SKILLEMALL.ai

AC geoskill-spatial-index-builder

构建 R-tree / Quadtree / GeoHash 空间索引并统计查询性能,结果与暴力搜索对齐。Build R-tree / Quadtree / GeoHash spatial indexes, benchmark query performance and align results with brute-force search.

ClawHub Agent Skills author: ruiduobao v1.0.0 MIT-0 27 files body ≈ 1 268 tokens Open the sourceclawhub.ai analyzed 2 d ago

构建 R-tree / Quadtree / GeoHash 空间索引并统计查询性能,结果与暴力搜索对齐。Build R-tree / Quadtree / GeoHash spatial indexes, benchmark query performance and align results with…

As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorData 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
55/100
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 55/100

  • 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
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1268 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 174: 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 main tool is a local spatial-index benchmark, but the package also includes undisclosed network, download, and credential-handling code that does not fit that purpose.
LLM: suspicious (high) · 4 Aug 2026