Skillquality 0.46
jup-architect
Use when analyzing the jup codebase, identifying technical debt, suggesting architectural improvements, or ensuring high engineering standards.
What it does
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
Contextfor state management and shared services. Avoid global state where possible. Useverbose_statesparingly. - Type Safety: Use Pydantic v2 for all data models and configuration. Leverage Python 3.12+ type hints throughout.
- Robust Error Handling: Replace
printwith 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.mockto 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
~/.jupand the lockfile are managed. Ensure the lockfile schema is stable and migration-friendly. - Dependency Management: Review the use of
uvand external libraries. Minimize unnecessary dependencies. - Testing Strategy: Suggest improvements to the current
pytestsuite 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.
Capabilities
skillsource-andraderskill-jup-architecttopic-agenttopic-agent-skillstopic-agentic-aitopic-agentstopic-ai-agentstopic-clitopic-installertopic-managertopic-skills
Install
Installnpx skills add andrader/jup
Transportskills-sh
Protocolskill
Quality
0.46/ 1.00
deterministic score 0.46 from registry signals: · indexed on github topic:agent-skills · 10 github stars · SKILL.md body (2,990 chars)
Provenance
Indexed fromgithub
Enriched2026-04-24 07:03:32Z · deterministic:skill-github:v1 · v1
First seen2026-04-23
Last seen2026-04-24