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Secrets Management

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wshobson
secrets-management

Implement secure secrets management for CI/CD pipelines using Vault, AWS Secrets Manager, or native platform solutions. Use when handling sensitive credentials, rotating secrets, or securing CI/CD environments.

Overview

Publisherwshobson
Repositoryagents
Skill namesecrets-management
Stars
39.8K
Forks
4.2K
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 wshobson on GitHub. Read the source before you install it.

Installation

Install the Secrets Management 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.
  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/wshobson/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/plugins/cicd-automation/skills/secrets-management .claude/skills/secrets-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Secrets Management 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 Secrets Management 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 Secrets Management 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.

Secrets Management

Secure secrets management practices for CI/CD pipelines using Vault, AWS Secrets Manager, and other tools.

Purpose

Implement secure secrets management in CI/CD pipelines without hardcoding sensitive information.

When to Use

  • Store API keys and credentials
  • Manage database passwords
  • Handle TLS certificates
  • Rotate secrets automatically
  • Implement least-privilege access

Secrets Management Tools

HashiCorp Vault

  • Centralized secrets management
  • Dynamic secrets generation
  • Secret rotation
  • Audit logging
  • Fine-grained access control

AWS Secrets Manager

  • AWS-native solution
  • Automatic rotation
  • Integration with RDS
  • CloudFormation support

Azure Key Vault

  • Azure-native solution
  • HSM-backed keys
  • Certificate management
  • RBAC integration

Google Secret Manager

  • GCP-native solution
  • Versioning
  • IAM integration

HashiCorp Vault Integration

Setup Vault

bash
# Start Vault dev server
vault server -dev

# Set environment
export VAULT_ADDR='http://127.0.0.1:8200'
export VAULT_TOKEN='root'

# Enable secrets engine
vault secrets enable -path=secret kv-v2

# Store secret
vault kv put secret/database/config username=admin password=secret

GitHub Actions with Vault

yaml
name: Deploy with Vault Secrets

on: [push]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Import Secrets from Vault
        uses: hashicorp/vault-action@v2
        with:
          url: https://vault.example.com:8200
          token: ${{ secrets.VAULT_TOKEN }}
          secrets: |
            secret/data/database username | DB_USERNAME ;
            secret/data/database password | DB_PASSWORD ;
            secret/data/api key | API_KEY

      - name: Use secrets
        run: |
          echo "Connecting to database as $DB_USERNAME"
          # Use $DB_PASSWORD, $API_KEY

GitLab CI with Vault

yaml
deploy:
  image: vault:1.17
  before_script:
    - export VAULT_ADDR=https://vault.example.com:8200
    - export VAULT_TOKEN=$VAULT_TOKEN
    - apk add curl jq
  script:
    - |
      DB_PASSWORD=$(vault kv get -field=password secret/database/config)
      API_KEY=$(vault kv get -field=key secret/api/credentials)
      echo "Deploying with secrets..."
      # Use $DB_PASSWORD, $API_KEY

Reference: See references/vault-setup.md

AWS Secrets Manager

Store Secret

bash
aws secretsmanager create-secret \
  --name production/database/password \
  --secret-string "super-secret-password"

Retrieve in GitHub Actions

yaml
- name: Configure AWS credentials
  uses: aws-actions/configure-aws-credentials@v4
  with:
    aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
    aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
    aws-region: us-west-2

- name: Get secret from AWS
  run: |
    SECRET=$(aws secretsmanager get-secret-value \
      --secret-id production/database/password \
      --query SecretString \
      --output text)
    echo "::add-mask::$SECRET"
    echo "DB_PASSWORD=$SECRET" >> $GITHUB_ENV

- name: Use secret
  run: |
    # Use $DB_PASSWORD
    ./deploy.sh

Terraform with AWS Secrets Manager

hcl
data "aws_secretsmanager_secret_version" "db_password" {
  secret_id = "production/database/password"
}

resource "aws_db_instance" "main" {
  allocated_storage    = 100
  engine              = "postgres"
  instance_class      = "db.t3.large"
  username            = "admin"
  password            = jsondecode(data.aws_secretsmanager_secret_version.db_password.secret_string)["password"]
}

GitHub Secrets

Organization/Repository Secrets

yaml
- name: Use GitHub secret
  env:
    API_KEY: ${{ secrets.API_KEY }}
    DATABASE_URL: ${{ secrets.DATABASE_URL }}
  run: |
    # Secrets are injected as env vars — never print them to logs
    ./deploy.sh

Environment Secrets

yaml
deploy:
  runs-on: ubuntu-latest
  environment: production
  steps:
    - name: Deploy
      env:
        PROD_API_KEY: ${{ secrets.PROD_API_KEY }}
      run: |
        # Secret injected as env var — never print to logs
        ./deploy.sh

Reference: See references/github-secrets.md

GitLab CI/CD Variables

Project Variables

yaml
deploy:
  script:
    - echo "Deploying with $API_KEY"
    - echo "Database: $DATABASE_URL"

Protected and Masked Variables

  • Protected: Only available in protected branches
  • Masked: Hidden in job logs
  • File type: Stored as file

Best Practices

  1. Never commit secrets to Git
  2. Use different secrets per environment
  3. Rotate secrets regularly
  4. Implement least-privilege access
  5. Enable audit logging
  6. Use secret scanning (GitGuardian, TruffleHog)
  7. Mask secrets in logs
  8. Encrypt secrets at rest
  9. Use short-lived tokens when possible
  10. Document secret requirements

Secret Rotation

Automated Rotation with AWS

python
import boto3
import json

def lambda_handler(event, context):
    client = boto3.client('secretsmanager')

    # Get current secret
    response = client.get_secret_value(SecretId='my-secret')
    current_secret = json.loads(response['SecretString'])

    # Generate new password
    new_password = generate_strong_password()

    # Update database password
    update_database_password(new_password)

    # Update secret
    client.put_secret_value(
        SecretId='my-secret',
        SecretString=json.dumps({
            'username': current_secret['username'],
            'password': new_password
        })
    )

    return {'statusCode': 200}

Manual Rotation Process

  1. Generate new secret
  2. Update secret in secret store
  3. Update applications to use new secret
  4. Verify functionality
  5. Revoke old secret

External Secrets Operator

Kubernetes Integration

yaml
apiVersion: external-secrets.io/v1beta1
kind: SecretStore
metadata:
  name: vault-backend
  namespace: production
spec:
  provider:
    vault:
      server: "https://vault.example.com:8200"
      path: "secret"
      version: "v2"
      auth:
        kubernetes:
          mountPath: "kubernetes"
          role: "production"

---
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
  name: database-credentials
  namespace: production
spec:
  refreshInterval: 1h
  secretStoreRef:
    name: vault-backend
    kind: SecretStore
  target:
    name: database-credentials
    creationPolicy: Owner
  data:
    - secretKey: username
      remoteRef:
        key: database/config
        property: username
    - secretKey: password
      remoteRef:
        key: database/config
        property: password

Secret Scanning

Pre-commit Hook

bash
#!/bin/bash
# .git/hooks/pre-commit

# Check for secrets with TruffleHog
docker run --rm -v "$(pwd):/repo" \
  trufflesecurity/trufflehog:3.88 \
  filesystem --directory=/repo

if [ $? -ne 0 ]; then
  echo "❌ Secret detected! Commit blocked."
  exit 1
fi

CI/CD Secret Scanning

yaml
secret-scan:
  stage: security
  image: trufflesecurity/trufflehog:3.88
  script:
    - trufflehog filesystem .
  allow_failure: false

Related Skills

  • github-actions-templates - For GitHub Actions integration
  • gitlab-ci-patterns - For GitLab CI integration
  • deployment-pipeline-design - For pipeline architecture

Frequently asked questions

What does the Secrets Management AI skill do?

Implement secure secrets management for CI/CD pipelines using Vault, AWS Secrets Manager, or native platform solutions. Use when handling sensitive credentials, rotating secrets, or securing CI/CD environments.

Why use Secrets Management on TypingMind?

Because you install it once and use it with any model. Secrets Management 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 Secrets Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wshobson/agents/tree/main/plugins/cicd-automation/skills/secrets-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Secrets Management?

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 Secrets Management?

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

Is the Secrets Management AI skill free?

Yes. It is published on GitHub by wshobson 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.

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