Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OKHP3/skillz --skill okhp3-outcome-modeling-coregit clone --depth 1 https://github.com/OKHP3/skillzWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/okhp3/skillz/okhp3-outcome-modeling-core)<a href="https://agentmods.dev/skills/okhp3/skillz/okhp3-outcome-modeling-core"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/okhp3-outcome-modeling-core/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/okhp3/skillz/okhp3-outcome-modeling-core"><img src="https://agentmods.dev/badge/skills/okhp3/skillz/okhp3-outcome-modeling-core.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.01982 |
| Opus 5 | $0.00036 | $0.00991 |
| Sonnet 5 | $0.00014 | $0.00396 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
okhp3-outcome-modeling-core scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
okhp3-outcome-modeling-core
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.
Scope
| 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 |
Core mental model
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.
Computational payload
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.
Operating procedure
1. Define the decision before the model
Record:
- target outcome and unit of analysis;
- forecast horizon and as-of timestamp;
- decision owner and action window;
- utility, cost, budget, risk tolerance, and hard constraints;
- whether the task is descriptive, predictive, causal, or allocative.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- benchmarks/benchmark.json 885 B
- benchmarks/equilibrium-review-2026-07-28.json 2.6 KB
- benchmarks/learning-ledger-2026-07-28.json 5.9 KB
- evals/evals.json 5.3 KB
- examples/core-example.json 754 B
- references/computational-model.md 3.2 KB
- references/glossary.md 2.2 KB
- scripts/calculate-outcome-model.py 2.9 KB runs code
- tests/test_calculations.py 774 B runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 189 lines · 71 tokens per session scan A 41a1e9992784
okhp3-outcome-modeling-core is a skill published in the GitHub repository OKHP3/skillz (3 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 1,982 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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