SKILLEMALL.ai

AC geoskill-precipitation-nowcasting

基于光流法(交叉相关位移估计)的拉格朗日持久性降水临近预报,外推未来 0-6 小时降水场,输出预报序列 GeoTIFF 与位移场 JSON。Optical-flow (cross-correlation) Lagrangian persistence nowcasting that extrapolates precipitation fields 0-6 hours ahead, outputting a forecast GeoTIFF stack and a displacement-field JSON.

ClawHub Agent Skills author: ruiduobao v1.0.0 MIT-0 27 files body ≈ 1 434 tokens Open the sourceclawhub.ai analyzed 4 d ago

基于光流法(交叉相关位移估计)的拉格朗日持久性降水临近预报,外推未来 0-6 小时降水场,输出预报序列 GeoTIFF 与位移场 JSON。Optical-flow (cross-correlation) Lagrangian persistence nowcasting that extrapolates…

As a process C 62/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
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: 22. 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 62/100

  • 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
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1434 tokens
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill

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)
  • +2Single-language instructions
  • +3Description length 259: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 14 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill's main nowcasting command appears local, but the package also ships undisclosed network, credential, and provenance-risk code that does not fit the offline precipitation-nowcasting purpose.
LLM: suspicious (high) · 4 Aug 2026