Agent skill

paper-first-principles

Convert academic papers into engineer-friendly progressive documentation

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/paper-first-principles

SKILL.md

Paper First Principles

Convert academic papers into progressive, engineer-friendly documentation using first principles thinking.

Quick Start

bash
# Basic usage
kimi paper-first-principles https://arxiv.org/abs/xxxx.xxxxx

# Engineer perspective with domain focus
kimi paper-first-principles paper.pdf --audience engineer --domain distributed-systems

# Output to file
kimi paper-first-principles paper.pdf --output ./docs/analysis.md

Output Structure

Generated documents contain 8 standard sections:

Section Content Audience
Opening One-sentence core insight All
Mechanism Breakdown Comparative analysis tables Engineers, Researchers
First Principles Problem essence and design rationale All
Progressive Deep Dive Layered complexity (simple → complex) Engineers, Researchers
Edge Cases Common pitfalls and misconceptions Engineers
Decision Tree When/how to apply Engineers, Managers
Engineering Checklist Actionable verification items Engineers
Summary Reusable design patterns All

Paper Types & Progressive Paths

Type Characteristics Progressive Path
Algorithm New algorithms/models Example → Core mechanism → Optimizations
System Architecture/engineering Single-node → Distributed → Production
Theory Theoretical analysis Problem → Theorem → Proof → Application

Parameters

--audience

  • engineer: Implementation details, design patterns, edge cases
  • researcher: Technical depth, related work comparison, theory
  • manager: Problem context, decision rationale, risk assessment

--domain (optional)

Engineering domain for contextual mapping:

  • distributed-systems: Distributed systems, microservices
  • storage: Storage systems, file systems
  • database: Databases, data warehouses
  • network: Networks, CDN, load balancing
  • ml-system: ML systems, recommendation systems

Analysis Workflow

This skill processes papers in 6 stages using prompts in prompts/:

  1. Core Extraction (extract_core.txt): Identify contributions and key decisions
  2. First Principles (first_principles.txt): Trace problem essence and rationale
  3. Progressive Layers (progressive_layers.txt): Organize by complexity
  4. Engineering Map (engineering_map.txt): Map to software patterns (engineer audience only)

Output Templates

Templates in templates/ provide structure for each paper type:

  • system.md: System papers (infrastructure, architecture)
  • algorithm.md: Algorithm papers (models, methods)
  • theory.md: Theory papers (analysis, proofs)

Examples

See examples/attention_residuals.md for a complete example converting the Attention Residuals paper into an engineer-friendly analysis with distributed systems mappings.

Resource Loading Guide

Always load in this order:

  1. Parse paperExtract core (use prompts/extract_core.txt)
  2. First principles analysis (use prompts/first_principles.txt)
  3. Progressive organization (use prompts/progressive_layers.txt)
  4. Engineering mapping (if --audience engineer, use prompts/engineering_map.txt)
  5. Generate output (use appropriate template from templates/)

Constraints & Notes

  • Paper quality matters: clear abstract and introduction required
  • Output depth auto-adjusts based on --audience
  • Engineering mapping accuracy depends on --domain setting
  • Complex proofs may need manual verification

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