BC drought-monitor
Calculate SPI (Standardized Precipitation Index) and SPEI (Standardized description: 'Calculate SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) from NASA POWER API data for drought monitoring. Supports multiple timescales (1-24 months), drought classification, and trend analysis.
Calculate SPI (Standardized Precipitation Index) and SPEI (Standardized description: 'Calculate SPI (Standardized Precipitation Index) and SPEI (Standardized…
As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected scalar at node end at line 2, column 101: …) and SPEI (Standardized description: 'Calculate SPI (Standardized Precipitati… ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 57/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (drought-monitor) differs from the folder (geoskill-drought-monitor)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 31 steps
- 100Execution cost. Instruction body is 3004 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 37 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 337: enough signal without eating the budget
- +4Structure: 42 headings
- +3Step-by-step instructions: 31 items
- +3Output format is stated explicitly
- +4Has examples (21 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.