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Respondent Panel

Community
glebis
respondent-panel

Run a panel of independent synthetic respondents against copy, a slogan, brand values, a landing page, or any user-facing text — each in an isolated context, each a different specific person — then read the panel for convergence and divergence. Use when the user asks "how does this land", "what would people think of this", "test this tagline", "get reactions to this copy", "run a panel", or wants gut-level audience reaction rather than expert critique. Complements persona-review, which is expert stakeholder critique of a document. Triggers on respondent panel, how does this land, what would people think, test this tagline, reactions to this copy, gut reaction, audience reaction.

Overview

Publisherglebis
Repositoryhumane-agentic-design
Skill namerespondent-panel
Stars
38
Forks
3
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by glebis on GitHub. Read the source before you install it.

Installation

Install the Respondent Panel 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/glebis/humane-agentic-design/tree/main/humane/skills/respondent-panel
  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/glebis/humane-agentic-design.git /tmp/humane-agentic-design
mkdir -p .claude/skills
cp -r /tmp/humane-agentic-design/humane/skills/respondent-panel .claude/skills/respondent-panel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Respondent Panel 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 Respondent Panel 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 Respondent Panel 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.

Respondent Panel

Announce at start: "I'm using the humane:respondent-panel skill to collect gut reactions from isolated synthetic respondents."

Gut-level audience reaction to something user-facing, from several people at once, each of whom has seen only the thing itself.

One reaction is an anecdote. Five reactions that all stumble on the same word is a finding. This skill is about getting the second one.

When to invoke

  • "How does this tagline land?"
  • "What would actual people think of this?"
  • "Test this copy / these brand values / this landing page hero."
  • "Run a respondent panel."
  • After jtbd or brandkit, to check whether the language you landed on survives contact with someone who has not read the reasoning.

When NOT to invoke

  • The artifact is a document meant to be studied — a PRD, a spec, a pitch deck script. That is persona-review: expert stakeholders reading carefully and arguing back. This skill is strangers glancing.
  • You want a usability judgement of an interface. That is nielsen-heuristics.
  • You want the copy fixed. Respondents deliberately do not rewrite; bring their reactions back and revise yourself — ux-writing owns the rewrite, and knows to revise against convergent findings only.

The one rule

Respondents see the artifact and nothing else.

Not the brief, not the JTBD corpus, not the positioning rationale, not the six drafts you rejected, not what you were hoping they would feel. Every sentence of context you add buys you a more agreeable answer and a less true one.

This is why respondents run in isolated contexts — a respondent that shares your session has already read everything you know and cannot un-read it.

Claude Code extras: launch each respondent as the bundled synthetic-respondent agent, all in a single message so they run concurrently and independently. Pass the artifact verbatim plus that respondent's persona brief — nothing else.

In pi: the package ships the same synthetic-respondent agent, discovered from pi.subagents.agents. Launch one subagent per respondent for the panel, each with only its persona brief and the artifact. Separate subagent contexts are what make the isolation real; a respondent you address in this session has read everything you have.

On other agents: run them sequentially in fresh sessions (or after clearing context), pasting only the persona brief and the artifact. If neither is possible, run one respondent and say plainly in the output that it is a single uncontaminated reaction, not a panel.

Workflow

Step 1 — Get the artifact

Ask for the exact text or file to react to. Take it verbatim. Do not tidy typos, expand abbreviations, or add the surrounding context you think is missing — if it is missing for respondents, it will be missing for real readers, and that is itself the finding.

Note the medium, because it sets the encounter: a billboard glimpsed at speed, an app-store subtitle, a landing-page hero, a cold email subject line. Tell each respondent where they are seeing it.

Step 2 — Build the panel

Default to 5 respondents. Three is thin; beyond seven you are paying for repetition.

Vary them along axes that plausibly change the reaction to this artifact. Pick 3–4 axes and make each respondent specific:

AxisWhy it moves the reaction
Relationship to the categoryNewcomer vs. burned-before vs. current happy user of a competitor
Ad exposureSomeone who sees forty pitches a day is bored where a rare viewer is curious
Age / life stageChanges what references land and what reads as dated
Place & language backgroundIdioms and wordplay travel badly; non-native readers catch ambiguity
Buying power over thisSomeone who would pay reads the claims differently than someone who wouldn't

Write each brief as a person, not a segment — two or three concrete sentences. "38, runs a two-van plumbing business outside Leeds, has bought three scheduling apps and abandoned all of them, reads nothing about software" beats "SMB owner, skeptical."

Ask the user to confirm the panel before spending on it, and say which axes you varied and why. If the artifact is aimed at a specific audience the user has already described (a jtbd.json persona, a stated target market), build the panel around that audience rather than a generic public — but keep at least one respondent from outside it, because that is who tells you when the copy only works for insiders.

Step 3 — Run them

All respondents get the identical artifact and medium. Only the persona brief differs. Never tell a respondent what the others said.

Step 4 — Read the panel

The panel is not a vote. Do not average the reactions or declare a winner. Report:

  1. Convergence — anything two or more respondents independently hit. Same word misread, same confusion about what is being sold, same comparison to another brand. This is the strongest signal the method produces; lead with it and quote the respondents directly.
  2. Divergence — where they split, and along which axis. If the newcomers liked it and everyone who has used a competitor was suspicious, that is a much more useful sentence than "reactions were mixed."
  3. Comprehension — how many understood what is being sold, in one pass, without help. Report this as a count. It is often the real finding and it is easy to lose under the more colourful reactions.
  4. Dead spots — parts of the artifact that no respondent mentioned at all. Copy nobody reacted to is copy nobody read.
  5. What actually landed — the specific phrases that worked, named. A panel that only reports problems will get you a rewrite that loses the good parts.

Quote respondents verbatim rather than paraphrasing them into marketing language. The unpolished phrasing is the evidence — "I thought it was an insurance thing" survives translation into "brand-category confusion" badly.

Step 5 — Hand off

State clearly what this panel is and is not: synthetic reactions that surface confusion, clichés, and tone problems cheaply and early. They are a rehearsal for contact with real people, not a replacement for it. Never present panel output as market research, and never attach a confidence percentage to it.

Then offer the next step:

  • Revise the copy with ux-writing against the convergent findings, then re-run the same panel — same briefs, so the comparison is clean.
  • Run before-after if the artifact is claiming a transformation.
  • Take the convergent findings to real users, if any are reachable.

Output

Markdown. Convergence first, then divergence by axis, then comprehension count, then dead spots, then what landed. Full individual reactions go at the end, under a heading, so the reader meets the pattern before the anecdotes.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Respondent Panel AI skill do?

Run a panel of independent synthetic respondents against copy, a slogan, brand values, a landing page, or any user-facing text — each in an isolated context, each a different specific person — then read the panel for convergence and divergence. Use when the user asks "how does this land", "what would people think of this", "test this tagline", "get reactions to this copy", "run a panel", or wants gut-level audience reaction rather than expert critique. Complements persona-review, which is expert stakeholder critique of a document. Triggers on respondent panel, how does this land, what would...

Why use Respondent Panel on TypingMind?

Because you install it once and use it with any model. Respondent Panel 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 Respondent Panel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/humane-agentic-design/tree/main/humane/skills/respondent-panel. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Respondent Panel?

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 Respondent Panel?

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

Is the Respondent Panel AI skill free?

Yes. It is published on GitHub by glebis 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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