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S4h Constraint Hardness Testing

Organization
human-avatar
s4h-constraint-hardness-testing

Tests whether a stated constraint is real — distinguishing genuine limits from assumptions, habits, or politics dressed as facts. Triggers: 'is this really a constraint', 'challenge the assumption', 'who says we can't', 'is this actually fixed', 'test the constraint'.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-constraint-hardness-testing
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 Hardness Testing 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-hardness-testing
  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-hardness-testing .claude/skills/s4h-constraint-hardness-testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Constraint Hardness Testing 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 Hardness Testing 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 Hardness Testing 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 Hardness Testing

Organisations accumulate phantom constraints — rules that were real once and calcified, or beliefs that were never tested, or someone's preference that got repeated until it sounded like policy. This skill separates genuine limits from assumed ones before any energy is spent working around or accepting them.


Your Process

Step 1: State the Constraint Write it exactly as it's been stated or assumed. Don't clean it up — the imprecision is often where the phantom lives.

Framing check: Confirm the specific constraint and the goal it is blocking before continuing. State what you've identified — the exact constraint as you understand it and the goal or action it prevents — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the constraint and the goal it blocks]. 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: Source It Who said this constraint exists? When? Is the source a law or regulation, a signed contract, a technical impossibility, a leadership decision, a team preference, or unknown? The source determines the hardness ceiling.

Step 3: Consequence Test What actually happens if this constraint is violated? State the consequence concretely. If the answer is "I'm not sure" or "someone would be unhappy," the constraint is not hard. Distinguish real consequences (fine, contract breach, system failure) from assumed ones (pushback, awkwardness, political cost).

Step 4: Precedent Check Has anyone tried to change or violate this constraint before? What happened? No precedent often means no one has tested it — not that it can't be changed.

Step 5: Conditions Test Under what circumstances would this constraint not apply? A truly hard constraint has no exceptions. If you can find a scenario where it wouldn't hold, the constraint is softer than stated.

Step 6: Classify

  • Hard: real source, concrete consequence, no exceptions, precedent confirms
  • Soft: real but negotiable, consequence is political or preferential
  • Assumed: no clear source, untested consequence
  • Outdated: was once hard, circumstances have changed

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
    • Real vs assumed verdict only — Classify each constraint without full analysis
    • 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 as stated:

[Exact wording]

Source: [Law/contract/technical/decision/preference/unknown] — [specific origin]

Consequence if violated:

[Concrete statement] — Real / Assumed

Precedent: [What happened when tested, or "untested"]

Conditions where it wouldn't apply:

[If any]

Classification: Hard / Soft / Assumed / Outdated

Recommended action:

ClassificationAction
HardAccept — design around it
SoftNegotiate explicitly
AssumedTest it — ask the source directly
OutdatedChallenge it — propose removal

Notes

The most dangerous class is Assumed, because it looks like Hard. Before accepting any constraint that cannot be sourced precisely, treat it as Assumed and test it — the cost of testing is almost always lower than the cost of a permanent workaround.


What's Next

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

  • Question: "Constraints tested. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-constraint-workaround-mapping — Route around the hard constraints
    • /s4h-constraint-scope-reduction — Reduce scope to avoid constraints that can't be bypassed
    • /s4h-decision-option-mapping — See what options remain given hard and soft constraints
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Constraint Hardness Testing AI skill do?

Tests whether a stated constraint is real — distinguishing genuine limits from assumptions, habits, or politics dressed as facts. Triggers: 'is this really a constraint', 'challenge the assumption', 'who says we can't', 'is this actually fixed', 'test the constraint'.

Why use S4h Constraint Hardness Testing on TypingMind?

Because you install it once and use it with any model. S4h Constraint Hardness Testing 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 Hardness Testing 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-hardness-testing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Constraint Hardness Testing?

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 Hardness Testing?

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

Is the S4h Constraint Hardness Testing 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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