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AC yeelight-smart-home

Control, organize, diagnose, design, personalize, and answer product knowledge questions for a Yeelight smart home. Use for Yeelight homes, rooms, areas, gateways, devices, groups, scenes, automations, lighting moods, preferences, local memory, recommendations, product manuals, FAQ, SKU lookup, and product pedia consultation. Requires the locally installed yeelight-home CLI runtime and must use only yeelight-home invoke --stdin.

ClawHub Agent Skills author: Yeelight v0.1.14 MIT-0 39 files · 2 scripts body ≈ 3 609 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Control, organize, diagnose, design, personalize, and answer product knowledge questions for a Yeelight smart home.

As a process C 64/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerCommerceCustomer supportData and analyticstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 66 steps
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3609 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 The response is described with custom markup (5 tags): a typed call is more reliable

    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)
    • -31 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 432: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 66 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (26 of 26)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a disclosed Yeelight smart-home control skill with powerful but purpose-aligned actions routed through a local runtime and guarded by validation and confirmations.
    LLM: benign (medium) · VirusTotal: · 21 Jul 2026