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

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

A justified decision layer adds decision-report.md, decision-results.json, and its own figure contract. It never replaces the primary evidence product.

Direct repository entry points

Starting point Command or guide Outcome
Question only python3 scripts/route_question.py "<question>" --scope full --output-dir <path> Evidence and method blueprint; no invented result
Question plus data python3 scripts/init_case.py <data.csv> --question "<question>" --output-dir <path> Preserved source, draft contract, readiness profile, and unresolved decisions
Existing decision case python3 scripts/validate_case.py <case.json> then python3 scripts/run_case.py <case.json> --output-dir <path> Validated expected, tail, sensitivity, provenance, and group-impact outputs
Worked precedents Fifteen-project portfolio Complete source-to-report evidence paths

How it works

The fixed evidence spine remains stable while the case-specific analytical layer changes.

Fixed evidence spine Adaptive case layer
Question, population, unit, target quantity, and horizon Route, fields, methods, and validation
Source lineage, quality status, and reproducibility Figures, report sections, and decision criteria
Uncertainty, limitations, and claim boundary Bounded action, evidence request, or stopping status

The data gate can stop the workflow

Uploaded row-level data do not go directly into a model. The system preserves the original, establishes a contract, checks grain and keys, profiles quality and privacy, and produces a dry-run remediation plan.

Gate status Meaning Permitted next step
ready No material failure under the declared contract Continue
ready_with_documented_limitations Localized issues remain Continue with visible limits
needs_user_confirmation A substantive transformation, privacy, or intended-use choice remains Pause for a named approval or clarification
blocked Grain, key, schema, leakage, or another critical failure invalidates the route Stop and request corrected evidence

Only safe normalization can run without approval. Deletion, imputation, outlier treatment, category merging, unit conversion, target correction, and grain changes require explicit action IDs. The processed copy never overwrites the source.

Four routes, no mandatory recommendation

Route Question Required discipline Valid endpoint
Descriptive What is happening? Denominators, coverage, trends, segments, and missingness Baseline report or evidence request
Diagnostic Why might it be happening? Contributions, competing explanations, hypotheses, and a visible causal boundary Prioritized explanations to test
Predictive What is likely next? Target, horizon, baseline, held-out validation, calibration, subgroup error, and drift Validated prediction, negative validation, or do_not_deploy
Prescriptive What should be done, if justified? Owner, alternatives, constraints, dependence, tail risk, sensitivity, and reversal conditions Bounded action or no decision-ready recommendation

Routes may compose, but a later route cannot erase the quality and evidence requirements of an earlier stage. Read the full system design in docs/architecture.md.

Two products, one evidence contract

The Evidence Intelligence Report is primary and the Decision Intelligence Brief is conditional

Product Main question Contents Existence rule
Evidence Intelligence Report What does the evidence establish? Source and QA contract, methods, validation, figures, uncertainty, limitations, lineage, and reproducibility Primary record for every complete project
Decision Intelligence Brief What action, pilot, diligence, evidence request, or stop follows? Decision status, alternatives, constraints, shared shocks, tail risk, sensitivity, and reversal conditions Conditional; only when a separate decision layer is justified

Every material figure is generated from the shared editorial evidence system, includes a title and description, and is paired with its analytical question, supported interpretation, and claim boundary. The visual system is part of the evidence contract, not decorative reporting.

Fifteen complete evidence paths

The public portfolio contains 15 primary reports and 10 conditional briefs—25 intelligence products in total—plus 119 canonical accessible figures: 50 evidence figures and 69 decision figures.

The cases span operational demand, distribution shift, scarce-capacity pilots, temporal model transport, tail-risk decisions, human-in-the-loop triage, commercial diligence, mitigation allocation, filing review, clustered field experiments, health evidence, Opportunity Zones, spatial access, clinical prediction, and campaign monitoring.

They intentionally end differently: some support a bounded decision, some require a pilot or targeted review, and some stop at an evidence request or do_not_deploy.

Explore Open
Visual case gallery Portfolio overview
Machine-readable catalog cases.json
Rebuild and comparison contract Verification guide
Method and domain routing method-domain-map.json

Verification you can reproduce

The current stable release is v1.0.5. Its versioned source package and SHA-256 checksum are published together; the release identity is also recorded in CITATION.cff, CHANGELOG.md, and RELEASE-MANIFEST.json.

make verify

This runs the standalone regression suite and rebuilds all fifteen projects in an isolated verified-file copy. Use make quality for Ruff, mypy, and codespell; use docs/verification.md for direct commands, static security gates, and no-Git release verification.

The 94 public tests cover data readiness, adaptive routing, numerical behavior, source and artifact identity, package integrity, local links, accessible SVGs, generator idempotence, no-Git releases, DNS and SSRF boundaries, and source parser security. The complete 94-test standalone regression suite and the fifteen-project rebuild are separate gates: successful reproduction establishes the reviewed workflow and declared numerical tolerance, not empirical validity, external adoption, or real-world impact.

CI exercises Python 3.11, 3.12, 3.13, and 3.14. Security checks include Bandit, dependency auditing, hardened source-builder tests, targeted branch coverage, and CodeQL.

Repository design

high-stakes-analytics-decision-lab/
├── SKILL.md                     # Agent Skill runtime contract
├── docs/                        # onboarding, architecture, layout, verification
├── references/                  # enforceable method and evidence contracts
├── assets/                      # templates and canonical README visuals
├── scripts/                     # profiling, routing, analysis, generation, checks
├── examples/real-data-cases/    # fifteen reproducible evidence projects
├── tests/                       # standalone contract and regression suite
├── agents/                      # supported agent-facing metadata
└── .github/                     # CI, security, issue, and PR workflows

SKILL.md stays at the root because Agent Skills-compatible runtimes discover it there. Human guides live in docs/; precise runtime rules live in references/; generated evidence stays beside the project that produced it. See docs/repository-layout.md before moving or regenerating files.

Documentation

Guide Use it for
Documentation home Choose a user, reviewer, or maintainer path
Getting started Install, route a question, profile data, and run a case
Architecture Understand gates, routes, products, the decision engine, and visual evidence
Repository layout Distinguish runtime contracts, human guides, canonical sources, and generated files
Verification Reproduce tests, portfolio outputs, quality checks, and release gates
Contributing Change code, documentation, sources, or cases without breaking evidence boundaries
Security Report a vulnerability privately

Responsible-use boundary

This is a public, tested research and portfolio prototype. It does not establish production readiness, institutional adoption, medical advice, investment advice, a regulatory finding, an assurance opinion, or achieved real-world impact. A reproducible result can still be decision-inappropriate; domain review remains mandatory before operational use.

Citation and license

Cite the version reviewed using CITATION.cff. Public release history is in CHANGELOG.md, and component-version boundaries are in VERSIONING.md.

Licensed under the MIT License.