dqz00116/skill-lib/paper-first-principles

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Use when converting academic papers into engineer-friendly documentation, extracting design patterns from research, or preparing technical knowledge sharing

Install

npx vskill@latest install dqz00116/skill-lib/paper-first-principles

SKILL.md

Paper First Principles

Overview

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

When to Use

Use when:

  • You need to understand a research paper deeply from an engineering perspective
  • Extracting design patterns, system architectures, or algorithms from academic literature
  • Preparing technical knowledge sharing or documentation for engineering teams
  • Mapping theoretical research to practical software engineering applications

Don't use for:

  • Papers without clear technical contributions (survey papers, opinion pieces)
  • One-off reading without need for structured documentation
  • Standard practices already well-documented elsewhere

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:

SectionContentAudience
OpeningOne-sentence core insightAll
Mechanism BreakdownComparative analysis tablesEngineers, Researchers
First PrinciplesProblem essence and design rationaleAll
Progressive Deep DiveLayered complexity (simple → complex)Engineers, Researchers
Edge CasesCommon pitfalls and misconceptionsEngineers
Decision TreeWhen/how to applyEngineers, Managers
Engineering ChecklistActionable verification itemsEngineers
SummaryReusable design patternsAll

Paper Types & Progressive Paths

TypeCharacteristicsProgressive Path
AlgorithmNew algorithms/modelsExample → Core mechanism → Optimizations
SystemArchitecture/engineeringSingle-node → Distributed → Production
TheoryTheoretical analysisProblem → 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 paper → Extract 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

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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