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CC aruba-iap

Comprehensive Aruba Instant AP (IAP) configuration management with automatic baseline capture, rollback support, and health monitoring. Supports device discovery, configuration snapshots, SSID management, and safe configuration changes with interactive config mode.

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 80 files · 1 script body ≈ 1 973 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive Aruba Instant AP (IAP) configuration management with automatic baseline capture, rollback support, and health monitoring.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureInfrastructureWriting and documentstype 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
C
74/100
safety, quality, tests
Safety 60%
80
Quality 40%
65
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Secrets in code
If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • high Secrets in code meta-credential-files examples/secrets.json
    Credential / dotenv files bundled with the skill (1)
    examples/secrets.json
Medium and low: 2
  • low Secrets in code secret-password-literal iapctl/tests/test_ap_info.py:16
    Hard-coded password / key literal (may be an example) (test fixture / example file)
    password="sh8beijing",
    fixture
  • low Secrets in code secret-password-literal iapctl/tests/test_wlan_command.py:16
    Hard-coded password / key literal (may be an example) (test fixture / example file)
    password="sh8beijing",
    fixture

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 11 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 75 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1973 tokens
  • 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
  • -227 emoji in the instructions: noise for the model
  • -46 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 266: enough signal without eating the budget
  • +4Structure: 32 headings
  • +3Step-by-step instructions: 75 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.