SKILLEMALL.ai
GitHub ↗ RU Sign in

BC xiaomi-air-purifier

Monitor and control Xiaomi Air Purifier 4 Lite via Mi Cloud. Use when asked to check air quality, humidity, purifier status, turn on/off the air purifier, or change fan mode/level. Supports multi-room setups.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 483 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Monitor and control Xiaomi Air Purifier 4 Lite via Mi Cloud.

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype 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
B
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
81
Run on models
none yet
Process rating
C
50/100
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:76
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…IEY+unGH…OEl+Vssh…N5g==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:80
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…UGn+vNpF…rmj+a2cbpG4Jn1g==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:125
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…dWF/U2BXvnSI9h+tuA8…vYn+3NgY…1kA==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:214
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…eBL+/1tqG…AR5+magt…Y4Q+w==}
    • low Secrets in code secret-high-entropy-token pnpm-lock.yaml:225
      High-entropy token-like string (may be an id, hash or a credential)
      resolution: {integrity: sha5…sjN+BHcB…mtt+vJjP…XDw==}

    Files scanned: 6. 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 50/100

    • 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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 483 tokens
    • 100Running it twice. No mutating operations

    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
    • -32 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 208: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.