# Academic Paper Strategist

> Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writin…

- **Type:** Skill
- **Install:** `agentstack add skill-lishix520-academic-paper-skills-strategist`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [lishix520](https://agentstack.voostack.com/s/lishix520)
- **Installs:** 0
- **Category:** [Search](https://agentstack.voostack.com/c/search)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [lishix520](https://github.com/lishix520)
- **Source:** https://github.com/lishix520/academic-paper-skills/tree/main/strategist

## Install

```sh
agentstack add skill-lishix520-academic-paper-skills-strategist
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 pape

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [lishix520](https://github.com/lishix520)
- **Source:** [lishix520/academic-paper-skills](https://github.com/lishix520/academic-paper-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-lishix520-academic-paper-skills-strategist
- Seller: https://agentstack.voostack.com/s/lishix520
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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