Design and operationalize outcome models that compress noisy event histories into calibrated forecasts and constrained decisions. Use when a user asks about feature reduction, probability, expected value, aggregation, or moving a prediction method between sports, business, sales, advertising, finance, or prediction markets. Load a domain adapter when one fits.
A 17 of 17 checks passed. Deterministic, no model, same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (9376 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
okhp3/skillz · 3 stars · license MIT · pushed 2026-09-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_nq3dcnimw2q6yxzp
GET https://markdownregistry.com/api/v1/resolve?ref=okhp3/skillz/okhp3-outcome-modeling-core
GET https://markdownregistry.com/api/v1/blob/41a1e9992784257bab60151720aae7e68ca50ebc4c8179a3cecb6e3d995ee448