ai-error-prevention（AI 错误预防）

Platform Notes

• Optional helper plugins may help in some environments, but they must not be treated as required for this skill.

AI Error Prevention in Software Development

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Error prevention strategies for AI-assisted development. Use when working with Claude to minimize hallucinations, incomplete solutions, and wasted tokens. Enforces "trust but verify" workflow.
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to ai-error-prevention or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

References

• Use the references/ directory for deep detail after reading the core workflow below.

Overview

This skill teaches you to prevent errors BEFORE they happen when working with Claude to generate code. It focuses on minimizing wasted tokens, catching Claude's mistakes early, and ensuring production-ready output.

Documentation Structure (Tier 2 Deep Dives):

• 📖 prevention-strategies.md - The 7 prevention strategies with detailed examples
• 📖 failure-modes.md - Common Claude failures and how to prevent them
• 📖 app-specific-prevention.md - Prevention for MADUUKA, MEDIC8, BRIGHTSOMA, DDA

When to Use This Skill

Always use when:

• Working with Claude to generate code
• Building software with AI assistance
• Want to minimize wasted tokens on wrong solutions
• Need to catch Claude's mistakes early
• Developing production-ready code with AI

This skill prevents errors BEFORE they happen.

Additional Guidance

Extended guidance for ai-error-prevention was moved to references/skill-deep-dive.md to keep this entrypoint compact and fast to load.

Use that deep dive for:

• The Core Problem
• The 7 Prevention Strategies (Quick Reference)
• Common Claude Failure Modes (Summary)
• AI Development Error Prevention Framework
• App-Specific Prevention (Summary)
• The Golden Rule
• Acceptance Checklist
• Token Waste Prevention
• Integration with Other Skills
• Summary