AD smyx-race-foul-detection-analysis
Triggers when a user provides a pet racing track start/finish video URL or file for analysis; uses HD cameras at the starting line and finish line to analyze race video in real time, detecting each pet's (greyhounds, racehorses, etc.) start time, finish order, and lane assignment, automatically determining false starts (start before the signal) or lane crossing (deviating from own lane into an adjacent lane) fouls and outputting judgment results. Assists referee decisions and improves race fairness. Application: pet racing (greyhound, horse, obstacle course), pet sports events, professional track training. Does NOT provide race advice — only returns objective video-based judgment results. | 当用户提供宠物赛道起点/终点视频URL或文件时，触发本技能进行竞赛犯规检测分析；通过架设在赛道起点和终点线的高清摄像头，实时分析比赛视频，检测每只宠物（赛犬、赛马等）的起跑时间、通过终点线的顺序以及所在道次，自动判定是否存在抢跑（起跑时间早于发令信号）或窜道（偏离自身赛道进入邻道）等犯规行为，并输出判定结果。辅助裁判决策，提高赛事公平性。应用场景：宠物竞速比赛（灵缇赛跑、赛马、宠物障碍赛）、宠物运动会、专业赛道训练。仅输出基于视频的客观判定结果，不提供赛事建议。
As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions
How to improve
- 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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 41/100
- 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
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1448 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)
- +3Description length 933: 120–800 characters recommended
- +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
- +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: 78.