]
 
**Date:** 2025-10-11
**Duration:** 2h 15m
**Status:** Complete
 
---
 
## Search Strategy
 
**Keywords:** BTK, Bruton tyrosine kinase, inhibitor, selectivity, off-target, kinase panel, IC50
**Data types sought:** IC50 values, selectivity data, kinase panel screening
**Constraints:** None (open date range)
 
**PubMed Query:**

("BTK" OR "Bruton tyrosine kinase") AND (inhibitor OR "kinase inhibitor") AND (selectivity OR "off-target")

 
---
 
## Screening Methodology
 
**Rubric:** Abstract scoring (0-10)
- Key terms: +3 pts each (or Keywords 0-3, Data type 0-4, Specificity 0-3 if using old rubric)
- Relevant terms: +1 pt each
- Threshold: ≥7 = relevant
 
**Sources:**
- Initial PubMed search
- Forward/backward citations via Semantic Scholar
 
---
 
## Results Statistics
 
**Papers Screened:**
- Total reviewed: 127 papers
- Highly relevant (≥8): 12 papers
- Relevant (7): 18 papers
- Possibly relevant (5-6): 23 papers
- Not relevant (<5): 74 papers
 
**Data Extracted:**
- IC50 values: 45 compounds across 12 papers
- Selectivity data: 8 papers with kinase panel screening
- Full text obtained: 18/30 relevant papers (60%)
 
**Citation Traversal:**
- Papers with citations followed: 7
- References screened: 45 papers
- Citing papers screened: 38 papers
- Relevant papers found via citations: 8 papers
 
---
 
## Key Findings Summary
 
### IC50 Values for BTK Inhibitors
- Ibrutinib: 0.5 nM (Smith et al., 2023)
- Acalabrutinib: 3 nM (Doe et al., 2024)
- [Additional findings synthesized from papers below]
 
### Selectivity Patterns
- Most inhibitors show >50-fold selectivity vs other kinases
- Common off-targets: TEC, BMX (other TEC family kinases)
 
### Gaps Identified
- Limited data on selectivity vs JAK/SYK
- Few papers on resistance mechanisms
- No in vivo selectivity data found
 
---
 
## File Inventory
 
- `SUMMARY.md` - This file (methodology + findings)
- `relevant-papers.json` - 30 relevant papers (score ≥7)
- `papers-reviewed.json` - All 127 papers screened
- `papers/` - 18 PDFs + 5 supplementary files
- `citations/citation-graph.json` - Citation relationships
 
---
 
## Reproducibility
 
**To reproduce:**
1. Use PubMed query above
2. Apply screening rubric (threshold ≥7)
3. Follow citations from highly relevant papers (≥8)
4. Check Unpaywall for paywalled papers
 
**Software:** Research Superpowers skills v2025-10-11
 
---
 
[Existing paper listings follow below...]
 
## Highly Relevant Papers (Score ≥ 8)
 
### [Paper Title]...

Report to user:

✅ Research session complete!
 
📄 Consolidation complete:
   1. SUMMARY.md - Enhanced with methodology, statistics, and findings
   2. relevant-papers.json - 30 relevant papers (score ≥7) in JSON format
 
📁 All files in: research-sessions/2025-10-11-btk-inhibitor-selectivity/
   - SUMMARY.md (complete: methodology + paper-by-paper findings)
   - relevant-papers.json (30 relevant papers for programmatic access)
   - papers-reviewed.json (127 total papers screened)
   - papers/ (18 PDFs)
 
🔍 Quick access:
   - Open SUMMARY.md for complete findings and methodology
   - Use relevant-papers.json for programmatic access

This is the start of the file. Read the full SKILL.md on GitHub.

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