About this skill

Problem

Lottery number selection is often framed by hot numbers, cold numbers, and missing values as if it predicts outcomes. For Da Le Tou, however, any 5+2 combination has the same first-prize probability of 1/21,425,712. This skill is aimed at rational entertainment: it does not promise wins, but explains randomness, long-term negative expectation, and budget control.

How It Works

Data fetching: It prioritizes domestic sources such as huiniao, cwl, and 500, then falls back to local dlt_history.json if all online sources fail, clearly marking results as reference-only.
Report generation: The default entrypoint produces a self-contained HTML report with entertainment combinations, randomness tests, method falsification, an honest ledger, and budget guardrails.
Self-checks: The health check covers data, cross-validation, report completeness, and method discovery gates; a non-zero exit code means the output should not be treated as ready.
Draw verification: After analysis, it expands recommended tickets, calculates prizes ticket by ticket, and compares them against a Monte Carlo random baseline.

Boundaries

It is not a basis for investing, financial planning, or borrowing, and it is not suitable for minors or betting beyond one's ability to pay. The report is meant to clarify randomness, spot paid recommendation scams, and keep entertainment spending under control.

Screenshots

Use Cases

Generate the current Da Le Tou entertainment combination and risk report before each Monday, Wednesday, or Saturday draw.
After the draw, verify whether a specific recommended ticket hit and compare its prize against the random baseline.
Run the pre-delivery health check to confirm data, report sections, and version sync have no blocking failures.
Test whether hot numbers, missing values, and similar claims have predictive power by running out-of-sample method falsification.

Best For

Probability engineers who need to explain lottery negative expectation instead of recommending numbers.
Rational players who want to compare expert recommendations and system recommendations against a random baseline.
Automation engineers maintaining local report pipelines and checking delivery guardrails before handoff.
Users splitting a small entertainment pool with friends and wanting a written pooling agreement.