Claude Code Input Validation and Sanitization Patterns Guide

Building reliable Claude Code skills requires careful attention to how data flows through your prompts, tools, and outputs. Input validation and sanitization patterns protect your skills from malformed data, injection attacks, and unexpected behavior that can break your automation workflows.

Why Input Validation Matters for Claude Skills

Claude Code skills interact with external systems through tools, MCP servers, and APIs. Each interface point represents a potential vulnerability where unexpected input can cause failures or security issues. When you chain multiple skills together—like using the tdd skill for test generation followed by the frontend-design skill for UI creation—the data passing between them must be predictable and safe.

Without proper validation, a malformed response from one skill can cascade into failures across your entire workflow. The supermemory skill, for instance, relies on clean data structures to store and retrieve context. Corrupted input can corrupt your persisted knowledge base.

Core Validation Patterns

Type Checking and Schema Validation

The foundation of input validation begins with type checking When a skill receives data from an external source—whether from user input, API responses, or file contents—you must verify the data matches expected types before processing.

// Validate incoming data structure
function validateInput(input, schema) {
  if (typeof input !== 'object' || input === null) {
    return { valid: false, error: 'Input must be an object' };
  }
  
  for (const [key, type] of Object.entries(schema)) {
    if (typeof input[key] !== type) {
      return { valid: false, error: `Expected ${key} to be ${type}` };
    }
  }
  
  return { valid: true };
}

// Usage example
const result = validateInput(userData, {
  name: 'string',
  age: 'number',
  email: 'string'
});

This pattern becomes essential when building skills that accept user-provided configurations. The pdf skill, for example, needs to validate page ranges, output formats, and file paths before attempting document generation.

Range and Boundary Validation

Numeric inputs require boundary checks. Always validate that numbers fall within acceptable ranges, especially when those values affect loop counts, timeouts, or resource allocation.

function validateNumericRange(value, min, max) {
  const num = Number(value);
  if (isNaN(num)) {
    return { valid: false, error: 'Value must be a number' };
  }
  if (num < min || num > max) {
    return { valid: false, error: `Value must be between ${min} and ${max}` };
  }
  return { valid: true, value: num };
}

// Validate timeout between 100ms and 30000ms
validateNumericRange(timeout, 100, 30000);

This pattern protects against denial-of-service scenarios where excessive resource requests could freeze your Claude Code session.

Sanitization Techniques

String Sanitization

User-provided strings often contain unwanted characters, excessive whitespace, or potential injection payloads. Sanitization transforms raw input into safe, predictable strings.

function sanitizeString(input, maxLength = 1000) {
  if (typeof input !== 'string') {
    return '';
  }
  
  return input
    .slice(0, maxLength)                    // Limit length
    .replace(/[\x00-\x1F\x7F]/g, '')         // Remove control characters
    .trim();                                 // Remove leading/trailing whitespace
}

function sanitizeFilename(filename) {
  return filename
    .replace(/[^a-zA-Z0-9._-]/g, '_')       // Replace unsafe chars
    .replace(/\.{2,}/g, '.')                 // Prevent path traversal
    .slice(0, 255);                          // Limit length
}

When using skills that generate files—like the canvas-design skill or any skill creating output artifacts—sanitizing filenames prevents path traversal vulnerabilities.

HTML and Markdown Sanitization

If your skill outputs HTML or markdown that gets rendered in downstream systems, you need to sanitize potentially dangerous content.

function sanitizeHtml(input) {
  return input
    .replace(/</g, '&lt;')
    .replace(/>/g, '&gt;')
    .replace(/"/g, '&quot;')
    .replace(/'/g, '&#x27;')
    .replace(/\//g, '&#x2F;');
}

function sanitizeMarkdown(input) {
  // Remove potentially dangerous markdown
  return input
    .replace(/\[.*\]\(javascript:.*\)/gi, '[blocked]')
    .replace(/<script\b[^<]*(?:(?!<\/script>)<[^<]*)*<\/script>/gi, '');
}

The docx skill and pptx skill often generate documents from user-provided templates. Sanitizing template variables prevents script injection if those documents get opened in vulnerable viewers.

Pattern: Defensive Skill Chaining

When chaining multiple skills together, each skill should validate input before processing and sanitize output before passing to the next skill.

User Input → [Skill A: Validate] → [Skill A: Process] → [Skill A: Sanitize] → 
[Skill B: Validate] → [Skill B: Process] → [Skill B: Sanitize] → Final Output

This defensive approach ensures that even if one skill receives unexpected data, it either transforms it into something safe or fails gracefully with a clear error message.

Example: Multi-Skill Workflow Validation

Consider a workflow using the xlsx skill to generate spreadsheets from user data, then the pdf skill to convert those spreadsheets into reports:

// Validate data before passing to xlsx skill
function prepareSpreadsheetData(rawData) {
  const sanitized = {
    rows: [],
    headers: sanitizeString(rawData.headers || 'Date,Value,Description', 100)
  };
  
  for (const row of (rawData.rows || []).slice(0, 10000)) {
    sanitized.rows.push({
      date: sanitizeString(row.date, 20),
      value: validateNumericRange(row.value, -999999999, 999999999).value || 0,
      description: sanitizeString(row.description, 500)
    });
  }
  
  return sanitized;
}

This preparation function ensures the xlsx skill receives predictable, bounded data regardless of what the user originally provided.

Error Handling and Graceful Degradation

Validation failures should never crash your skill unexpectedly. Implement graceful degradation that provides useful feedback:

function safeProcess(input, validators, processor) {
  for (const validator of validators) {
    const result = validator(input);
    if (!result.valid) {
      return { 
        success: false, 
        error: result.error,
        partial: null 
      };
    }
  }
  
  try {
    const output = processor(input);
    return { success: true, output };
  } catch (err) {
    return { 
      success: false, 
      error: `Processing failed: ${err.message}`,
      partial: null 
    };
  }
}

This pattern allows skills like the tdd skill to handle malformed test specifications by providing helpful error messages rather than failing silently or crashing.

Practical Implementation Tips

Start with validation at skill boundaries—the points where your skill receives input from Claude Code’s tool system or sends output to external systems. Focus validation effort where data crosses trust boundaries.

Use schema validation libraries like Zod or Joi when building complex skills with many input fields. These libraries provide reusable validation logic across your skill implementations.

Log validation failures to understand what inputs cause problems in production. This feedback loop helps you improve validation rules over time, especially when users provide unexpected data formats.

Finally, remember that validation adds latency. Balance thoroughness with performance by applying stricter validation to untrusted external input while trusting data from within your own skill chain.

Claude Skills for Enterprise Security and Compliance — Enterprise security patterns that build on solid input validation foundations
Best Claude Skills for Developers 2026 — Developer skills including tdd for test-driven validation pattern development
Claude Skills Token Optimization: Reduce API Costs — Efficiently automate validation code generation without runaway API costs

Built by theluckystrike — More at zovo.one