adi-decision-engine
Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/dimgouso/adi-decision-engineADI 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:
optionscriteria- optional
constraints - optional
policy_name - optional evidence, confidence, or context
Use scripts/validate_request.py first if request quality is uncertain, then scripts/run_adi.py to execute it.
2. Freeform mode
The user provides a natural-language tradeoff problem.
First use 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
- Determine whether the user input is structured or freeform.
- For freeform input, normalize it into a request skeleton using scripts/normalize_problem.py.
- Validate candidate requests with scripts/validate_request.py.
- Run complete requests with scripts/run_adi.py.
- Present the ADI result in clear decision-support language:
- recommendation first
- strongest tradeoff second
- caveats and sensitivity after that
Decision hygiene rules
- Never rank options without explicit criteria.
- Never silently invent hard constraints.
- If criterion direction is ambiguous, stop and clarify.
- Normalize vague goals into named criteria before scoring.
- Prefer a small, explicit criteria set over many overlapping criteria.
- Keep the policy choice visible:
balanced,risk_averse, orexploratory.
Output quality rules
- Show the top recommendation first.
- Explain why it won.
- Mention the strongest tradeoff.
- Call out eliminated or constraint-violating options.
- Include confidence caveats when evidence is weak.
- Use a compact comparison table or structured bullet list when comparing several options.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-dimgouso-adi-decision-engine": {
"enabled": true,
"auto_update": true
}
}
}