SKILLEMALL.ai

CD sport-analysis

Conducts video safety risk analysis for participants in outdoor sports competitions, long-distance running, marathons, etc.; identifies sports injuries and sudden health risks, outputs professional analysis reports, and provides timely warnings to ensure sports safety. | 户外体育赛事风险分析工具,针对户外体育比赛、长跑马拉松等运动项目的参赛人员进行视频安全风险分析,识别运动损伤和突发健康风险,输出专业分析报告,及时预警保障运动安全

ClawHub Agent Skills author: smyx-sunjinhui v1.0.15 MIT-0 30 files · 18 scripts body ≈ 1 659 tokens Open the sourceclawhub.ai↗ analyzed 8 d ago

Conducts video safety risk analysis for participants in outdoor sports competitions, long-distance running, marathons, etc.; identifies sports injuries and…

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerMedia and videoData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (sport-analysis) differs from the folder (smyx-sport-analysis)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 15 steps
  • 100Execution cost. Instruction body is 1659 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)
  • +3Output format is not stated: the model decides each time
  • -254 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 353: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 15 items
  • +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: 72.

External checks

ClawHub: suspicious
This sports video analysis skill is not clearly malicious, but it silently creates and reuses cloud identity state while sending sensitive media and report queries to remote services, which needs Review before install.
LLM: suspicious (high) · 29 Sept 2026