High Stakes Analytics Decision Lab

High-Stakes Analytics & Decision Lab

Evidence-gated analytics for consequential questions. Move from an ambiguous decision to reproducible evidence—and only then, when justified, to bounded action.

Quick start · Architecture · Live explorer · Portfolio · Documentation · Contributing

Why this exists

High-stakes analysis often fails before the model: the question is underspecified, the data contract is implicit, cleaning choices are hidden, uncertainty is treated as independent, or a recommendation is written because the template expects one.

This repository is a platform-neutral Agent Skill and reproducible research portfolio built around a stricter sequence:

Principle	System behavior
Evidence before method	Declare the question, population, grain, target quantity, horizon, lineage, and claim boundary first
Readiness before analysis	Preserve the source, profile quality and privacy, and pause on material transformations
Adaptive routes	Add descriptive, diagnostic, predictive, or prescriptive work only when justified
Honest endpoints	Accept an evidence request, negative validation, do_not_deploy, or no recommendation
Dependent uncertainty	Retain shared time, market, participant, campaign, operational, and spatial shocks
Traceable communication	Link claims and accessible figures to JSON, CSV, hashes, and rerunnable code

The result is not a fixed report generator. It is an evidence-gated orchestration system that can stop, ask for a named decision, or produce a bounded analytical product without upgrading weak evidence into a stronger claim.

Start in three steps

1. Install the Skill

npx skills add limingrui679-design/high-stakes-analytics-decision-lab -g

The Agent Skills installer discovers the compact package under skills/high-stakes-analytics-decision-lab/: 39 files and about 472 KiB, rather than the full research portfolio. Its machine-readable file and hash contract is in bundle-manifest.json.

Use docs/getting-started.md for Codex-specific, no-install, and direct repository options.

2. Ask for the evidence outcome

$high-stakes-analytics-decision-lab
Run the data-readiness gate on this source, preserve the original file, and
select only the analytical routes the evidence supports. Produce an Evidence
Intelligence Report. Add a Decision Intelligence Brief only if the evidence
and decision context justify one.

Start with the decision or evidence question—not a preferred model. A valid result may be a bounded action, a pilot requirement, targeted diligence, an evidence request, negative validation, or do_not_deploy.

3. Review the evidence package

Every complete project keeps the narrative, machine result, visual evidence, and source lineage together:

report.md                    # primary Evidence Intelligence Report
results.json                 # machine-readable analytical result
chart-map.json               # figure-to-question and source contract
figures/*.svg                # accessible analytical visuals