SKILLEMALL.ai

AC geospatial-data-quality-audit

Unified geospatial data quality audit for GIS data packages. Checks raster, vector, table, NetCDF, and directory structure. Outputs JSON/HTML reports, issue layers, checksums, and machine-readable exit codes. Use when the user wants to validate data packages, check delivery quality, find CRS/nodata/ geometry issues, or generate QA reports.

ClawHub Agent Skills author: ruiduobao v2.0.0 MIT-0 6 files body ≈ 1 021 tokens Open the sourceclawhub.ai analyzed 2 d ago

Unified geospatial data quality audit for GIS data packages.

As a process C 64/100 · Has gaps — weak spots: failures and branches, consistency, progress reporting

AnalyzerData and analyticsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
C
64/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 64/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (geospatial-data-quality-audit) differs from the folder (geoskill-geospatial-data-quality-audit)
    • 60Tools and files. Uses tools (python) 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. 26 steps
    • 100Execution cost. Instruction body is 1021 tokens
    • 100Running it twice. No mutating operations
    • 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 341: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 26 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.

    External checks

    ClawHub: suspicious
    This is mostly a local geospatial audit tool, but it has review-worthy packaging and report-safety issues before use.
    LLM: suspicious (medium) · 31 Jul 2026