# High Stakes Analytics Decision Lab

Build or review source-backed descriptive, diagnostic, predictive, and prescriptive...

- Repository: https://github.com/limingrui679-design/high-stakes-analytics-decision-lab (1008 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add limingrui679-design/high-stakes-analytics-decision-lab --skill high-stakes-analytics-decision-lab -g -a claude-code -y`
Codex: `npx skills add limingrui679-design/high-stakes-analytics-decision-lab --skill high-stakes-analytics-decision-lab -g -a codex -y`
Cursor: `npx skills add limingrui679-design/high-stakes-analytics-decision-lab --skill high-stakes-analytics-decision-lab -g -a cursor -y`
Gemini CLI: `npx skills add limingrui679-design/high-stakes-analytics-decision-lab --skill high-stakes-analytics-decision-lab -g -a gemini-cli -y`

## Description

Build or review source-backed descriptive, diagnostic, predictive, and prescriptive analysis for consequential decisions. Use when an agent must profile and safely prepare uploaded data, turn a real dataset or research question into a reproducible study, investigate drivers without overstating causality, validate a model, compare feasible actions under dependent uncertainty and tail risk, trace every parameter to evidence and approval, or produce an answer-first analytical report across health, business, finance, policy, engineering, operations, behavioral science, AI, or planning.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# High-Stakes Analytics & Decision Lab

Turn a real question into a defensible path from source evidence to analysis and,
only when justified, bounded action. Keep observation, diagnosis, prediction,
causal evidence, value judgments, and recommendation visibly separate.

## Start here

Run the environment audit before an executable workflow:

```bash
python3 scripts/hsadl.py doctor
```

For a safe end-to-end setup example:

```bash
python3 scripts/hsadl.py demo --output-dir /absolute/path/to/demo
```

The demo is a synthetic engineering fixture. Never cite its values as empirical
evidence.

## Route before choosing a method

| Route | Primary question | Valid endpoint |
|---|---|---|
| Descriptive | What is happening? | Baseline report or evidence request |
| Diagnostic | Why might it be happening? | Explanations to test with a visible causal boundary |
| Predictive | What is likely next? | Validated prediction, negative validation, or `do_not_deploy` |
| Prescriptive | What should be done, if justified? | Bounded action, pilot, diligence, evidence request, or no recommendation |

When only a question is available, generate a blueprint instead of inventing
results:

```bash
python3 scripts/hsadl.py route "How should limited review capacity be allocated?" \
  --scope full --output-dir /absolute/path/to/blueprint
```

Read `references/analytics-triad.md` and `references/method-routing.md` when a
request is ambiguous or spans several routes.

## Evidence-gated workflow

The rest: https://github.com/limingrui679-design/high-stakes-analytics-decision-lab/blob/HEAD/skills/high-stakes-analytics-decision-lab/SKILL.md