S4h Ethics Consent Review logo

S4h Ethics Consent Review

Organization
human-avatar
s4h-ethics-consent-review

Review a UX flow, data practice, or communication pattern to verify that user consent is genuine — informed, voluntary, and meaningful. Use during design or implementation of checkout flows, onboarding, notification settings, permissions requests, terms of service, dark patterns, or any feature where users make choices about what they share or agree to. TRIGGERS: 'consent review', 'is this dark pattern', 'check this onboarding flow', 'is this consent genuine', any UX that involves users agreeing to something, sharing data, or opting in/out.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-ethics-consent-review
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 Ethics Consent Review 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-ethics-consent-review
  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-ethics-consent-review .claude/skills/s4h-ethics-consent-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Ethics Consent Review 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 Ethics Consent Review 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 Ethics Consent Review 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.

Ethics Consent Review

Consent is not a checkbox. It is a meaningful act by a person who understands what they're agreeing to, genuinely has the option to decline, and isn't being manipulated into compliance.

Most consent failures aren't malicious — they're the accumulated result of copy-paste terms, optimised conversion flows, and defaults set by people who never questioned them. This review surfaces those failures before they become patterns users resent or regulators flag.


The Three Tests

Every consent decision must pass three tests:

Informed — Does the person genuinely understand what they're agreeing to? Not technically (buried in ToS), but practically. If you explained it plainly in conversation, would they be surprised?

Voluntary — Does the person genuinely have the ability to decline? Is declining as easy as accepting? Are there consequences for declining that make the choice effectively coerced?

Meaningful — Does the person's choice actually matter? Is there genuine optionality, or is the "choice" cosmetic — the default is set to the outcome the business wants and the friction to change it is high?


Your Process

Step 1: Map the consent moment What specifically is the user being asked to consent to? When in the flow does this happen? What is the default? What happens if they decline?

Framing check: Confirm the ethical situation and the parties affected before continuing. State what you've identified — the specific consent moment, the UX or data practice under review, and who is giving consent — in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [your one-sentence framing of the specific consent moment and context]. 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: Apply the Informed test

  • Is the language plain enough for a non-expert to understand?
  • Is the material information prominent, or buried in fine print?
  • Is the full scope of what's being agreed to clear — or is it vague enough to cover future practices the user can't anticipate?
  • Would a reasonable user be surprised by how this consent is actually used downstream?

Step 3: Apply the Voluntary test

  • What happens if the user declines? Do they lose access to the core service? (If so, consent is coerced.)
  • Is declining as visually and mechanically easy as accepting?
  • Is there pressure — time limits, repeated prompts, fear language — that reduces voluntary choice?
  • Are there bundled consents (agreeing to X requires also agreeing to Y) that prevent granular choice?

Step 4: Apply the Meaningful test Review for dark patterns — design choices that steer users toward a specific outcome regardless of their preference:

  • Confirmshaming — labelling the decline option pejoratively ("No thanks, I don't want to save money")
  • Roach motel — easy to opt in, deliberately difficult to opt out
  • Hidden defaults — default to the business-favourable option, buried change option
  • Misdirection — visual hierarchy or motion that draws attention away from the decline option
  • Forced continuity — consent obtained once treated as permanent consent for expanding uses

Step 5: Assess the power dynamic Is this a context where users have genuine alternatives? Can they use a competing product without this consent? Are any users in a particularly vulnerable position (under duress, time pressure, low digital literacy) where their ability to exercise real choice is diminished?


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
    • Dark patterns only — Flag manipulative design elements specifically
    • 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

Flow Being Reviewed: [What UX, what consent moment, what the default is]

Informed Test

  • Plain language: ✅ / ⚠️ / ❌ — [finding]
  • Material information prominent: ✅ / ⚠️ / ❌ — [finding]
  • Scope clarity: ✅ / ⚠️ / ❌ — [finding]
  • Surprise test: ✅ / ⚠️ / ❌ — [finding]

Voluntary Test

  • Declining is easy: ✅ / ⚠️ / ❌ — [finding]
  • No coercion: ✅ / ⚠️ / ❌ — [finding]
  • Granular choice: ✅ / ⚠️ / ❌ — [finding]

Meaningful Test Dark patterns detected:

  • [Pattern name if present]: ✅ None / ⚠️ Minor / ❌ Significant — [finding]

Power Dynamic [1–2 sentences: do users have genuine alternatives; any vulnerability concerns]

Verdict [Is this consent genuine? What are the specific problems if any?]

Recommended Changes

  • [Specific fix per failing item]

Notes

The standard is not "legally defensible consent." It is "consent a reasonable person would consider genuine." Those are not the same standard, and in the long run, the second one matters more for user trust.

Where dark patterns are found, name them specifically. Vague concerns are easy to dismiss; named patterns with clear examples are not.


What's Next

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

  • Question: "Consent reviewed. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-ethics-empathy-circle — Apply empathy to those whose consent is in question
    • /s4h-communication-audience-modeling — Model how to communicate consent requirements
    • /s4h-ethics-impact-scan — Scan for impact on those whose consent was not obtained
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Ethics Consent Review AI skill do?

Review a UX flow, data practice, or communication pattern to verify that user consent is genuine — informed, voluntary, and meaningful. Use during design or implementation of checkout flows, onboarding, notification settings, permissions requests, terms of service, dark patterns, or any feature where users make choices about what they share or agree to. TRIGGERS: 'consent review', 'is this dark pattern', 'check this onboarding flow', 'is this consent genuine', any UX that involves users agreeing to something, sharing data, or opting in/out.

Why use S4h Ethics Consent Review on TypingMind?

Because you install it once and use it with any model. S4h Ethics Consent Review 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 Ethics Consent Review in TypingMind?

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

Which AI models can use S4h Ethics Consent Review?

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 Ethics Consent Review?

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

Is the S4h Ethics Consent Review 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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