agentleFS
saitarrunsaitarrun/Devforge-ai/skills/configuration-management/SKILL.md

configuration-management

Skill4 starsChanged 5 months agoApache-2.0View on GitHub

Secrets management, environment-specific configs, safe defaults, feature flags, configuration-as-code, compliance and audit trails. Use when managing secrets, configuring deployments, or implementing feature flags.

  • Reads credentials

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

Skill: Configuration Management

Secrets stay secret. Configs are code. Feature flags enable safe rollout.

Secrets Management

Never commit secrets:

# ✗ Don't
export DATABASE_PASSWORD="my-password"
git add .env
git commit

# ✓ Do
export DATABASE_PASSWORD=$(aws secretsmanager get-secret-value ...)
# or
source <(vault kv get -format=json secret/db | jq -r '.data | to_entries | .[] | "\(.key)=\(.value)"')

Tools: Vault, AWS Secrets Manager, Azure Key Vault

Environment Configs

# config/environments.yaml

development:
  database:
    host: localhost:5432
    pool_size: 5
  cache:
    ttl: 60s

production:
  database:
    host: db.prod.example.com:5432
    pool_size: 100
  cache:
    ttl: 3600s

Load by environment:

import yaml
config = yaml.load(open(f'config/{ENV}.yaml'))

Feature Flags

Safe rollout:

feature_auth_v2: enabled for [10%, 25%, 50%, 100%]

if feature_flag("auth_v2"):
    use_new_auth()
else:
    use_old_auth()

Tools: LaunchDarkly, Unleash, custom

Safe Defaults

# ✓ Good defaults
class Config:
    DEBUG = False  # Never debug in production
    TIMEOUT = 30   # Reasonable default
    RETRIES = 3    # Sensible retry count

# ✗ Bad defaults
class Config:
    DEBUG = True   # Oops, left it on
    TIMEOUT = 3600  # Way too long

Status: Ready for configuration work
Best for: Secrets, configs, feature flags, environment management

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.