ai-error-handling

Platform Notes

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

AI Error Handling & Validation

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

Use When

• Validation and error handling for AI-generated code. Use when verifying AI output, building production code, or ensuring code correctness. Enforces automatic quality checks and validation loops.
• 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-handling 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

Category	Artifact	Format	Example
Correctness	AI output validation test plan	Markdown doc covering schema validation, hallucination detection, and fallback paths	docs/ai/output-validation-tests.md

References

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

When to Use This Skill

Use when:

• Claude generates code (always validate)
• Building production code (quality gates required)
• Reviewing AI output (systematic verification)
• Ensuring code correctness (automated checks)

This skill automatically enforces validation patterns.

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The 5-Layer Validation Stack

Every AI-generated code MUST pass through all 5 layers:

Layer 1: Syntax Check ─→ Can it parse?
    ↓
Layer 2: Requirement Check ─→ Does it meet specs?
    ↓
Layer 3: Test Check ─→ Do tests pass?
    ↓
Layer 4: Security Check ─→ Any vulnerabilities?
    ↓
Layer 5: Documentation Check ─→ Can Claude explain it?
    ↓
APPROVED ✓

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Additional Guidance

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

Use that deep dive for:

• Layer 1: Syntax Validation
• Layer 2: Requirement Validation
• Layer 3: Test Validation
• Layer 4: Security Validation
• Input Validation
• SQL Injection Prevention
• XSS Prevention
• Authentication & Authorization
• Data Exposure
• Error Handling
• Layer 5: Documentation Validation
• The Validation Loop
• Additional deep-dive sections continue in the reference file.