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Portfolio Optimization Campaign — select balanced combinations from candidate sets optimizing value, diversity, risk, and robustness using Markowitz, Knapsack, Pareto, Real Options, MAP-Elites, and minimax regret methods.
Passed 16 of 20 test cases with the skill, and 11 of 20 without it.
and 5 more in the full report
7 fixed11 passed either way3 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before evaluating 12 candidate marketing campaigns, we need to locate historical portfolio optimization reports and candidate baseline data stored in our vault. Which tool should be called for this query?
| Checked | Without skill | With skill |
|---|---|---|
| The response specifies using mcp__wiki-vault__vault_search. | Missed | Passed |
The instructions your model reads when the skill loads.
Select balanced combinations from candidate sets by optimizing across multiple objectives simultaneously. This campaign applies portfolio theory concepts — originally from finance but broadly applicable — to any selection problem where you must choose a subset from many candidates while balancing competing concerns.
| Signal | Strategy |
|---|---|
| maximize total value / ROI / impact within budget | value-maximization |
| maximize coverage / diversity / avoid redundancy | diversity-maximization |
| balance risk / hedge / diversify failure modes | risk-balancing |
| sequence / phase / timeline / dependencies | temporal-sequencing |
| robust under uncertainty / scenario-proof | robustness-under-uncertainty |
| Strategy | Description |
|---|---|
| value-maximization | Maximize total value within constraints using Knapsack, LP, Cost-benefit, NPV ranking |
| diversity-maximization | Maximize portfolio diversity using MAP-Elites, Niche coverage, Maximum dispersion |
| risk-balancing | Balance risk-return using Markowitz mean-variance, CVaR, Risk parity, Kelly criterion |
| temporal-sequencing | Optimal ordering using Real Options, Critical path, Dependency graph, Staged investment |
| robustness-under-uncertainty | Perform well across futures using Minimax regret, Robust optimization, Scenario planning |
| Tactic | Description |
|---|---|
| pareto-frontier-construction | Build and visualize the Pareto frontier, then select from non-dominated solutions |
| niche-coverage-analysis | Map candidates to niches, score coverage, identify gaps |
| scenario-stress-testing | Evaluate portfolio performance across multiple future scenarios |
| SOP | Description |
|---|---|
| objective-definition | Define optimization objectives and constraints from context |
| optimization-run | Execute multi-objective optimization to produce Pareto front |
test cases passed, vs 11 / 20 without
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