SKILL.md — Paper2Protocol Skill Definition

Description

# SKILL.md — Paper2Protocol Skill Definition **Version:** 1.2 **Created:** 2026-03-20 **License:** CC BY-NC 4.0 ## Overview From published high-impact primary literature, reverse-engineer complete experimental validation plans — transforming scientific discoveries into executable research protocols. **Core Principle: Only use primary sources (PMC full-text, journal PDFs), never abstracts or second-hand reviews.** --- ## Input Requirements ### ✅ Accepted - PMC full-text (NCBI PubMed Central, Open Access) - Journal website PDFs (Nature/Science/Cell, peer-reviewed) - DeepReader-generated full-text analysis documents ### ❌ Rejected - Abstracts only - News articles / media interpretations - Review articles (as primary input) - AI-generated summaries (not based on primary sources) ### Input Formats 1. **PMC URL** → Auto-fetch full text 2. **PDF file** → Direct analysis 3. **Paper title** → Search PMC for full text --- ## Workflow (5 Stages) ### Stage 1: Source Acquisition & Quality Assessment 1. Validate input as primary source 2. Fetch full text (PMC API / PDF parsing) 3. Quality rating: - Journal tier (CNS / sub-journal / field-top / other) - Research type (basic / clinical / translational) - Data completeness (supplementary materials, raw data links) - Reproducibility (method detail, sample size) ### Stage 2: Scientific Logic Deconstruction Extract complete scientific logic: 1. **Core Scientific Question**: What problem does this paper solve? 2. **Research Strategy**: Hypothesis, models (in vivo/in vitro/in silico/clinical), key techniques 3. **Validation Chain**: ``` Hypothesis → Key Experiment 1 → Key Experiment 2 → ... → Conclusion ``` Annotate purpose and expected outcome at each node. 4. **Innovation Analysis**: Methodological, conceptual, and application innovations. ### Stage 3: Executable Experimental Paths #### 3.1 Experiment Layering - **Must-do**: Core experiments validating the hypothesis - **Should-do**: Supporting experiments - **Nice-to-do**: Mechanism deep-dives or scope extensions #### 3.2 Per-Experiment Details | Field | Content | |-------|---------| | Experiment Name | Specific name | | Purpose | Role in validation chain | | Method | Detailed protocol (paper Methods + best practices) | | Samples/Materials | Cell lines, animal models, clinical samples | | Sample Size | Statistically required minimum | | Key Reagents | Brand, catalog reference, concentration | | Equipment | Required instruments + alternatives | | Expected Results | Positive/negative controls, data type | | Timeline | Per-experiment duration + replicates | | Budget | Reagents + consumables + services | | Risk Assessment | Failure causes + backup plans | #### 3.3 Bioinformatics Analysis (if applicable) | Field | Content | |-------|---------| | Analysis Goal | Specific task | | Data Source | Public databases (TCGA/GEO) or generated data | | Tools | Recommended pipeline (R/Python/online) | | Key Parameters | Standard settings | | Expected Output | Figure types, statistics | | Compute Resources | Local/server/cloud requirements | #### 3.4 Bioinformatics Code (REQUIRED when analysis involves bioinformatics) **When experiments involve bioinformatics, complete runnable code MUST be provided.** Requirements: - **Language**: R (Bioconductor) or Python (R preferred) - **Completeness**: End-to-end, data download to publication figures - **Comments**: Key steps annotated in English - **Data Sources**: Prioritize public databases (TCGA, GEO, Beat-AML) - **Standard Tools**: ssGSEA/GSEA, DESeq2, CIBERSORTx/xCell, survival, ComplexHeatmap - **Statistical Rigor**: Multiple testing correction (BH), power analysis Coverage: 1. **Subtype Classification**: ssGSEA + K-means/Hierarchical clustering 2. **Differential Expression**: DESeq2/edgeR → volcano plot 3. **Survival Analysis**: Kaplan-Meier + Cox regression + ROC (timeROC) 4. **Gene Enrichment**: GSEA + ssGSEA + Hallmark/Immunologic gene sets 5. **Immune Microenvironment**: CIBERSORTx/xCell deconvolution 6. **Heatmaps**: ComplexHeatmap / pheatmap 7. **Prognostic Models**: LASSO Cox + glmnet + Nomogram (rms) 8. **Flow Cytometry**: FlowJo export → Python statistical analysis 9. **Panel Selection**: LASSO + Random Forest intersection → minimal gene set 10. **Automation**: Bash shell script to chain all analysis steps #### 3.5 Budget Summary ``` Phase 1 (Core Validation): $XX,XXX - Reagents: $X,XXX - Consumables: $X,XXX - Services (sequencing): $XX,XXX - Animals: $X,XXX Phase 2 (Mechanism): $XX,XXX ... Total: $XXX,XXX – $XXX,XXX ``` ### Stage 4: Extension Projects (2-3 proposals) Each includes: - **Project Name** - **Scientific Question** - **Innovation** vs original paper - **Feasibility**: ⭐ rating (technical difficulty, resources, timeline) - **Expected Outcomes**: Paper tier, patent potential, clinical value - **Risk Assessment**: Bottlenecks and failure risks ### Stage 5: Multi-Paper Synthesis (Accumulation Mode) Triggered when ≥3 papers accumulate per topic: - **By Scientific Question**: Group papers by shared research questions - **By Method**: Rank techniques by frequency → prioritize platform setup - **Integrated Roadmap**: Deduplicate protocols, consolidate budgets - **Research Timeline**: 12-month plan based on synthesis --- ## Output Format ### Standard Structure ```markdown # 📋 [Paper Title] → Experimental Validation Plan ## 📄 Paper Information ## 🔬 Part 1: Validation Logic ## 🧪 Part 2: Executable Experimental Paths ## 💻 Part 3: Bioinformatics Code (if applicable) ## 🚀 Part 4: Extension Projects ## 📝 Execution Recommendations ``` ### Output Formats - **Markdown** (default) - **PDF Report** (HTML → browser print, all tables and code blocks) - **Any document platform** (Feishu, Notion, etc.) --- ## Storage & Indexing ``` literature-to-experiment/ ├─ index.json ├─ by_project/ │ └─ [Project Name]/ │ └─ PMCxxxxxx_protocol.md ├─ by_topic/ │ └─ [Topic Name]/ └─ summaries/ └─ [Topic]_synthesis.md ``` --- ## Notes 1. **Pricing**: Based on 2025-2026 market rates, marked "reference price" 2. **Sample Size**: Follows statistical principles, power analysis recommended 3. **Ethics**: Mark IRB/IACUC requirements for human/animal studies 4. **Timeliness**: Flag methods >5 years old for verification 5. **Code**: Must provide complete runnable code for bioinformatics analyses --- ## Dependencies - **DeepReader**: Full-text analysis (pre-requisite step) - **academic-paper**: If integrating plans into papers ## License CC BY-NC 4.0 — Free for academic use with attribution. No commercial use without permission. ## Authors - Jiacheng Lou ([GitHub](https://github.com/ChrisLou-bioinfo)) - 🦞 Claw (AI Research Assistant)

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