Deployment Validation Config Validate logo

Deployment Validation Config Validate

Community
huanchain
deployment-validation-config-validate

You are a configuration management expert specializing in validating, testing, and ensuring the correctness of application configurations. Create comprehensive validation schemas, implement configurat

Overview

Publisherhuanchain
Repositoryantigravity-skills
Skill namedeployment-validation-config-validate
Stars
84
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by huanchain on GitHub. Read the source before you install it.

Installation

Install the Deployment Validation Config Validate AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huanchain/antigravity-skills/tree/main/skills/deployment-validation-config-validate
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huanchain/antigravity-skills.git /tmp/antigravity-skills
mkdir -p .claude/skills
cp -r /tmp/antigravity-skills/skills/deployment-validation-config-validate .claude/skills/deployment-validation-config-validate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deployment Validation Config Validate in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deployment Validation Config Validate on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deployment Validation Config Validate is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Configuration Validation

You are a configuration management expert specializing in validating, testing, and ensuring the correctness of application configurations. Create comprehensive validation schemas, implement configuration testing strategies, and ensure configurations are secure, consistent, and error-free across all environments.

Use this skill when

  • Working on configuration validation tasks or workflows
  • Needing guidance, best practices, or checklists for configuration validation

Do not use this skill when

  • The task is unrelated to configuration validation
  • You need a different domain or tool outside this scope

Context

The user needs to validate configuration files, implement configuration schemas, ensure consistency across environments, and prevent configuration-related errors. Focus on creating robust validation rules, type safety, security checks, and automated validation processes.

Requirements

$ARGUMENTS

Instructions

1. Configuration Analysis

Analyze existing configuration structure and identify validation needs:

python
import os
import yaml
import json
from pathlib import Path
from typing import Dict, List, Any

class ConfigurationAnalyzer:
    def analyze_project(self, project_path: str) -> Dict[str, Any]:
        analysis = {
            'config_files': self._find_config_files(project_path),
            'security_issues': self._check_security_issues(project_path),
            'consistency_issues': self._check_consistency(project_path),
            'recommendations': []
        }
        return analysis

    def _find_config_files(self, project_path: str) -> List[Dict]:
        config_patterns = [
            '**/*.json', '**/*.yaml', '**/*.yml', '**/*.toml',
            '**/*.ini', '**/*.env*', '**/config.js'
        ]

        config_files = []
        for pattern in config_patterns:
            for file_path in Path(project_path).glob(pattern):
                if not self._should_ignore(file_path):
                    config_files.append({
                        'path': str(file_path),
                        'type': self._detect_config_type(file_path),
                        'environment': self._detect_environment(file_path)
                    })
        return config_files

    def _check_security_issues(self, project_path: str) -> List[Dict]:
        issues = []
        secret_patterns = [
            r'(api[_-]?key|apikey)',
            r'(secret|password|passwd)',
            r'(token|auth)',
            r'(aws[_-]?access)'
        ]

        for config_file in self._find_config_files(project_path):
            content = Path(config_file['path']).read_text()
            for pattern in secret_patterns:
                if re.search(pattern, content, re.IGNORECASE):
                    if self._looks_like_real_secret(content, pattern):
                        issues.append({
                            'file': config_file['path'],
                            'type': 'potential_secret',
                            'severity': 'high'
                        })
        return issues

2. Schema Validation

Implement configuration schema validation with JSON Schema:

typescript
import Ajv from 'ajv';
import ajvFormats from 'ajv-formats';
import { JSONSchema7 } from 'json-schema';

interface ValidationResult {
  valid: boolean;
  errors?: Array<{
    path: string;
    message: string;
    keyword: string;
  }>;
}

export class ConfigValidator {
  private ajv: Ajv;

  constructor() {
    this.ajv = new Ajv({
      allErrors: true,
      strict: false,
      coerceTypes: true
    });
    ajvFormats(this.ajv);
    this.addCustomFormats();
  }

  private addCustomFormats() {
    this.ajv.addFormat('url-https', {
      type: 'string',
      validate: (data: string) => {
        try {
          return new URL(data).protocol === 'https:';
        } catch { return false; }
      }
    });

    this.ajv.addFormat('port', {
      type: 'number',
      validate: (data: number) => data >= 1 && data <= 65535
    });

    this.ajv.addFormat('duration', {
      type: 'string',
      validate: /^\d+[smhd]$/
    });
  }

  validate(configData: any, schemaName: string): ValidationResult {
    const validate = this.ajv.getSchema(schemaName);
    if (!validate) throw new Error(`Schema '${schemaName}' not found`);

    const valid = validate(configData);

    if (!valid && validate.errors) {
      return {
        valid: false,
        errors: validate.errors.map(error => ({
          path: error.instancePath || '/',
          message: error.message || 'Validation error',
          keyword: error.keyword
        }))
      };
    }
    return { valid: true };
  }
}

// Example schema
export const schemas = {
  database: {
    type: 'object',
    properties: {
      host: { type: 'string', format: 'hostname' },
      port: { type: 'integer', format: 'port' },
      database: { type: 'string', minLength: 1 },
      user: { type: 'string', minLength: 1 },
      password: { type: 'string', minLength: 8 },
      ssl: {
        type: 'object',
        properties: {
          enabled: { type: 'boolean' }
        },
        required: ['enabled']
      }
    },
    required: ['host', 'port', 'database', 'user', 'password']
  }
};

3. Environment-Specific Validation

python
from typing import Dict, List, Any

class EnvironmentValidator:
    def __init__(self):
        self.environments = ['development', 'staging', 'production']
        self.environment_rules = {
            'development': {
                'allow_debug': True,
                'require_https': False,
                'min_password_length': 8
            },
            'production': {
                'allow_debug': False,
                'require_https': True,
                'min_password_length': 16,
                'require_encryption': True
            }
        }

    def validate_config(self, config: Dict, environment: str) -> List[Dict]:
        if environment not in self.environment_rules:
            raise ValueError(f"Unknown environment: {environment}")

        rules = self.environment_rules[environment]
        violations = []

        if not rules['allow_debug'] and config.get('debug', False):
            violations.append({
                'rule': 'no_debug_in_production',
                'message': 'Debug mode not allowed in production',
                'severity': 'critical'
            })

        if rules['require_https']:
            urls = self._extract_urls(config)
            for url_path, url in urls:
                if url.startswith('http://') and 'localhost' not in url:
                    violations.append({
                        'rule': 'require_https',
                        'message': f'HTTPS required for {url_path}',
                        'severity': 'high'
                    })

        return violations

4. Configuration Testing

typescript
import { describe, it, expect } from '@jest/globals';
import { ConfigValidator } from './config-validator';

describe('Configuration Validation', () => {
  let validator: ConfigValidator;

  beforeEach(() => {
    validator = new ConfigValidator();
  });

  it('should validate database config', () => {
    const config = {
      host: 'localhost',
      port: 5432,
      database: 'myapp',
      user: 'dbuser',
      password: 'securepass123'
    };

    const result = validator.validate(config, 'database');
    expect(result.valid).toBe(true);
  });

  it('should reject invalid port', () => {
    const config = {
      host: 'localhost',
      port: 70000,
      database: 'myapp',
      user: 'dbuser',
      password: 'securepass123'
    };

    const result = validator.validate(config, 'database');
    expect(result.valid).toBe(false);
  });
});

5. Runtime Validation

typescript
import { EventEmitter } from 'events';
import * as chokidar from 'chokidar';

export class RuntimeConfigValidator extends EventEmitter {
  private validator: ConfigValidator;
  private currentConfig: any;

  async initialize(configPath: string): Promise<void> {
    this.currentConfig = await this.loadAndValidate(configPath);
    this.watchConfig(configPath);
  }

  private async loadAndValidate(configPath: string): Promise<any> {
    const config = await this.loadConfig(configPath);

    const validationResult = this.validator.validate(
      config,
      this.detectEnvironment()
    );

    if (!validationResult.valid) {
      this.emit('validation:error', {
        path: configPath,
        errors: validationResult.errors
      });

      if (!this.isDevelopment()) {
        throw new Error('Configuration validation failed');
      }
    }

    return config;
  }

  private watchConfig(configPath: string): void {
    const watcher = chokidar.watch(configPath, {
      persistent: true,
      ignoreInitial: true
    });

    watcher.on('change', async () => {
      try {
        const newConfig = await this.loadAndValidate(configPath);

        if (JSON.stringify(newConfig) !== JSON.stringify(this.currentConfig)) {
          this.emit('config:changed', {
            oldConfig: this.currentConfig,
            newConfig
          });
          this.currentConfig = newConfig;
        }
      } catch (error) {
        this.emit('config:error', { error });
      }
    });
  }
}

6. Configuration Migration

python
from typing import Dict
from abc import ABC, abstractmethod
import semver

class ConfigMigration(ABC):
    @property
    @abstractmethod
    def version(self) -> str:
        pass

    @abstractmethod
    def up(self, config: Dict) -> Dict:
        pass

    @abstractmethod
    def down(self, config: Dict) -> Dict:
        pass

class ConfigMigrator:
    def __init__(self):
        self.migrations: List[ConfigMigration] = []

    def migrate(self, config: Dict, target_version: str) -> Dict:
        current_version = config.get('_version', '0.0.0')

        if semver.compare(current_version, target_version) == 0:
            return config

        result = config.copy()
        for migration in self.migrations:
            if (semver.compare(migration.version, current_version) > 0 and
                semver.compare(migration.version, target_version) <= 0):
                result = migration.up(result)
                result['_version'] = migration.version

        return result

7. Secure Configuration

typescript
import * as crypto from 'crypto';

interface EncryptedValue {
  encrypted: true;
  value: string;
  algorithm: string;
  iv: string;
  authTag?: string;
}

export class SecureConfigManager {
  private encryptionKey: Buffer;

  constructor(masterKey: string) {
    this.encryptionKey = crypto.pbkdf2Sync(masterKey, 'config-salt', 100000, 32, 'sha256');
  }

  encrypt(value: any): EncryptedValue {
    const algorithm = 'aes-256-gcm';
    const iv = crypto.randomBytes(16);
    const cipher = crypto.createCipheriv(algorithm, this.encryptionKey, iv);

    let encrypted = cipher.update(JSON.stringify(value), 'utf8', 'hex');
    encrypted += cipher.final('hex');

    return {
      encrypted: true,
      value: encrypted,
      algorithm,
      iv: iv.toString('hex'),
      authTag: cipher.getAuthTag().toString('hex')
    };
  }

  decrypt(encryptedValue: EncryptedValue): any {
    const decipher = crypto.createDecipheriv(
      encryptedValue.algorithm,
      this.encryptionKey,
      Buffer.from(encryptedValue.iv, 'hex')
    );

    if (encryptedValue.authTag) {
      decipher.setAuthTag(Buffer.from(encryptedValue.authTag, 'hex'));
    }

    let decrypted = decipher.update(encryptedValue.value, 'hex', 'utf8');
    decrypted += decipher.final('utf8');

    return JSON.parse(decrypted);
  }

  async processConfig(config: any): Promise<any> {
    const processed = {};

    for (const [key, value] of Object.entries(config)) {
      if (this.isEncryptedValue(value)) {
        processed[key] = this.decrypt(value as EncryptedValue);
      } else if (typeof value === 'object' && value !== null) {
        processed[key] = await this.processConfig(value);
      } else {
        processed[key] = value;
      }
    }

    return processed;
  }
}

8. Documentation Generation

python
from typing import Dict, List
import yaml

class ConfigDocGenerator:
    def generate_docs(self, schema: Dict, examples: Dict) -> str:
        docs = ["# Configuration Reference\n"]

        docs.append("## Configuration Options\n")
        sections = self._generate_sections(schema.get('properties', {}), examples)
        docs.extend(sections)

        return '\n'.join(docs)

    def _generate_sections(self, properties: Dict, examples: Dict, level: int = 3) -> List[str]:
        sections = []

        for prop_name, prop_schema in properties.items():
            sections.append(f"{'#' * level} {prop_name}\n")

            if 'description' in prop_schema:
                sections.append(f"{prop_schema['description']}\n")

            sections.append(f"**Type:** `{prop_schema.get('type', 'any')}`\n")

            if 'default' in prop_schema:
                sections.append(f"**Default:** `{prop_schema['default']}`\n")

            if prop_name in examples:
                sections.append("**Example:**\n```yaml")
                sections.append(yaml.dump({prop_name: examples[prop_name]}))
                sections.append("```\n")

        return sections

Output Format

  1. Configuration Analysis: Current configuration assessment
  2. Validation Schemas: JSON Schema definitions
  3. Environment Rules: Environment-specific validation
  4. Test Suite: Configuration tests
  5. Migration Scripts: Version migrations
  6. Security Report: Issues and recommendations
  7. Documentation: Auto-generated reference

Focus on preventing configuration errors, ensuring consistency, and maintaining security best practices.

Frequently asked questions

What does the Deployment Validation Config Validate AI skill do?

You are a configuration management expert specializing in validating, testing, and ensuring the correctness of application configurations. Create comprehensive validation schemas, implement configurat

Why use Deployment Validation Config Validate on TypingMind?

Because you install it once and use it with any model. Deployment Validation Config Validate is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deployment Validation Config Validate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huanchain/antigravity-skills/tree/main/skills/deployment-validation-config-validate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Deployment Validation Config Validate?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deployment Validation Config Validate?

As many as you like. As long as a model supports skills, you can use Deployment Validation Config Validate with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deployment Validation Config Validate AI skill free?

Yes. It is published on GitHub by huanchain under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇