SKILLEMALL.ai

CF gis-skill

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ClawHub Agent Skills author: 坤图_GIS v1.0.0 MIT-0 80 files body ≈ 6 784 tokens Open the sourceclawhub.ai analyzed 2 d ago

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As a process F 24/100 · Will not run — References files that are not bundled: V5_CONSTITUTION.md

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
72/100
safety, quality, tests
Safety 60%
100
Quality 40%
30
Run on models
none yet
Process rating
F
24/100
Will not run
References files that are not bundled: V5_CONSTITUTION.md
Tools and files w 18
0
Result and completion w 14
0
When it triggers w 12
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Map keys must be unique at line 35, column 1: x-license: "CC-BY-NC-SA-4.0" version: "5.0.2" ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-short description under 40 chars: too little signal for triggering
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 6784 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: V5_CONSTITUTION.md
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "x-author-id"
  • note frontmatter-key unknown frontmatter key "x-skill-fingerprint"
  • note frontmatter-key unknown frontmatter key "x-license"

Process rating: all ten parameters 24/100

Will not run. References files that are not bundled: V5_CONSTITUTION.md
  • 0Tools and files. 1 referenced file(s) missing: V5_CONSTITUTION.md
  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (gis-skill) differs from the folder (gisskillv5)
  • 70Execution cost. Instruction body is 6784 tokens
  • 100Steps. 64 steps
  • low 10 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)
  • +3Description length 1: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -268 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 64 items
  • +4Has examples (15 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 30.

External checks

ClawHub: suspicious
The skill is a broad GIS automation package, but it also enables default self-evolution, feedback retention, external update checks, package rewriting, and some source-data mutation that users should review before installing.
LLM: suspicious (high) · 23 Jun 2026