> and rotation across Vault, AWS SSM, 1Password, and Doppler. Use when setting up projects, scanning for leaked secrets, or rotating credentials.
npx skills add https://github.com/borghei/Claude-Skills --skill env-secrets-manager
Complete environment variable and secrets management lifecycle: .env file structure across dev/staging/production, .env.example auto-generation that strips sensitive values, required-variable validation at startup, secret leak detection in git history, credential rotation playbooks, environment drift detection, and integration with HashiCorp Vault, AWS SSM, 1Password CLI, and Doppler.
.env.example (strips secrets), environment-specific files, and fail-fast startup validation.Before running, confirm these inputs. If any is unknown or vague, ASK — do not assume:
.env, scan for leaked secrets, or check env drift (selects env_validator.py vs secret_scanner.py vs env_sync_checker.py).env/.env.example files or directory to scan (the input the tools read)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|------|---------|---------|
| env_validator.py | Validate a .env against .env.example: missing/extra vars, empty secrets, leaked credentials | python scripts/env_validator.py .env.example .env --strict --check-secrets |
| secret_scanner.py | Scan a directory/file for hardcoded secrets via pattern matching | python scripts/secret_scanner.py ./src --severity high --json |
| env_sync_checker.py | Compare env configs across dev/staging/prod and report drift | python scripts/env_sync_checker.py .env.* --baseline .env.example |
Load the reference that matches the task — keep this file lean and pull detail on demand:
.env layout, the .env.* file hierarchy, required .gitignore patterns, and the full Python startup-validation script. Read when scaffolding a project or wiring validation.This skill covers:
.env file scaffolding, hierarchy, and validation for any language/frameworkThis skill does NOT cover:
engineering/ci-cd-pipeline-builder for deployment pipeline secrets)engineering/ci-cd-pipeline-builder)engineering/api-design-reviewer for API security patterns)ra-qm-team/ compliance skills for access control frameworks)| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| engineering/ci-cd-pipeline-builder | Inject secrets from Vault/SSM/Doppler into CI/CD pipeline stages | Rotation playbook outputs feed pipeline secret-update steps |
| engineering/dependency-auditor | Flag dependencies that bundle or require hardcoded credentials | Dependency audit findings trigger secret leak scans on affected repos |
| engineering/skill-security-auditor | Validate that no skill packages ship embedded secrets or credentials | Security audit references this skill's regex patterns for detection |
| engineering/codebase-onboarding | Include .env.example setup and secret-manager access in onboarding checklists | Onboarding workflow consumes the .env hierarchy and validation script |
| engineering/observability-designer | Monitor authentication failures post-rotation; alert on anomalous secret access | Post-rotation verification metrics flow into observability dashboards |
| ra-qm-team/soc2-compliance-auditor | Demonstrate secret management controls for SOC 2 CC6.1 and CC6.6 criteria | Rotation audit logs and access policies serve as SOC 2 evidence artifacts |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take borghei/env-secrets-manager from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.