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AC secrets-vault-manager

Use when the user asks to set up secret management infrastructure, integrate HashiCorp Vault, configure cloud secret stores (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), implement secret rotation, or audit secret access patterns.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 7 files body ≈ 3 560 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Use when the user asks to set up secret management infrastructure, integrate HashiCorp Vault, configure cloud secret stores (AWS Secrets Manager, Azure Key…

As a process C 51/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureKubernetesAWSGoogle CloudAzureInfrastructuretype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security references/emergency_procedures.md:49
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - Check if the compromised credential grants access to other secrets (privilege escalation)

    Files scanned: 7. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 51 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3560 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 243: enough signal without eating the budget
    • +4Structure: 34 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (1 of 3)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.