brycewang-stanford/academic-paper-strategist

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Academic Paper Strategist

Overview

This skill provides a systematic framework for strategic planning of academic papers in philosophy and interdisciplinary research. It guides you through three phases—from platform selection to optimized outline—with AI-driven literature search, research gap identification, originality assessment, and quality-controlled outline design.

Output: A detailed, review-ready paper outline with supporting documentation (platform style guide, literature review, gap analysis, reviewer assessment).

Companion Skill: Use academic-paper-composer to execute the outline and write the full paper.

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When to Use This Skill

Use academic-paper-strategist when you need to:

Planning Stage:

• Design a research paper from initial idea to structured outline
• Identify a suitable publication platform (PhilArchive, arXiv, etc.)
• Understand writing standards for a specific preprint platform

Research Stage:

• Conduct systematic literature search
• Identify research gaps with evidence
• Assess originality of your research idea
• Predict potential impact

Design Stage:

• Structure paper chapters and arguments
• Optimize outline from reviewer perspective
• Prepare submission-ready strategy

Triggers:

• "Plan a paper on [topic]"
• "Help me design a paper about [subject]"
• "Identify research gaps in [field]"
• "Is this idea original?"
• "What platform should I submit to?"

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Workflow Overview

Phase 1: PLATFORM ANALYSIS (Target Selection + Style Learning)
    ↓
Phase 2: THEORETICAL FRAMEWORK (AI-Driven Gap Identification)
    ↓
Phase 3: OUTLINE OPTIMIZATION (Quality-Controlled Design)
    ↓
Output: Detailed Outline + Supporting Documentation

Quality Gates: 3 validation checkpoints ensure each phase meets standards before proceeding.

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Phase 1: Platform Analysis

Goal

Identify the optimal submission platform and understand its writing standards through systematic sample paper analysis.

Input Required from User

• Core research idea or topic (brief description)
• Target platform (optional - if unclear, I'll recommend)
• Field/discipline (philosophy, cognitive science, interdisciplinary, etc.)

Workflow

Step 1.1: Platform Selection (If Needed)

If target platform unclear, I will:

1. List candidate platforms based on research content:

   • PhilArchive/PhilPapers: Philosophy papers, phenomenology, metaphysics
   • arXiv (cs.AI, q-bio.NC): Computational, neuroscience, AI-related
   • PhilSci-Archive: Philosophy of science, formal methods
   • PsyArXiv: Psychology, cognitive science
   • SocArXiv: Social sciences, interdisciplinary

2. Evaluate each platform:

   • Subject area alignment (does your topic fit?)
   • Methodology match (philosophical/empirical/computational)
   • Acceptance criteria
   • Typical review timeline

3. Provide recommendation with reasoning

4. Decision Point 1: You confirm platform or suggest alternative

Step 1.2: Sample Paper Search (AI-Driven, Quality-Controlled)

I will conduct multi-dimensional search for 8-10 representative papers:

Search Strategy (load references/search_strategy.md for details):

Time Dimension:

• Recent (last 6 months): 3 papers - capture current trends
• Current (1-2 years): 3 papers - established standards
• Classic (highly cited): 2 papers - quality benchmarks

Relevance Dimension:

• Use keyword combinations from your topic
• Score each paper 0-10 for relevance
• Retain only papers scoring ≥7/10

Diversity Dimension:

• Multiple authors (≥5 unique)
• Different research perspectives
• Varied paper lengths

Tools Used:

• Exa MCP (semantic search)
• Tavily MCP (web search)
• Platform-specific search (PhilPapers, arXiv)

Quality Validation: After search, I'll run scripts/evaluate_samples.py to generate evaluation report:

python scripts/evaluate_samples.py

This produces:

• Sample quality metrics
• Time distribution check
• Relevance statistics
• Diversity assessment
• Pass/Fail recommendation

Quality Gate 1 (Must Pass):

• ✓ Sample papers ≥8
• ✓ Time distribution balanced
• ✓ Average relevance ≥8/10
• ✓ Unique authors ≥5

If Failed: Re-search with adjusted criteria

Step 1.3: Writing Standards Extraction

From the 8-10 sample papers, I will extract:

Structural Patterns:

• Abstract structure (Problem→Method→Results→Contribution?)
• Chapter organization (how many sections? typical flow?)
• Average proportions (Intro 15%, Main 70%, Conclusion 15%?)

Style Patterns:

• First-person vs passive voice usage
• How arguments are structured
• Citation density and format
• Use of technical terminology

Format Specifications:

• Typical word count range
• Reference count range
• Section heading conventions

Output: [Platform]_Writing_Standards_Guide.md

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Phase 2: Theoretical Framework

Goal

AI-driven systematic literature search, research gap identification, and originality assessment.

Input Required from User

• Core research question/thesis (your main argument)
• Background context (why you're interested in this)
• Optional: Any papers you already know about

Workflow

Step 2.1: Literature Search (AI-Driven, Fully Automated)

Important: This phase is AI-driven. You provide your idea; I conduct comprehensive literature search and gap analysis.

Multi-Round Search Strategy:

Round 1: Direct Search (Primary Literature)

1. Extract core concepts from your idea (3-5 concepts)
2. Generate keyword combinations (10-15 combinations)
   • Concept + concept
   • Concept + method
   • Include synonyms and disciplinary variants
3. Search each combination using Exa/Tavily
4. Collect 30-50 candidate papers
5. Quality filter: Retain top 20 papers (relevance ≥7/10)

Round 2: Expanded Search (Adjacent Fields)

1. Extract new keywords from Round 1 papers
2. Search adjacent disciplines:
   • Philosophy → cognitive science
   • Neuroscience → philosophy of mind
   • AI → consciousness studies
3. Collect 10-20 bridging papers

Round 3: Classic Literature (Foundational Works)

1. Identify highly-cited papers (>100 citations)
2. Track citations from Round 1-2 papers
3. Collect 5-10 foundational papers

Total Literature Base: 35-50 papers

Load Reference: references/search_strategy.md for detailed methodology

Step 2.2: Research Gap Identification (AI Analysis)

Using collected literature, I will automatically identify 3-5 research gaps:

Gap Identification Methods:

1. Concept Mapping:

   • Plot papers on Concept × Method matrix
   • Identify white spaces (unexplored combinations)

2. Problem-Solution Analysis:

   • What problems does literature address?
   • What limitations do authors acknowledge?
   • What questions remain unanswered?

3. Temporal Analysis:

   • What was once studied but abandoned?
   • What emerged recently but unexplored?

Gap Types:

• Complete gaps: No existing research
• Partial gaps: Preliminary work only, needs development
• Controversy gaps: Competing theories, no resolution

For Each Gap, I Document:

• Clear definition (50-100 words)
• Evidence (3-5 citations showing gap exists)
• Significance assessment (High/Medium/Low)
• Feasibility assessment (Can you address it?)

Validation: Run scripts/gap_analysis.py to ensure quality:

python scripts/gap_analysis.py

This validates:

• Each gap has ≥3 pieces of evidence
• Definitions are specific and clear
• Significance is justified

Quality Gate 2 (Must Pass):

• ✓ Literature base ≥20 papers
• ✓ Identified gaps ≥3
• ✓ Each gap has ≥3 evidence citations
• ✓ At least 1 high-significance gap

If Failed: Continue search or pivot research direction

Output: Literature_Review_Report.md + Research_Gap_Analysis.md

Step 2.3: Originality Assessment (AI Analysis)

I will automatically assess your idea's originality:

Step 1: Similarity Analysis

• Compare your idea with top 15 most similar papers
• Create similarity matrix (topic/method/conclusion overlap)
• Calculate overall similarity percentage

Interpretation:

• 80%: High similarity, needs repositioning

• 50-80%: Moderate, emphasize differences
• <50%: Good originality, proceed

Step 2: Innovation Classification

Identify which innovation types apply (need ≥2):

1. Methodological: New approach to known problem
2. Theoretical: New framework or model
3. Application: Existing theory to new domain
4. Integrative: Synthesizing separate literatures

Step 3: Impact Prediction (1-10 scale)

Scoring Criteria:

• Gap Importance (5 points): Core vs. peripheral problem?
• Generalizability (3 points): Widely applicable?
• Explanatory Power (2 points): Resolves existing puzzles?

Target: ≥7/10 for good impact potential

Output: Originality_Assessment_Report.md (similarity analysis + innovation types + impact prediction + 300-word justification)

Step 2.4: Core Concepts Discussion (Interactive)

Decision Point 2: Based on literature analysis, I will:

1. Propose 3-5 core concepts to emphasize
2. Explain rationale (based on gap analysis + literature frequency)
3. Ask for your feedback: Agree? Adjust? Add?

This ensures the paper focuses on the right concepts to maximize contribution.

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Phase 3: Outline Optimization

Goal

Design a structured, review-ready outline optimized from a reviewer's perspective.

Input

• Literature analysis from Phase 2
• Core concepts (confirmed in Step 2.4)
• Platform standards from Phase 1

Workflow

Step 3.1: Initial Structure Design

Based on platform standards, I will:

1. Design chapter structure:

   • Abstract
   • Introduction (with subsections)
   • Main body (3-5 chapters, each with subsections)
   • Conclusion

2. Allocate word counts:

   • Introduction: 15-20% of total
   • Main body: 60-70% of total
   • Conclusion: 10-15% of total

3. Determine argument flow:

   • Logical progression of ideas
   • Where to introduce concepts
   • Where to address objections

Output: Initial_Outline_Draft.md

Step 3.2: Reviewer-Perspective Self-Assessment

I will evaluate the outline as if I were a platform reviewer, using 7 dimensions (load references/quality_standards.md for criteria):

7-Dimension Assessment (5 points each, 35 total):

1. Argument Clarity (1-5)

   • Is the thesis clear?
   • Are supporting arguments identifiable?

2. Argument Completeness (1-5)

   • Any logical gaps or jumps?
   • All premises justified?

3. Literature Support (1-5)

   • Expected citation count (40+ for philosophy)
   • Key works covered?

4. Methodological Clarity (1-5)

   • Approach explicit (philosophical argument/phenomenological/etc.)?
   • Method justified?

5. Originality Expression (1-5)

   • Contribution clear?
   • Differentiated from existing work?

6. Organization (1-5)

   • Logical flow?
   • Proportions balanced?

7. Platform Fit (1-5)

   • Matches platform style?
   • Meets format requirements?

Scoring:

• Total: X/35
• Passing threshold: ≥28/35 (80%)

Requirement: Must identify at least 3-5 specific issues with concrete improvement suggestions.

Output: Reviewer_Assessment_Report.md

Step 3.3: Optimization Recommendations (Data-Driven)

For each dimension scoring <4/5, I will provide:

Issue Description:

• What specific problem exists?

Severity (High/Medium/Low):

• High: Affects paper acceptability
• Medium: Affects paper quality
• Low: Minor improvement

Concrete Solution:

• Specific actionable fix
• Example of how to implement

Expected Improvement:

• How much will this raise the score?

Prioritization:

1. All high-severity issues first
2. Then medium-severity
3. Then low-severity (optional)

Decision Point 3: I present recommendations; you decide:

• Accept (implement all)
• Selective (choose which to implement)
• Modify (adjust recommendations)

Step 3.4: Final Outline Generation

After implementing approved optimizations, I produce:

Detailed Outline Structure:

# [Paper Title]

## Abstract (250 words)
- [Key points to cover]

## 1. Introduction (1,500 words)
### 1.1 The Puzzle (400 words)
- [Specific content guidance]
### 1.2 Existing Approaches (600 words)
- [Specific theories to discuss]
### 1.3 This Paper's Contribution (500 words)
- [Specific claims to make]

## 2. [Main Chapter] (1,200 words)
### 2.1 [Section] (400 words)
- [Argument structure]
- [Key citations]
...

[Complete structure to 3rd-level headings]

## References
- [Expected 40-60 sources]

Quality Gate 3 (Must Pass):

• ✓ Reviewer score ≥28/35 (80%)
• ✓ All high-severity issues resolved
• ✓ Word allocations sum to target total
• ✓ Platform conformity ≥70%

If Failed: Redesign outline addressing identified issues

Final Output: Optimized_Detailed_Outline.md

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Complete Output Package

Upon completion of all 3 phases, you receive:

Documentation

1. [Platform]_Writing_Standards_Guide.md

   • Platform style patterns
   • Structural templates
   • Citation and format conventions

2. Sample_Papers_Evaluation_Report.md

   • 8-10 analyzed papers
   • Quality metrics
   • Extracted patterns

3. Literature_Review_Report.md

   • 35-50 core papers
   • Organized by theme
   • Annotated with relevance

4. Research_Gap_Analysis.md

   • 3-5 identified gaps
   • Evidence packages
   • Significance assessments

5. Originality_Assessment_Report.md

   • Similarity analysis
   • Innovation classification
   • Impact prediction

6. Reviewer_Assessment_Report.md

   • 7-dimension scores
   • Identified issues
   • Optimization recommendations

7. Optimized_Detailed_Outline.md ⭐ Main Deliverable

   • Complete structure to 3rd-level headings
   • Word count allocations
   • Content guidance for each section
   • Key citations to include

Ready for Next Step

With the Optimized_Detailed_Outline.md, proceed to academic-paper-composer skill to write the full paper.

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Quality Assurance System

Quality Standards Reference

For detailed evaluation criteria, load:

references/quality_standards.md

This document defines:

• Sample paper selection criteria
• Literature search comprehensiveness metrics
• Gap identification requirements
• Reviewer assessment rubrics
• Quality gate thresholds

Evaluation Scripts

Two Python scripts support quality validation:

1. Sample Paper Evaluator

python scripts/evaluate_samples.py

Function: Validates collected sample papers against quality standards

• Checks time distribution
• Calculates average relevance
• Verifies diversity
• Generates evaluation report

When to Use: After Step 1.2 (sample paper search)

2. Gap Analysis Validator

python scripts/gap_analysis.py

Function: Validates identified research gaps

• Checks evidence sufficiency (≥3 per gap)
• Validates gap definitions
• Assesses significance justifications
• Generates gap portfolio report

When to Use: After Step 2.2 (gap identification)

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Decision Points (Interactive)

This skill has 3 key decision points where I pause for your input:

Decision Point 1: Platform Selection (Step 1.1)

I provide: Platform analysis + recommendation You decide: Accept recommendation or suggest alternative

Decision Point 2: Core Concepts (Step 2.4)

I provide: 3-5 proposed core concepts + rationale You decide: Confirm, adjust, or supplement concepts

Decision Point 3: Optimization Acceptance (Step 3.3)

I provide: Prioritized list of improvements + recommendations You decide: Accept all, select specific ones, or request modifications

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Example Usage

User Request

"I want to write a philosophy paper about self-continuity during sleep, arguing that narrative compression maintains identity across sleep-wake cycles."

Skill Response

Phase 1: Platform Analysis

1. Analyzing topic... Recommended platform: PhilArchive (philosophy of mind focus)
2. Searching sample papers... [AI searches with keywords: "self-continuity", "sleep", "personal identity", "phenomenology"]
3. Found 10 candidates, evaluating... ✓ 8 papers meet quality standards
4. Extracting writing patterns... ✓ Style guide generated

Phase 2: Theoretical Framework

1. Conducting literature search...
   • Round 1: 25 papers on self/sleep/memory
   • Round 2: 12 papers bridging philosophy + neuroscience
   • Round 3: 8 classic papers (Dennett, Thompson, Metzinger)
   • Total: 45 papers in literature base
2. Identifying gaps... ✓ 4 gaps found:
   • Gap 1: Compression mechanism (how self is rebuilt)
   • Gap 2: Functional explanation (why continuity needed)
   • Gap 3: Philosophical implications of discontinuity
   • Gap 4: Integration of phenomenology + neuroscience
3. Assessing originality... ✓ 62% similarity (medium), integrative innovation, impact score: 8/10
4. Decision Point 2: Proposed core concepts: narrative compression, periodic death, self-model transparency, memory consolidation. [User confirms]

Phase 3: Outline Optimization

1. Designing initial outline... ✓ 6 chapters, 9,600 words total
2. Reviewer assessment... Score: 26/35 (below threshold)
   • Issues found: Methodological clarity (3/5), Originality expression (3/5)
3. Decision Point 3: Recommendations:
   • Add explicit methodology section (High priority)
   • Strengthen differentiation from existing work (High priority)
   • [User accepts both]
4. Regenerating outline... ✓ New score: 30/35 (passes)

Output: Optimized_Detailed_Outline.md ready for writing phase

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Tips for Best Results

Provide Clear Research Ideas

• The more specific your initial idea, the better the literature search
• Include any philosophers/theories you're building on
• Mention any specific questions you want to address

Trust the AI-Driven Search

• Literature search, gap identification, and originality assessment are fully automated
• I use multiple search strategies to ensure comprehensive coverage
• Quality gates validate that standards are met

Engage at Decision Points

• Your input at the 3 decision points shapes the final outline
• Feel free to adjust my recommendations based on your expertise
• Decisions are collaborative, not automated

Use Quality Validation

• If unsure about quality, I can re-run evaluation scripts
• Reports provide objective metrics and concrete feedback
• Quality gates ensure no phase proceeds without meeting standards

Iterate if Needed

• If Phase 1 fails quality gates, we re-search with adjusted criteria
• If Phase 2 reveals insufficient gaps, we pivot research direction
• If Phase 3 scores low, we redesign with clear improvement targets

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Limitations and Notes

• Calibrated for philosophy and interdisciplinary papers: May need adjustment for pure empirical sciences or formal logic
• Preprint platform focus: Primarily targets PhilArchive, arXiv, PhilSci-Archive (not peer-reviewed journals)
• Requires web access: Literature search depends on Exa/Tavily MCP tools
• Human judgment still essential: AI provides analysis and recommendations, but you make final decisions
• Complementary to writing skill: This skill produces outlines; use academic-paper-composer for actual writing

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Related Skills

Next Step: academic-paper-composer

• Takes the optimized outline from this skill
• Executes systematic writing with quality control
• Produces submission-ready manuscript

Can Be Used Standalone: If you already have a mature outline from another source, you can skip this skill and go directly to academic-paper-composer.

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Summary

academic-paper-strategist transforms a research idea into a publication-ready outline through:

1. Platform Analysis: Identify optimal venue and learn writing standards (8-10 sample papers)
2. Theoretical Framework: AI-driven literature search (35-50 papers) + gap identification (3-5 gaps) + originality assessment
3. Outline Optimization: Reviewer-perspective evaluation (7 dimensions) + targeted improvements

Quality Assurance: 3 quality gates + 2 validation scripts ensure each phase meets standards.

Output: Detailed outline ready for systematic writing, with complete supporting documentation.

Estimated Time: 2-4 hours for complete strategic planning (depending on literature availability and iteration needs).