ai-development-guide_skill

This skill guides technical decision making, detects anti-patterns, and improves code quality and QA workflows for AI development projects.

102

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill shinpr/claude-code-workflows --skill ai-development-guide

  • SKILL.md11.8 KB

Overview

This skill provides a compact, actionable developer guide for technical decision criteria, anti-pattern detection, debugging techniques, and a language-agnostic quality check workflow. It helps teams detect code smells, enforce fail-fast error handling, and run a repeatable QA pipeline before shipping. Use it to reduce technical debt and improve reliability during design and implementation.

How this skill works

The skill inspects code and designs against a catalog of red-flag anti-patterns (duplication, mixed responsibilities, error masking, etc.), enforces fail-fast fallback principles, and guides root-cause debugging via structured methods (5 Whys, minimal repro, contextual logs). It also prescribes a four-phase quality check workflow—static analysis, build verification, testing, and a final quality gate—and a three-stage impact analysis before implementation.

When to use it

  • Choosing architecture or making non-trivial technical decisions
  • Before implementing fallbacks or error-handling logic
  • Performing code reviews to find anti-patterns and duplication
  • Planning debugging and root-cause analysis for persistent failures
  • Running pre-release quality checks and CI pipelines

Best practices

  • Stop and review on red-flag patterns: repeated code, mixed responsibilities, error suppression
  • Prefer explicit failures with preserved error context; log then propagate rather than returning silent defaults
  • Apply Rule of Three: refactor only after a pattern appears at least three times
  • Record certainty and exploratory notes when introducing uncertain technology
  • Follow the mandatory 3-stage impact analysis (Discovery → Understanding → Identification) before changing shared code

Example use cases

  • Design doc review for a new fallback path in a distributed service
  • Code review that surfaces duplicated validation logic across modules
  • Debugging a production failure using minimal reproduction and 5 Whys
  • CI pipeline configuration enforcing format → lint → static analysis → tests → final gate
  • Deciding whether to extract shared functionality after repeated implementations

FAQ

Only when explicitly approved in the design doc, for business-critical continuity, or when a documented graceful degradation path exists with monitoring for activation.

How do I decide to commonalize code?

Follow Rule of Three: consolidate after a third similar implementation, and consider readability, future divergence, and business logic overlap before extracting.

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ai-development-guide skill by shinpr/claude-code-workflows | VeilStrat