S4h Design Constraints logo

S4h Design Constraints

Organization
human-avatar
s4h-design-constraints

Uses constraints productively — maps hard limits, soft preferences, and how the tightest constraints define the solution space. Triggers: 'what are the design constraints', 'map the constraints', 'what are we working within', 'what's fixed', 'constraint-driven design', 'what does the solution space look like', 'working within limits'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-design-constraints
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 Design Constraints 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-design-constraints
  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-design-constraints .claude/skills/s4h-design-constraints
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Design Constraints 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 Design Constraints 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 Design Constraints 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.

Design: Design Constraints

Every design problem has a solution space — the set of forms that could satisfy the need. Constraints are not what prevent good design; they are what define the problem precisely enough to be solved. A problem with no constraints has no solution because it has no shape. The designer's task is to understand the constraint set completely: which limits are genuinely fixed, which are preferences that could move, and what territory they collectively leave open.

Christopher Alexander's work on pattern languages makes this explicit: every good design form is a response to a specific set of forces in tension. Understanding those forces — what they require, where they conflict, where they align — is how a designer finds the form that resolves them. Jony Ive's approach at Apple was similar: start with the hardest constraints (manufacturing tolerances, material properties, thermal dissipation) and let design decisions cascade from there. The hardest constraint is often the most generative.

This skill maps the full constraint set, classifies each constraint by hardness, surfaces the interactions between them, and identifies where the productive design space lies within the boundaries they collectively draw.


Your Process

Step 1: Elicit the Full Constraint Set List every known constraint. Do not classify or judge yet — just enumerate. Include:

  • Technical constraints (what the medium or material allows)
  • Physical constraints (size, weight, space, material properties)
  • Economic constraints (cost, time, resources)
  • Regulatory or legal constraints (requirements that must be met)
  • Organisational constraints (what the team, system, or process can support)
  • User constraints (what users can understand, do, or accept)
  • Aesthetic or brand constraints (what must be consistent with existing forms)

Framing check: Confirm the design context and the constraint domain before continuing. State what you've identified — the thing being designed and the primary constraint types in play — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the design context and constraints]. 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: Classify by Hardness For each constraint, classify:

  • Hard: genuinely fixed — physical law, signed contract, technical impossibility, binding regulation
  • Soft: real but negotiable — someone's preference, a policy that could be changed, a convention rather than a rule
  • Assumed: stated as a constraint but never tested — habit, fear, or unknown origin

For each soft or assumed constraint, note what it would take to move it.

Step 3: Map Constraint Interactions Some constraints compound: two constraints together rule out more territory than either does alone. Some constraints conflict: they cannot be satisfied simultaneously at full strength, requiring a trade-off. Some constraints are redundant: satisfying one automatically satisfies another.

Identify:

  • Compounding pairs: constraints that together eliminate a class of solution
  • Conflicting pairs: constraints that create a trade-off requiring a design decision
  • Redundant constraints: where satisfying one satisfies another for free

Step 4: Find the Tightest Constraint The tightest genuine constraint is the one with the smallest solution set. It determines where to start design. It is usually the most generative constraint — because it narrows the field to what's actually possible and often points toward what's uniquely possible within those limits.

Identify the tightest constraint or constraint pair. Ask: what does design look like if this constraint is accepted fully and treated as a design requirement?

Step 5: Map the Solution Space Given the hard constraint set, describe the territory that remains open. What categories of solution are viable? What are the dimensions of variation within that territory? Where are the most interesting sub-regions — areas with multiple viable directions, or areas where constraints interact in generative ways?


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences — what is being designed and which constraints seem most significant — 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
    • Solution space focus — Skip individual constraint analysis; describe the territory directly
    • 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

Constraint inventory:

ConstraintDomainClassificationNotes
[Constraint][Domain]Hard / Soft / Assumed[What it would take to move, if soft]

Constraint interactions:

PairTypeEffect
[A] + [B]Compounding / Conflicting / Redundant[What this means for design]

Tightest constraint:

[State it. What category of design decisions it forces.]

Solution space: [3–5 sentences describing the design territory that remains viable. What categories of solution are open, what dimensions of variation exist, and where the most interesting territory is.]

Design direction implied:

[One to two sentences. Given this constraint set, what kind of solution is it pointing toward?]


Notes

This skill is a close neighbour of /s4h-constraint-hardness-testing, which focuses on testing whether individual constraints are actually real. Design constraints goes further: it treats the full constraint set as a system and asks what solution space it collectively defines. Use hardness-testing when you need to challenge a specific constraint; use this skill when you need to understand how the full constraint map shapes the design problem.

The most common error is treating all constraints as equally hard and using them to justify not exploring territory that's actually open. Separate the hard from the soft before concluding that a class of solution is impossible.


What's Next

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

  • Question: "Constraints mapped. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-design-user-needs — Revisit what the user actually needs within this solution space
    • /s4h-design-iteration — Begin the design cycle within the solution space you've mapped
    • /s4h-constraint-rule-inversion — Flip the tightest constraint into a creative driver
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Design Constraints AI skill do?

Uses constraints productively — maps hard limits, soft preferences, and how the tightest constraints define the solution space. Triggers: 'what are the design constraints', 'map the constraints', 'what are we working within', 'what's fixed', 'constraint-driven design', 'what does the solution space look like', 'working within limits'.

Why use S4h Design Constraints on TypingMind?

Because you install it once and use it with any model. S4h Design Constraints 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 Design Constraints in TypingMind?

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

Which AI models can use S4h Design Constraints?

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 Design Constraints?

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

Is the S4h Design Constraints 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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