integrating-secrets-managers
About
This skill enables Claude to generate secure configurations and setup code for integrating secrets managers like HashiCorp Vault and AWS Secrets Manager. It provides production-ready templates and follows best practices for credential management. Use it when you need to implement or automate a secure secrets management solution in your DevOps workflows.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/integrating-secrets-managersCopy and paste this command in Claude Code to install this skill
Documentation
Overview
This skill empowers Claude to automate the integration of secrets managers into your infrastructure. It generates the necessary configuration files and setup code, ensuring a secure and efficient workflow for managing sensitive credentials.
How It Works
- Identify Requirements: Claude analyzes the user's request to determine the specific secrets manager and desired configurations.
- Generate Configuration: Based on the identified requirements, Claude generates the appropriate configuration files (e.g., Vault policies, AWS IAM roles) and setup code.
- Provide Instructions: Claude provides clear instructions on how to deploy and configure the generated code and integrate it into the existing infrastructure.
When to Use This Skill
This skill activates when you need to:
- Integrate HashiCorp Vault into your infrastructure.
- Set up AWS Secrets Manager for secure credential storage.
- Generate configuration files for managing secrets.
- Implement best practices for secrets management.
Examples
Example 1: Integrating Vault with a Kubernetes Cluster
User request: "Integrate Vault with my Kubernetes cluster for managing database credentials."
The skill will:
- Generate Vault policies for accessing database credentials.
- Create Kubernetes service accounts with appropriate annotations for Vault integration.
- Provide instructions for deploying the Vault agent injector to the Kubernetes cluster.
Example 2: Setting up AWS Secrets Manager for API Keys
User request: "Set up AWS Secrets Manager to securely store API keys for my application."
The skill will:
- Generate an IAM role with permissions to access AWS Secrets Manager.
- Create a Secrets Manager secret containing the API keys.
- Provide code snippets for retrieving the API keys from Secrets Manager within the application.
Best Practices
- Least Privilege: Generate configurations that grant only the necessary permissions for accessing secrets.
- Secure Storage: Ensure that secrets are stored securely within the chosen secrets manager.
- Regular Rotation: Implement a strategy for regularly rotating secrets to minimize the impact of potential breaches.
Integration
This skill can be used in conjunction with other skills for deploying applications, configuring infrastructure, and automating DevOps workflows. It provides a secure foundation for managing sensitive information across your entire infrastructure.
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
langchain
MetaLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
