validation-patterns

Name: validation-patterns
Availability: InStock
Author: Hamza-Haadi

Validate Kailash SDK workflows for parameter, connection, import, and security compliance.

Updated Apr 2, 2026

Search Tags:#workflow #code review #validation #compliance #security audit #kailash sdk #parameter checking

Ready to run

npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill validation-patterns-hamza-haadi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: validation-patterns
Source: https://github.com/Hamza-Haadi/disease-risk-classifier-hamza/tree/main/.claude/skills/16-validation-patterns
Command: npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill validation-patterns-hamza-haadi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Workflow code built with the Kailash SDK often fails at runtime due to missing parameters, malformed connections, relative imports, or security risks like hardcoded secrets and SQL injection. This Skill provides validation patterns and checklists that catch these issues before execution, commit, or deployment. ## Core Features & Use Cases - Parameter & Connection Validation: Verify required node parameters, types, and the 4-parameter connection format before calling workflow.build(). - Pattern Compliance Checking: Enforce DataFlow result access patterns, absolute imports, string-based node APIs, and snake_case naming conventions. - Security Auditing: Detect hardcoded secrets, SQL injection risks, code injection risks, and file path traversal in workflow definitions. - Use Case: Before committing a new workflow, run the validation checklist to confirm all nodes use string IDs, connections use 4 parameters, .build() is called before execute, and no API keys are hardcoded. ## Quick Start Ask the AI to validate your Kailash workflow code against the gold standards checklist and report any violations with fixes.

Frequently Asked Questions about validation-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate a Kailash workflow before execution?▼

Call workflow.build() before runtime.execute(), which raises WorkflowValidationError for invalid parameters or connections. You can also run the pre-execution checklist covering required parameters, 4-parameter connections, unique node IDs, and circular dependency detection.

How to check workflow connections for errors in Kailash SDK?▼

Use the 4-parameter connection format add_connection(source_id, source_param, target_id, target_param) and verify both nodes and parameters exist. Common issues include missing connections, invalid node IDs, circular dependencies, and unreachable nodes.

What security checks should I run on workflow code?▼

Check for hardcoded secrets, SQL injection from string-concatenated queries, code injection in PythonCodeNode, and file path traversal. Use environment variables for API keys and parameterized queries for database operations.

Why does my workflow fail with WorkflowValidationError?▼

This error occurs from missing required parameters, invalid parameter types, unknown parameters, or connection type mismatches. Define parameter contracts with get_parameters() in custom nodes and validate business logic constraints inside the run() method.

Does DataFlow validation require specific result access patterns?▼

Yes, DataFlow results must use the results["node_id"]["result"] dictionary access pattern, not attribute access like results["node_id"].result. String IDs must be preserved and each database should use one DataFlow instance.