Decision Criteria Weighting

SkillDev tools

Runs a weighted multi-criteria analysis — making explicit what matters, how much, and how each option performs against it. Triggers: 'weighted decision matrix', 'multi-criteria analysis', 'help me choose between', 'compare these options', 'decision matrix'.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Decision Criteria Weighting skill

What this skill tells your AI

The instructions your AI receives, as published by human-avatar/skills-for-humanity in skills/s4h-decision-criteria-weighting/SKILL.md and read by ahel’s review.

Intuitive decisions fail when too many criteria are in play and their relative importance isn't made explicit. This skill forces that explicitness. The goal is not to replace judgment — it is to make the judgment visible enough to inspect, challenge, and defend.


Your Process

Step 1: State the Decision and List Real Options Name the decision. List the actual options being considered — not aspirational ones. If an option isn't genuinely available, remove it before it contaminates the analysis.

Framing check: Confirm the decision and its intended outcome before continuing. State what you've identified — the specific decision being made and the options on the table — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the decision and the options being compared]. Is that right?"
  • Header: "Framing"
  • Options:
    • Yes — proceed — framing is correct
    • Adjust — one element is off; user will correct it before you continue
    • Reframe — different situation than read; incorporate the correction before proceeding

Step 2: Identify 4-8 Criteria Name the criteria that define a good outcome for this specific decision. Criteria should be independent (not measuring the same thing twice), observable (you can score against them), and genuinely relevant (removing one would change the analysis).

Before narrowing: Show the complete generated set of candidate criteria to the user first. Use AskUserQuestion:

  • Question: "I've identified [N] candidate criteria. Before I narrow to the most decision-relevant ones, are there any you'd flag as especially important, or any I've missed?"
  • Header: "Prioritise"
  • Options:
    • Proceed with your selection — the set looks right
    • Flag one — user will name a specific criterion to include
    • Add a missing one — user will describe it

Step 3: Weight the Criteria Distribute exactly 100 points across the criteria. This forces trade-offs — you cannot weight everything highly. If everything matters equally, the distribution reveals a failure to think through what actually matters most.

Step 4: Score Each Option Score each option on each criterion from 1 to 5. Do this before calculating totals — the sequence matters. Scoring after you see where things are headed is reverse-engineering to confirm a preference, which defeats the exercise.

Step 5: Calculate Weighted Scores Weighted score = sum of (weight × score) for each criterion. Calculate for all options.

Step 6: Sense-Check If the math agrees with your intuition, good. If it disagrees, investigate: is the intuition catching something the criteria missed, or is the intuition rationalising a preference? Either is possible. Don't dismiss either.


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being analyzed and what the core question is — then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full analysis — Complete all steps, reasoning shown throughout
    • Key findings only — Bottom-line output, skip step-by-step detail
    • Weights only — Establish criteria priorities before scoring any options
    • Reframe — The read is off; correct it and the analysis will follow the corrected framing

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.

Output Format

Decision: [Statement]

Criteria and weights:

CriterionWeight (total = 100)

Scored matrix:

Option[Criterion 1] (×W)[Criterion 2] (×W)...Weighted Total

Sense-check:

[Does the result match intuition? If not — what is the intuition picking up that the matrix doesn't capture, or what is the intuition getting wrong?]

Recommendation:

[Option name] — [one sentence rationale]


Notes

The value of this exercise is in the weighting step, not the scoring. Most decision disagreements are disagreements about what matters, not about how options perform. Making weights explicit moves the conversation to the right place.


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Criteria weighted and options ranked. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-decision-premortem-analysis — Stress-test the winning option before committing
    • /s4h-decision-reversibility-analysis — Assess how reversible the top option is
    • /s4h-ethics-check — Check whether the top option is ethically sound
    • Done — Wrap up and synthesise what we have so far

Signals

GitHub stars
223
Forks
22
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
s4h-decision-criteria-weighting
Source
github.com/human-avatar/skills-for-humanity