BD smyx-workplace-stress-heatmap-analysis
Using fixed cameras in enterprise office areas (open-plan workstations), the system anonymously analyzes multiple employees' facial expressions (e.g., frowning, downturned mouth corners) and postures (e.g., rigid sitting, forward leaning, frequent eye rubbing), and applies a group-level stress assessment model to compute a real-time stress index for each workstation zone, generating a stress distribution heatmap of the entire. | 通过企业办公区（开放式工位）的固定摄像头，匿名分析多名员工的面部表情（如皱眉、嘴角下垂）和姿态（如僵坐、前倾、频繁揉眼），使用群体压力评估模型计算每个工位区域的实时压力指数，生成整个办公区的压力分布热力图（颜色从绿到红，绿色代表低压力，红色代表高压力）。系统定期（如每小时）生成压力热力图，用于组织健康度监测。
Using fixed cameras in enterprise office areas (open-plan workstations), the system anonymously analyzes multiple employees' facial expressions (e.g.…
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 30. 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")
Process rating: all ten parameters 35/100
- 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
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1704 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -256 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 588: enough signal without eating the budget
- +4Structure: 19 headings
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.