AI Output Critical Audit Designer Agent Skill

Design a structured protocol for auditing AI-generated text against Ennis's six CT standards.

Context cost
6k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 23.9 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 structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.

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-literacyai-outputannotationauditcritical-thinkingennisepistemichallucination

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.md23.9 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: ai-output-critical-audit-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/ai-output-critical-audit-designer" skill_name: "AI Output Critical Audit Designer" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "strong" evidence_sources: - "Ennis (2015) — Critical thinking: a streamlined conception" - "Paul & Elder (2008) — The Miniature Guide to Critical Thinking Concepts and Tools" - "Facione (1990) — Critical Thinking: a statement of expert consensus (Delphi report)" - "Dai et al. (2023) — Can large language models provide useful feedback on research papers? A large-scale empirical analysis" - "Wineburg & McGrew (2019) — Lateral reading and the nature of expertise" input_schema: required: - field: "ai_output_sample" type: "string" - field: "student_level" type: "string" optional: - field: "ct_standard_focus" type: "string" - field: "subject_area" type: "string" - field: "task_context" type: "string" - field: "student_profiles" type: "array" output_schema: type: "object" fields: - field: "ai_failure_mode_analysis" type: "object" - field: "annotation_protocol" type: "object" - field: "audit_rubric" type: "object" - field: "push_back_stems" type: "array" - field: "teacher_modelling_script" type: "string" chains_well_with: - "critical-thinking-task-designer" - "source-credibility-evaluation-protocol" - "elaborative-interrogation-generator" - "ai-hallucination-fact-check-protocol" teacher_time: "4 minutes" tags: ["AI-literacy", "critical-thinking", "Ennis", "audit", "annotation", "AI-output", "hallucination", "epistemic"] ---
71# AI Output Critical Audit Designer
72
73## What This Skill Does
74
75Generates a structured protocol for critically auditing AI-generated text against Ennis's (2015) six critical thinking standards — clarity, accuracy, precision, relevance, depth, and breadth — with adaptations that address AI-characteristic failure modes not covered by general critical thinking frameworks. The key pedagogical challenge is that AI-generated text is fluent, confident, and well-formed, which makes it harder to evaluate critically than text that looks suspicious. Standard source credibility heuristics (Who wrote this? Who funds it?) break down because the author is an LLM. What replaces them is a close-reading protocol trained on AI-specific patterns: assertions stated with unearned confidence, claims with plausible-sounding precision but no verifiable source, expert-sounding language without genuine epistemic depth, and the systematic absence of "I don't know." This skill generates the domain anchor for the ai-literacy suite: an annotation protocol (students mark up AI text in real time), an audit rubric (scoring AI text Weak/Moderate/Strong on each CT standard), push-back sentence stems calibrated for AI failure modes, and a teacher modelling script. This is the equivalent of sourcing-skill-builder in the historical-thinking domain — the foundational move students must learn before they can do the more specialised work.
76
77## Evidence Foundation
78
79Ennis (2015) provided a streamlined CT framework built on six intellectual standards: **clarity** (the claim is expressed precisely enough to evaluate), **accuracy** (the claim corresponds to reality), **precision** (the claim is specific enough to be useful), **relevance** (the claim addresses the question at hand), **depth** (the claim engages with the real complexity of the issue), and **breadth** (the claim considers multiple perspectives). These standards are the explicit theoretical grounding for Kharbach's (2026) AI-age CT activities. Paul & Elder (2008) operationalised similar standards into the intellectual standards framework used widely in CT education, providing the pedagogical tradition behind Ennis's schema. Facione's (1990) Delphi consensus defined CT as comprising interpretation, analysis, evaluation, inference, explanation, and self-regulation — the evaluative dimension is precisely what AI audit activates. Dai et al. (2023) conducted a large-scale empirical analysis of LLM-generated feedback and documented the characteristic failure pattern: AI outputs are fluent, well-structured, and tend toward overconfidence with insufficient epistemic hedging. Their findings — that LLMs produce vague suggestions while avoiding identification of specific errors — map directly onto Ennis's precision and accuracy standards. Wineburg & McGrew (2019) established that effective text evaluation requires what they call "disciplined scrutiny" — a trained, protocol-driven reading practice rather than intuitive judgment. This provides the methodological justification for a structured annotation protocol rather than open-ended evaluation.
80
81## Input Schema
82
83The teacher must provide:
84- **AI output sample:** The specific AI text to audit, OR a description of the type to design the protocol for. *e.g. "This is the text of a ChatGPT response about the causes of World War One: [paste text]" / "AI-generated essay introduction for a Year 10 Geography task on climate change" / "Chatbot explanation of quadratic equations for Year 9 Maths students"*
85- **Student level:** Year group and CT experience. *e.g. "Year 10, can identify basic argument claims but haven't formally studied CT standards" / "Year 12, familiar with Paul & Elder framework"*
86
87Optional (injected by context engine if available):
88- **CT standard focus:** Which standards to emphasise. *e.g. "Accuracy and precision — students often accept AI statistics without checking" / "All six — for a full audit activity"*
89- **Subject area:** The discipline context — what counts as adequate evidence differs by subject
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,411 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.
5,115
on trigger
The instruction body, read only when the skill fires.
3.0%
of a 200k window
Ten skills this size would take about 30% of the window before you open a file.
050k100k150k200k context window

6k 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, 23.9 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md23.9 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 Output Critical Audit Designer · 6k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/garethmanning-ai-output-critical-audit-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-output-critical-audit-designer" ] && { [ ! -e .claude/skills/ai-output-critical-audit-designer ] || { mv .claude/skills/ai-output-critical-audit-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-output-critical-audit-designer" .claude/skills/ && echo "Installed .claude/skills/ai-output-critical-audit-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/ai-output-critical-audit-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-output-critical-audit-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/garethmanning-ai-output-critical-audit-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-output-critical-audit-designer" ] && { [ ! -e ~/.claude/skills/ai-output-critical-audit-designer ] || { mv ~/.claude/skills/ai-output-critical-audit-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-output-critical-audit-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/ai-output-critical-audit-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/ai-output-critical-audit-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-output-critical-audit-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.

Reviews

—no reviews yet

Nobody has reviewed this skill yet. The rating is the mean of the reviews written here, so there is none until somebody writes the first.

Sign in to review it

A review is tied to an account so it can be answered, edited and taken down by the person who wrote it.

Sign in
Who can post

Anybody with an account, one review each. It carries the version that was current when you wrote it, and the publisher may reply once.

Publisher
Servers0
Claim this skill