BC s2-energy-perception
S2-SP-OS Energy Radar. Maps spatial inventory and generates advanced local visual dashboards (Bar/Pie/Trend) for user insights without cloud analytics. / S2 能耗雷达。映射空间设备库，并在本地生成高级数据可视化看板（柱状/饼图/趋势），拒绝云端分析泄露隐私。
As a process C 52/100 · Has gaps — weak spots: steps, result and completion, failures and branches
GeneratorData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 52/100
- 0Steps. Prose only: no discrete steps
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. Tools declared in frontmatter
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 170 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)
- +4Structure: 2 headings, hard to scan
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 207: enough signal without eating the budget
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.
External checks
ClawHub: suspicious
This local energy-dashboard skill does not show exfiltration or destructive code, but it can mislead users with simulated household data and nudges agents toward unsafe power-control automations.
LLM: suspicious (high) · VirusTotal: · 29 May 2026