terraform-expert
Terraform/OpenTofu IaC covering state management, DRY modules, and safety practices. Use when writing or reviewing Terraform/OpenTofu configurations, planning infrastructure changes, or managing state and provider versions.
- License 许可
- MIT
- 所属仓库
- muratovv/ai-hats
- 最近更新
- 2026-11-05
- Stars
- ★ 3
- Forks
- 1
01 前言 Front Matter
- name
- terraform-expert
- license
- MIT
- description
- Terraform/OpenTofu IaC covering state management, DRY modules, and safety practices. Use when writing or reviewing Terraform/OpenTofu configurations, planning infrastructure changes, or managing state and provider versions.
03 SKILL.md 正文
Terraform/OpenTofu Expert
Maintain robust, DRY, and secure infrastructure as code.
When to Use
Provisioning and declaring infrastructure — resources, modules, state, provider versions. Its sibling ansible-ops owns the next layer: configuring and deploying onto the hosts Terraform created. Rule of thumb — if it changes what exists, it's here; if it changes what runs on what exists, it's ansible-ops.
Conventions
- State:
tofu planbeforeapply. NEVER edit state manually. Pin provider versions. - DRY: Use modules for common resources (VPC, VM, SG). Use
localsfor naming logic. - Data-Driven: Fetch existing IDs via
datasources. Validate inputs withvalidationblocks. - Safety:
sensitive = truefor secrets.prevent_destroy = truefor production resources. - Scaling: Prefer
for_eachovercountfor resource sets.
Bundled Rules
IaC Tools
- Documentation First: Check current official docs before generating any IaC config.
- No Hardcoded Versions: Verify OS images, ISOs, container tags exist before referencing.
- Environment Awareness: Consult project environment docs for endpoints, storage, networks.
Anti-Patterns
tofu applywithoutplan— always review the plan first- Manual state editing — use
tofu state mv/rmcommands - Unpinned provider versions — leads to non-reproducible infrastructure
04 同仓库更多 Skills
tool-evaluation-protocol
Time-bounded protocol for evaluating new tools, libraries, or methodologies before adoption. Use when evaluating a candidate dependency for the project, researching a methodology (e.g. ML/LLM frameworks, testing approaches), or following up on a hype-driven recommendation.
systematic-debugging
4-phase bug-fix protocol (evidence, pattern, hypothesis, verify). Use when investigating any error, failure, or unexpected behavior, handling bug reports from users or monitoring, or diagnosing test failures with non-obvious causes.
skill-template
Canonical template and validation guide for ai-hats skills. Use when creating a new skill, reviewing an existing one for structural compliance, or deciding which pattern fits a new behavior.
skill-optimization
Audit and refactor library components to eliminate redundancy and staleness. Use during periodic library maintenance, after adding multiple new skills or rules, when a role's token budget is too high, or after retrospectives identify component issues.
silent-fallback-gate
PreToolUse hook — refuses an Edit or Write that adds an inert broad except. Use when a Python edit was blocked for a silent fallback.
retro-to-framework
Convert an observation about agent behavior — from a retrospective or from a single incident — into a framework-level change (hook, rule, skill, skill update). Use after a retrospective identifies problems that are not project-specific, after an incident where an agent misapplied a component it had read, when CLAUDE.md band-aids accumulate (more than 3 per-project rules that could be generic), or when the same problem recurs across multiple projects.