SKILLEMALL.ai

AB tailings-dam-risk

Screen tailings dam bodies, reservoir areas, catchments, and downstream exposure using remote sensing change detection. Produce patrol priorities based on hazard, exposure, and evidence.

ClawHub Agent Skills author: ruiduobao v2.0.0 MIT-0 6 files body ≈ 1 801 tokens Open the sourceclawhub.ai analyzed 2 d ago

Screen tailings dam bodies, reservoir areas, catchments, and downstream exposure using remote sensing change detection.

As a process B 66/100 · Nearly there — weak spots: failures and branches, consistency

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
B
66/100
Nearly there
Failures and branches w 10
0
Consistency w 8
40
Tools and files w 18
60
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: 6. 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 66/100

  • 0Failures and branches. Linear process with no failure handling
  • 40Consistency. Frontmatter name (tailings-dam-risk) differs from the folder (geoskill-tailings-dam-risk)
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 27 steps
  • 100Execution cost. Instruction body is 1801 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 14 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 186: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 27 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
The skill mostly matches its stated purpose, but it needs Review because it can produce tailings-dam risk reports using synthetic fallback data when real inputs or downloads are missing.
LLM: suspicious (high) · VirusTotal: · 31 Jul 2026