--- name: brycewang-stanford/academic-paper-strategist source: https://app.decimal.ai/s/brycewang-stanford-academic-paper-strategist@1/SKILL.md source_sha256: 18017c5fbd42 --- # 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. --- ## 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?" --- ## 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. --- ## 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: ```bash 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` --- ## 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: ```bash 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. --- ## 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**: ```markdown # [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` --- ## 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. --- ## Quality Assurance System ### Quality Standards Reference For detailed evaluation criteria, load: ```markdown 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 ```bash 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 ```bash 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) --- ## 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 --- ## 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 --- ## 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 --- ## 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 --- ## 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. --- ## 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).