improve-codebase-architecture/SKILL.md
Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.
npx skillsauth add kayaman/skills improve-codebase-architectureInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Explore a codebase like an AI would, surface architectural friction, discover opportunities for improving testability, and propose module-deepening refactors as GitHub issue RFCs.
A deep module (John Ousterhout, "A Philosophy of Software Design") has a small interface hiding a large implementation. Deep modules are more testable, more AI-navigable, and let you test at the boundary instead of inside.
Use the Agent tool with subagent_type=Explore to navigate the codebase naturally. Do NOT follow rigid heuristics — explore organically and note where you experience friction:
The friction you encounter IS the signal.
Present a numbered list of deepening opportunities. For each candidate, show:
Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"
Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:
Show this to the user, then immediately proceed to Step 5. The user reads and thinks about the problem while the sub-agents work in parallel.
Produce 3+ radically different interface designs for the deepened module. If the platform supports parallel sub-agents, spawn them concurrently; otherwise work through each design sequentially. Each design must explore a genuinely different shape — not variations of the same idea.
Provide each design with a separate technical brief (file paths, coupling details, dependency category, what's being hidden). Give each design a different constraint:
Each sub-agent outputs:
Present designs sequentially, then compare them in prose.
After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not just a menu.
Before creating the issue, run preflight checks:
gh is authenticated: gh auth statusgh repo view --json nameWithOwner -q .nameWithOwnerOnce confirmed, create a refactor RFC as a GitHub issue using gh issue create. Use the template in reference.md.
tools
Guidance for designing charts, graphs, plots, dashboards, and data visualizations that communicate clearly and persuade. Use when creating or reviewing a visualization, choosing a chart type, picking a color palette, decluttering a busy graphic, fixing misleading axes or proportions, building a dashboard, annotating a figure, or turning data into a presentation, report, or data-driven story. Grounded in the standard data-visualization literature (Knaflic, Tufte, Cleveland & McGill, Cairo, Wilke, Munzner, Few, Berinato). Covers chart selection, graphical perception and encoding, color and accessibility, decluttering, graphical integrity, dashboards, and narrative. Does NOT cover building data pipelines or ETL, statistical modeling or analysis methods, BI tool/vendor selection, or general UI/UX layout (see ux-design-principles). Tool-agnostic, with optional Python recipes.
development
Architect and implement production-grade microservices systems in TypeScript (NestJS) and Python (FastAPI), including resilience, observability, testing, deployment, and migration guidance.
development
--- name: databricks-genie-spaces-best-practices description: Design, configure, curate, govern, monitor, and integrate Databricks AI/BI Genie Spaces — the natural-language-to-SQL surface over Unity Catalog. Covers space scoping, general instructions, parameterized example SQL, SQL functions, trusted assets, JOIN configuration, knowledge store, certified queries, benchmarks, monitoring tab, feedback loops, the Genie Conversation API, governance via Unity Catalog (row filters, column masks, embed
tools
Implement OTP and passwordless authentication on AWS for TypeScript projects using Cognito CUSTOM_AUTH triggers (default) or a custom DynamoDB-backed flow, with SES (email) and SNS (SMS) delivery. Use when the user mentions OTP, one-time password, passwordless login, magic link, Cognito custom auth, DefineAuthChallenge, CreateAuthChallenge, VerifyAuthChallengeResponse, SES verification email, SNS SMS code, or MFA over email/SMS. Covers architecture decision (Cognito vs custom), Lambda trigger handlers, SES/SNS notifiers, DynamoDB schema with TTL, rate limiting, constant-time comparison, threat model (enumeration, replay, brute force), and aws-sdk-client-mock testing.