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S4h Decision Criteria Weighting

Organization
human-avatar
s4h-decision-criteria-weighting

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'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-decision-criteria-weighting
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Decision Criteria Weighting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-decision-criteria-weighting
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-decision-criteria-weighting .claude/skills/s4h-decision-criteria-weighting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Decision Criteria Weighting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads S4h Decision Criteria Weighting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so S4h Decision Criteria Weighting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Decision Criteria Weighting

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

Frequently asked questions

What does the S4h Decision Criteria Weighting AI skill do?

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'.

Why use S4h Decision Criteria Weighting on TypingMind?

Because you install it once and use it with any model. S4h Decision Criteria Weighting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install S4h Decision Criteria Weighting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-decision-criteria-weighting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Decision Criteria Weighting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with S4h Decision Criteria Weighting?

As many as you like. As long as a model supports skills, you can use S4h Decision Criteria Weighting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Decision Criteria Weighting AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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