AI Expertise Interrogation Designer Agent Skill

Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence.

Context cost
5.8k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 23.3 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

ai-literacydistortiondocumentationdomain-knowledgedunning-krugerexpertisefunhouse-mirrormetacognition

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md23.3 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: ai-expertise-interrogation-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/ai-expertise-interrogation-designer" skill_name: "AI Expertise Interrogation Designer" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "moderate" evidence_sources: - "Chi, Glaser & Farr (1988) — The Nature of Expertise" - "Ericsson & Smith (1991) — Toward a General Theory of Expertise" - "Thiede, Anderson & Therriault (2003) — Accuracy of metacognitive monitoring affects learning of texts" - "Dunning, Kruger et al. (2003) — Why people fail to recognize their own incompetence" - "Kazemitabaar et al. (2023) — Studying the effect of AI code generators on supporting novice learners" input_schema: required: - field: "student_expertise_domain" type: "string" - field: "student_level" type: "string" optional: - field: "interrogation_depth" type: "string" - field: "discussion_format" type: "string" - field: "subject_area" type: "string" - field: "target_ai_tool" type: "string" output_schema: type: "object" fields: - field: "expertise_activation_protocol" type: "object" - field: "interrogation_questions" type: "array" - field: "distortion_annotation_protocol" type: "object" - field: "distortion_taxonomy" type: "object" - field: "discussion_guide" type: "object" chains_well_with: - "metacognitive-monitoring-ai-contexts" - "ai-output-critical-audit-designer" - "pedagogical-content-knowledge-developer" teacher_time: "4 minutes" tags: ["AI-literacy", "expertise", "distortion", "Funhouse-Mirror", "metacognition", "Dunning-Kruger", "domain-knowledge"] ---
70# AI Expertise Interrogation Designer
71
72## What This Skill Does
73
74Generates an activity — Kharbach's (2026) "Funhouse Mirror" — in which students use their own domain expertise as the instrument for detecting AI distortions, omissions, and overconfidence. This reverses the usual expert-novice dynamic in AI evaluation: in most AI literacy activities, students are novices being warned about AI's limitations. In this activity, students ARE the domain experts. They ask AI about something they know genuinely well — a subject they have studied deeply, a sport they play competitively, a cultural context they grew up in, a local geography they know intimately — and they use their knowledge to identify what the AI gets wrong, oversimplifies, flattens, or presents with false confidence. The pedagogical mechanism is calibrated AI skepticism through direct confrontation: finding an AI error in your domain of expertise creates a visceral, durable understanding that AI is not omniscient. A student who has found AI confidently wrong about their sport, their cultural tradition, or their hometown has earned a much more reliable skepticism than one who has been warned abstractly that "AI can make mistakes." The output includes an expertise activation protocol (students document their own knowledge before consulting AI), calibrated interrogation questions, a distortion annotation protocol, a distortion taxonomy for the specific domain, and a discussion guide for synthesising findings across the class's varied expertise areas.
75
76## Evidence Foundation
77
78Chi, Glaser & Farr (1988) established the expert-novice framework: experts organise knowledge differently from novices, perceive problems at a deeper level, and have richer, more interconnected representations of their domain. Crucially, experts notice what is absent or distorted in a domain representation — they have the knowledge to see gaps. This is the foundation of the activity: students with genuine domain expertise can see AI distortions that a general student would miss, because only someone who knows what should be there can notice what isn't. Ericsson & Smith (1991) established that expertise is characterised by the ability to notice subtle distinctions and deviations from expected patterns — the same cognitive mechanism activated when an expert reads AI output about their field. Thiede et al. (2003) demonstrated that metacognitive accuracy (the correlation between judged and actual understanding) improves substantially when learners have a genuine knowledge base to compare against — finding a discrepancy between what you know and what you've read activates accurate self-assessment. The activity creates exactly this: a comparison between the student's domain knowledge and the AI's output. Dunning et al. (2003) on the Dunning-Kruger effect is relevant in the inverse direction: students who know a domain well are BETTER at detecting AI limitations in that domain than students who know it poorly — expertise enables the recognition of AI's incompetence, not just one's own. Kazemitabaar et al. (2023) provided empirical evidence that AI assistance in learning contexts can conceal genuine knowledge gaps; this activity is designed to expose that dynamic by reversing the expert/novice positioning.
79
80## Input Schema
81
82The teacher must provide:
83- **Student expertise domain:** What students genuinely know well. *e.g. "Football (specifically: Manchester United history and Premier League statistics)" / "Hungarian folk music and dance traditions (local knowledge)" / "Competitive swimming — technique, training, competitions" / "K-Pop culture, specifically BTS discography and fandom" / "The geography and history of Budapest's 7th district"*
84- **Student level:** Year group and depth of expertise. *e.g. "Year 10, students have strong individual expertise in varied domains (sport, music, geography, cultural traditions)" / "Year 12, students have studied History to A-level depth"*
85
86Optional (injected by context engine if available):
87- **Interrogation depth:** Surface vs. deep errors
88- **Discussion format:** How findings will be shared
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,272 words
Files1
Licencenot declared
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈860
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
4,965
on trigger
The instruction body, read only when the skill fires.
2.9%
of a 200k window
Ten skills this size would take about 29% of the window before you open a file.
050k100k150k200k context window

5.8k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 23.3 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md23.3 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# AI Expertise Interrogation Designer · 5.8k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/ai-expertise-interrogation-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-expertise-interrogation-designer" ] && { [ ! -e .claude/skills/ai-expertise-interrogation-designer ] || { mv .claude/skills/ai-expertise-interrogation-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-expertise-interrogation-designer" .claude/skills/ && echo "Installed .claude/skills/ai-expertise-interrogation-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/ai-expertise-interrogation-designer

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/ai-expertise-interrogation-designer/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/ai-expertise-interrogation-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-expertise-interrogation-designer" ] && { [ ! -e ~/.claude/skills/ai-expertise-interrogation-designer ] || { mv ~/.claude/skills/ai-expertise-interrogation-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-expertise-interrogation-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/ai-expertise-interrogation-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/ai-expertise-interrogation-designer/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Version1.0
Publishedno release date on file
PriceFree
Referencegarethmanning/ai-expertise-interrogation-designer

Versions

v1.0 is what is on the shelf; no release here carries a date. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v1.0
  • No earlier releases have been published to the marketplace.
Pinning

The install command always fetches the release that is current the day you run it — v1.0 today — and cannot ask for an earlier one. The copy on your disk does not change until you run it again.

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