dqz00116/skill-lib/paper-first-principles
Claude Code and agent skill by dqz00116/skill-lib
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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-principlesSKILL.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
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 casesresearcher: Technical depth, related work comparison, theorymanager: Problem context, decision rationale, risk assessment
--domain (optional)
Engineering domain for contextual mapping:
distributed-systems: Distributed systems, microservicesstorage: Storage systems, file systemsdatabase: Databases, data warehousesnetwork: Networks, CDN, load balancingml-system: ML systems, recommendation systems
Analysis Workflow
This skill processes papers in 6 stages using prompts in prompts/:
- Core Extraction (
extract_core.txt): Identify contributions and key decisions - First Principles (
first_principles.txt): Trace problem essence and rationale - Progressive Layers (
progressive_layers.txt): Organize by complexity - 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:
- Parse paper → Extract core (use
prompts/extract_core.txt) - First principles analysis (use
prompts/first_principles.txt) - Progressive organization (use
prompts/progressive_layers.txt) - Engineering mapping (if
--audience engineer, useprompts/engineering_map.txt) - 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
--domainsetting - Complex proofs may need manual verification
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingpaper-first-principles/SKILL.md
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