respondent-panel

respondent-panel is a skill for Claude Code, Codex from glebis/humane-agentic-design. It costs 143 tokens per session (1,672 once invoked), scanned A, original, MIT.

A method for collecting reactions to user-facing text from several separate fictional audience members, each with a different perspective. It looks for patterns in how people respond to copy, slogans, brand values, or landing pages.

In plain words
What is it for?
Use it to test a headline, tagline, brand message, landing-page text, or other customer-facing language and identify points of agreement or disagreement.
Why use it?
One reaction may be personal, while repeated confusion or dislike points to a broader wording problem. The panel helps reveal what people notice before the copy is revised.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present. Also seen: mentions Claude Code.

Part of the humane plugin — 17 skills, 1 agent shipped together

Good fit Use it to test a headline, tagline, brand message, landing-page text, or other customer-facing language and identify points of agreement or disagreement.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/glebis/humane-agentic-design/respondent-panel
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add glebis/humane-agentic-design --skill respondent-panel
Clone the repo
git clone --depth 1 https://github.com/glebis/humane-agentic-design

Made for: Claude Code, Codex.

Or install humane, the plugin that ships this one along with the rest of its 17 skills, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for respondent-panel

README.md
[![agentmods](https://agentmods.dev/badge/skills/glebis/humane-agentic-design/respondent-panel/github.svg)](https://agentmods.dev/skills/glebis/humane-agentic-design/respondent-panel)
Your own site
<a href="https://agentmods.dev/skills/glebis/humane-agentic-design/respondent-panel"><img src="https://agentmods.dev/badge/skills/glebis/humane-agentic-design/respondent-panel/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for respondent-panel

Your own site · 80×15
<a href="https://agentmods.dev/skills/glebis/humane-agentic-design/respondent-panel"><img src="https://agentmods.dev/badge/skills/glebis/humane-agentic-design/respondent-panel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,672 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00143 $0.01672
Opus 5 $0.00072 $0.00836
Sonnet 5 $0.00029 $0.00334
Haiku 4.5 $0.00014 $0.00167

Measured 9d ago against content hash 65c3d771b942, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

respondent-panel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

humane/skills/respondent-panel/SKILL.md · 150 lines

How it starts

The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

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.

Read the full file on GitHub · 150 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 150 lines · 143 tokens per session scan A 65c3d771b942

Subscribe to this mod's changes

respondent-panel is a skill published in the GitHub repository glebis/humane-agentic-design (28 stars, last pushed 7d ago), licensed MIT. It adds 143 tokens to every session and 1,672 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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