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AC terraform-ai-skills

Use when bulk-managing Terraform modules at scale — upgrading providers across AWS, GCP, Azure, or DigitalOcean repositories, standardizing GitHub Actions workflows, automating semantic releases, running security scans, or performing end-to-end maintenance cycles across 10–200+ module repositories

modbender/skill-library-mcp Agent Skills author: modbender MIT 21 files · 5 scripts body ≈ 958 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use when bulk-managing Terraform modules at scale — upgrading providers across AWS, GCP, Azure, or DigitalOcean repositories, standardizing GitHub Actions…

As a process C 60/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureTerraformAWSGoogle CloudAzureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
97
Quality 40%
89
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Risky intent intent-offensive-security SECURITY.md:128
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Bug Bounty
    • low Risky intent intent-offensive-security SECURITY.md:130
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      We currently do not offer a bug bounty program, but we deeply appreciate security researchers who responsibly disclose vulnerabilities. We will:

    A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

    Files scanned: 20. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 958 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -33 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 298: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.