adi-decision-engine

# ADI Decision Engine

## Core promise

Turn a messy tradeoff problem into a structured, auditable multi-criteria decision and return a ranked recommendation with confidence and explanation.

## When to use this skill

Use this skill when the user needs structured decision support rather than open-ended brainstorming. Typical triggers include:

- multi-criteria decision analysis
- weighted scoring or option ranking
- vendor selection or procurement
- route planning with explicit tradeoffs
- hiring shortlist ranking
- tool or platform comparison
- policy-driven or auditable agent decisions

## Input modes

This skill supports exactly two input modes.

### 1. Structured mode

The user already has a decision request with:

- `options`
- `criteria`
- optional `constraints`
- optional `policy_name`
- optional evidence, confidence, or context

Use [scripts/validate_request.py](scripts/validate_request.py) first if request quality is uncertain, then [scripts/run_adi.py](scripts/run_adi.py) to execute it.

### 2. Freeform mode

The user provides a natural-language tradeoff problem.

First use [scripts/normalize_problem.py](scripts/normalize_problem.py) to produce a request skeleton. Do not pretend the request is complete if important fields are missing. If the skeleton is not ready, ask for the missing inputs instead of inventing scores or constraints.

## Output contract

If ADI runs successfully, the final answer must contain:

- `best_option`
- a short rationale for why it won
- top-ranked alternatives
- confidence summary
- constraint impact summary
- sensitivity or stability summary when available
- explicit assumptions

If the request is not complete enough to run, return a request-completion prompt rather than a fabricated ranking.

## Workflow

1. Determine whether the user input is structured or freeform.
2. For freeform input, normalize it into a request skeleton using [scripts/normalize_problem.py](scripts/normalize_problem.py).
3. Validate candidate requests wit...