CC smart-home-planner
Use when users want help with smart home planning, device selection, automation design, platform comparison, or configuration for Home Assistant, 米家/小米, or Apple HomeKit. Triggers on smart home, 智能家居, home automation, device recommendation, 自动化场景, and home assistant setup.
Use when users want help with smart home planning, device selection, automation design, platform comparison, or configuration for Home Assistant, 米家/小米, or…
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
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.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- 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.
- 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 · 2
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high Dangerous commands
cmd-persistenceknowledge/automation-guide.md:159Persistence mechanism (cron / launchd / scheduled task / autorun registry)<!-- ~/Library/LaunchAgents/com.mijia.automation.plist -->
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high Dangerous commands
cmd-persistenceknowledge/automation-guide.md:182Persistence mechanism (cron / launchd / scheduled task / autorun registry)launchctl load ~/Library/LaunchAgents/com.mijia.automation.plist
Files scanned: 25. 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
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 100 steps, 2 vague phrases
- 100Failures and branches. 10 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2975 tokens
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 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
- -32 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 273: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 100 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.