/strategist

name: academic-paper-strategist
description: '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 writing standards. Triggered by phrases like ''plan a paper on,'' ''help me design a paper about,'' ''identify research gaps in,'' ''is this idea original,'' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).'

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来源仓库: https://github.com/lishix520/academic-paper-skills 项目名称: academic-paper-skills 开源协议: MIT License 收录日期: 2026-04-02

声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。 -->

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→