CF smyx-child-emotion-recognition-analysis
Using fixed cameras (and optional microphones) at home or in kindergartens, AI multimodal analysis recognizes a child's facial expressions (eyebrow/eye shape, mouth-corner curvature), cry-sound features (pitch, frequency, duration), and body-motion amplitude (waving, stomping, curling up) in real time, and jointly identifies the child's typical emotional state: happy, sad, angry, fearful, etc. | 通过家庭或幼儿园内的固定摄像头（及可选麦克风），利用AI多模态分析技术实时分析儿童的面部表情（如眉眼形态、嘴角弧度）、哭声音频特征（音调、频率、持续时间）以及肢体动作幅度（挥手、跺脚、蜷缩等），综合识别出儿童当前的典型情绪状态：快乐、悲伤、愤怒、恐惧等。
Using fixed cameras (and optional microphones) at home or in kindergartens, AI multimodal analysis recognizes a child's facial expressions (eyebrow/eye shape…
As a process F 32/100 · No process to follow — 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 32/100
- 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
- 21Steps. 1 steps, 1 vague phrases
- 30Running it twice. 1 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1571 tokens
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
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 526: 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.