S4h Constraint Scope Reduction logo

S4h Constraint Scope Reduction

Organization
human-avatar
s4h-constraint-scope-reduction

Finds the minimum that satisfies the actual requirement — stripping everything wanted but not needed. Triggers: 'minimum viable', 'what's the minimum', 'strip this back', 'what do we actually need', 'scope reduction', 'simplify the requirement'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-constraint-scope-reduction
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 Constraint Scope Reduction 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-constraint-scope-reduction
  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-constraint-scope-reduction .claude/skills/s4h-constraint-scope-reduction
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Constraint Scope Reduction 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 Constraint Scope Reduction 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 Constraint Scope Reduction 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.

Constraint Scope Reduction

Scope grows because wants accumulate alongside needs, and no one separates them. This skill forces that separation. The goal is not to build less — it is to find the version where every element is doing real work, and nothing is there because it sounded good in the planning meeting.


Your Process

Step 1: State the Full Current Scope List everything in the current plan, feature set, or requirement. Be complete — you can only strip what you've named.

Framing check: Confirm the constraint and the goal it is blocking before continuing. State what you've identified — the specific scope being reduced and the core job it must accomplish — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the scope and the core job it must accomplish]. 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: Find the Core Job Ask: what is the single verb this must do? Not what it should do, what it must do. Strip all nouns and adjectives until only the job remains. "A user can complete a purchase" is a job. "A seamless, intuitive checkout experience" is not.

Step 3: Classify Each Element — Must or Want For every element in scope: is it required for the core job (must) or desired beyond it (want)? Apply pressure to musts — if you removed it, would the core job fail?

Step 4: Remove All Wants and Test Drop every want. Confirm: can the core job still be done? If yes, the wants were not load- bearing. If no, you've found a hidden must — reclassify and investigate why it felt optional.

Step 5: Simplify the Musts For each must: is there a simpler way to achieve the same outcome? Simpler means fewer moving parts, less code, lower cost, or faster delivery — not necessarily less effort to design.

Step 6: State the Minimum Viable Version Write the minimum scope in a single paragraph. It should be possible to build from this description without the original scope document.


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
    • Minimum viable scope only — The smallest version that still satisfies the actual need
    • 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

Current scope:

[Bulleted list of everything in scope]

Core job (the verb):

[Single sentence]

Must vs Want classification:

ElementMust / WantReason

What was removed and why it's safe to remove:

[Brief list — each removal with one-line justification]

Minimum viable scope:

[Single paragraph — buildable without further clarification]


Notes

The most common mistake is reclassifying wants as musts when pressure is applied. Challenge every must: "if we launched without this, what actually breaks?" The answer is usually "nothing breaks, someone would be annoyed."


What's Next

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

  • Question: "Scope reduced. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-decision-criteria-weighting — Evaluate options within the reduced scope
    • /s4h-resource-allocation-analysis — Reallocate for the new scope
    • /s4h-decision-premortem-analysis — Stress-test the reduced scope plan
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Constraint Scope Reduction AI skill do?

Finds the minimum that satisfies the actual requirement — stripping everything wanted but not needed. Triggers: 'minimum viable', 'what's the minimum', 'strip this back', 'what do we actually need', 'scope reduction', 'simplify the requirement'.

Why use S4h Constraint Scope Reduction on TypingMind?

Because you install it once and use it with any model. S4h Constraint Scope Reduction 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 Constraint Scope Reduction in TypingMind?

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

Which AI models can use S4h Constraint Scope Reduction?

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 Constraint Scope Reduction?

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

Is the S4h Constraint Scope Reduction 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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