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Design and operationalize outcome models that compress noisy event histories into calibrated forecasts and constrained decisions.
Passed 20 of 23 test cases with the skill, and 10 of 23 without it.
and 9 more in the full report
10 fixed10 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before engineering state features or running model training scripts, what explicit parameters must be recorded in Step 1 of the operating procedure to define the decision context?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies recording target outcome, unit of analysis, forecast horizon, and as-of timestamp. | Missed | Passed |
The instructions your model reads when the skill loads.
OverKill Hill P³ · overkillhill.com · github.com/OKHP3
Build a defensible model for systems where many noisy events contribute to a measurable outcome. The core separates the shared world model from the decision objective, so the same evidence can support forecasting, ranking, budgeting, or market comparison without confusing prediction with optimization.
| In scope | Out of scope |
|---|---|
| Repeated event histories, state vectors, and aggregate outcomes | A universal algorithm that fits every dataset |
| Feature reduction and diminishing-returns testing | Claims that a fixed feature count always explains 95% of outcomes |
| Calibrated forecasts and decision-ready uncertainty | Presenting a forecast as certainty or advice without constraints |
| Domain-adapter routing and handoff contracts | Live trading, betting, or political targeting |
Treat a complex system as a noisy, time-indexed process:
events -> entity state -> outcome estimate -> objective and constraints -> decision
Aggregation can reduce the relative influence of idiosyncratic noise, but it does not erase causal structure, dependencies, or meaningful rare events. The goal is to expose persistent signal while preserving uncertainty and time order.
Use the formulas, glossary, synthetic fixture, and deterministic helper supplied
with this package. Read references/computational-model.md for the event-to-state,
logistic, feature-tier, calibration, and decision equations. Read
references/glossary.md before using unfamiliar terms. Run
scripts/calculate-outcome-model.py examples/core-example.json to reproduce the
small arithmetic example. The helper reads local JSON, prints JSON, and performs
no network access or writes.
test cases passed, vs 10 / 23 without
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