SKILLEMALL.ai

BF smyx-fish-isolation-detection-analysis

Through fixed cameras on aquariums, the system continuously tracks the 2D position of each fish in the school, computes the school centroid (center of mass), and measures the distance from each fish to the centroid (in units of fish body length). | 通过鱼缸固定摄像头,持续跟踪鱼群中每条鱼的二维位置,计算鱼群中心(质心),并检测每条鱼与中心的距离(以鱼体长为单位)。当某条鱼偏离鱼群中心超过 10 倍体长,且持续时间超过 1 小时(或用户设定阈值)时,输出'离群行为'提示。

ClawHub Agent Skills author: smyx-sunjinhui v1.0.8 MIT-0 30 files body ≈ 2 258 tokens Open the sourceclawhub.ai analyzed 2 d ago

Through fixed cameras on aquariums, the system continuously tracks the 2D position of each fish in the school, computes the school centroid (center of mass)…

As a process F 32/100 · Will not run — weak spots: steps, result and completion, when it triggers

AnalyzerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
F
32/100
Will not run
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 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
  • 25Steps. 1 steps
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2258 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
  • -270 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 362: 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.

External checks

ClawHub: suspicious
The skill is a real fish-video analysis wrapper, but it silently creates and reuses identity state, stores tokens locally, and is configured to send media and identity data to private development HTTP endpoints rather than the documented cloud service.
LLM: suspicious (high) · 26 Aug 2026