aws-orchestrator-agent
aws-orchestrator-agent에는 talkops-ai에서 수집한 skills 7개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.
이 저장소의 skills
terraform s3 bucket object storage lifecycle encryption aws_s3_bucket
Commits Terraform module files to GitHub using GitHub MCP server tools. Handles both new file creation and existing file updates with SHA tracking. Supports batch commits for multiple files. Use when committing generated or updated Terraform modules to a GitHub repository after HITL approval. Never uses shell git commands — exclusively uses MCP tools for all GitHub operations.
Generates complete, production-ready Terraform .tf files for any AWS module. Follows loaded service-specific skills for HCL patterns, variable schemas, and output schemas. Use when asked to create a new Terraform module, generate infrastructure code, write HCL for any AWS service, or produce .tf files. Works with any AWS service that has hashicorp/aws Terraform provider support.
Fetches existing Terraform modules from GitHub via MCP tools and applies targeted, surgical edits. Reads module-index.md to locate modules in the repository, uses get_file_contents to fetch current files with SHAs, and applies changes via edit_file without full file rewrites. Use when asked to update, modify, patch, or change an existing Terraform module that is already committed to GitHub. Not for new module creation.
Validates Terraform modules by running terraform init, fmt check, and validate commands in a sandbox environment. Returns VALID or INVALID with structured error details including file names and line numbers. Use after tf-generator or tf-updater writes .tf files to verify correctness before committing to GitHub. Handles path system differences between virtual filesystem and real shell execution.
Generates per-service Agent Skills directories that guide the tf-generator subagent for any AWS service. Creates SKILL.md with YAML frontmatter, step-by-step workflow instructions, and references/ with HCL resource patterns, variable schemas, and output schemas. Use when no skill directory exists for the requested AWS service, or when existing skills have a stale provider-version and need regeneration.
Analyses existing Terraform modules on GitHub to plan targeted modifications. Fetches current module structure, understands resource naming and variable conventions, identifies dependencies, and produces a structured update plan with impact assessment and breaking change detection. Use when asked to analyse, plan, or prepare changes to an existing Terraform module before applying them. Does NOT modify files — only reads and produces a plan for the tf-updater to execute.