Jup architect

jup is a command-line tool for installing and managing agent skills across different ai agents, scopes, and projects.

Install

npx -y skills add andrader/jup --skill jup-architect

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Use when analyzing the jup codebase, identifying technical debt, suggesting architectural improvements, or ensuring high engineering standards.

SKILL.md

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jup Expert Architect Assistant 🏗️

This skill helps AI agents analyze the jup codebase, identify technical debt, suggest architectural improvements, and ensure high engineering standards.

How to Use This Skill

Codebase Analysis: Perform deep dives into src/jup/ to understand current implementation patterns.
Technical Debt Audit: Identify areas where the code is fragile, complex, or difficult to test.
Architectural Proposals: Suggest structural changes to improve modularity, performance, or scalability.
Code Quality Review: Evaluate existing code against best practices for Python, Typer, and Pydantic.

Core Architectural Values

Modularity: Maintain clear boundaries between CLI commands (commands/), configuration (config.py), and data models (models.py). Commands should focus on CLI interaction, while logic should be extracted to utilities or models where possible.
Idiomatic Typer: Leverage Typer's Context for state management and shared services. Avoid global state where possible. Use verbose_state sparingly.
Type Safety: Use Pydantic v2 for all data models and configuration. Leverage Python 3.12+ type hints throughout.
Robust Error Handling: Replace print with structured logging or custom exceptions when appropriate. Ensure the CLI fails gracefully with helpful error messages and non-zero exit codes.
Testability: Prioritize designs that are easy to unit test. Use unittest.mock to isolate dependencies in tests.

Key Areas of Focus

Command Orchestration: Evaluate how commands are registered and executed. Ensure the commands/ package is easy to extend.
Data Persistence: Analyze how ~/.jup and the lockfile are managed. Ensure the lockfile schema is stable and migration-friendly.
Dependency Management: Review the use of uv and external libraries. Minimize unnecessary dependencies.
Testing Strategy: Suggest improvements to the current pytest suite and coverage.

Process Flow

digraph architect_flow {
    "Deep dive src/jup/" [shape=box];
    "Identify tech debt/bottlenecks" [shape=box];
    "Propose improvements" [shape=box];
    "Align with jup values?" [shape=diamond];
    "Refine proposal" [shape=box];
    "Create architectural spec" [shape=box];

    "Deep dive src/jup/" -> "Identify tech debt/bottlenecks";
    "Identify tech debt/bottlenecks" -> "Propose improvements";
    "Propose improvements" -> "Align with jup values?";
    "Align with jup values?" -> "Refine proposal" [label="no"];
    "Refine proposal" -> "Align with jup values?";
    "Align with jup values?" -> "Create architectural spec" [label="yes"];
}

Deliverables

Architectural Specs: Detailed specifications for structural changes in docs/superpowers/specs/.
Refactoring Plans: Implementation plans for cleaning up technical debt in docs/superpowers/plans/.
Code Quality Reports: Summaries of findings and recommendations.