Secure secrets management for CI/CD pipelines using Vault, AWS Secrets Manager, and platform-native solutions.
Works with
Supports multiple backends: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, Google Secret Manager, and GitHub/GitLab native secrets
Includes integration examples for GitHub Actions, GitLab CI, Terraform, and Kubernetes with automatic secret rotation capabilities
Covers best practices including secret masking in logs, least-privilege access, audit logging, and secret s
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsecrets-managementExecute the skills CLI command in your project's root directory to begin installation:
Fetches secrets-management from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate secrets-management. Access via /secrets-management in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Secure secrets management practices for CI/CD pipelines using Vault, AWS Secrets Manager, and other tools.
Implement secure secrets management in CI/CD pipelines without hardcoding sensitive information.
# 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
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, $API_KEY
deploy:
image: vault:latest
before_script:
- export VAULT_ADDR=https://vault.example.com:8200
- export VAULT_TOKEN=$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 secretsmanager create-secret \
--name production/database/password \
--secret-string "super-secret-password"
- 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
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"]
}
- name: Use GitHub secret
run: |
echo "API Key: ${{ secrets.API_KEY }}"
echo "Database URL: ${{ secrets.DATABASE_URL }}"
deploy:
runs-on: ubuntu-latest
environment: production
steps:
- name: Deploy
run: |
echo "Deploying with ${{ secrets.PROD_API_KEY }}"
Reference: See references/github-secrets.md
deploy:
script:
- echo "Deploying with $API_KEY"
- echo "Database: $DATABASE_URL"
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}
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
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
secrets-management fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
secrets-management has been reliable in day-to-day use. Documentation quality is above average for community skills.
secrets-management reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for secrets-management matched our evaluation — installs cleanly and behaves as described in the markdown.
secrets-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in secrets-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend secrets-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for secrets-management matched our evaluation — installs cleanly and behaves as described in the markdown.
secrets-management reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: secrets-management is the kind of skill you can hand to a new teammate without a long onboarding doc.
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