Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing.
SkillScore75Maintained
Automatic
◆Data Analyst pack
Last verified 8/22/2026
What it changes
Passed 17 of 23 test cases with the skill, and 14 of 23 without it.
Without skill63%
With skill75%
Checks it passed that the model alone missed
Performs sensitivity testing by perturbing criteria weights.
Applies vector normalization when TOPSIS aggregation is specified.
Applies linear normalization when Weighted Sum Model aggregation is specified.
and 2 more in the full report
4 fixed14 passed either way5 still failing1 broke
Pass-rate lift
+13 pp
Tokens
+1%
Agent turns
0%
Response time
-19%
Model gemini-3.6-flashMeasured 8/22/2026v118 of 24 cases passed with skill · 23 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 4
Prompt
We are assessing three IT service management vendors using TOPSIS aggregation. Our analyst suggested applying simple min-max linear normalization to make criteria comparable before vector distance calculations. How should the normalization step be structured?
What was checked, without and with the skill
Checked
Without skill
With skill
Applies vector normalization when TOPSIS aggregation is specified.
Missed
Passed
case-04 · gemini-3.6-flash · 8/22/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md47 linessingle file
Scoring Matrix Construction
Standard MCDA workflow of define criteria → assign weights → score → aggregate → sensitivity check, producing a complete scoring matrix and ranking results.
Stages
Criterion Definition — Extract evaluation criteria from research goals and candidates
Weight Elicitation — Compute criteria weights using specified method
Alternative Scoring — Score candidates against each criterion
Normalization — Normalize scores to make different scales comparable
Sensitivity Testing — Perturb weights to verify result robustness