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- Ai Development Guide
ai-development-guide_skill
102
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
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Installation
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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.