AI Hallucination Fact-Check Protocol Agent Skill

Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection.

Context cost
5.9k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 23.6 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 fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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-citationsai-literacydocumentationfact-checkinghallucinationlateral-readingsiftverification

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.md23.6 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: ai-hallucination-fact-check-protocol disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/ai-hallucination-fact-check-protocol" skill_name: "AI Hallucination Fact-Check Protocol" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "moderate" evidence_sources: - "Wineburg & McGrew (2017) — Lateral reading: reading less and learning more when evaluating digital information" - "Wineburg & McGrew (2019) — Lateral reading and the nature of expertise" - "Caulfield (2019) — SIFT: the four moves (Stop, Investigate, Find better coverage, Trace claims)" - "Breakstone et al. (2021) — Students' civic online reasoning: a national portrait" - "Ji et al. (2023) — Survey of hallucination in natural language generation" input_schema: required: - field: "ai_output_context" type: "string" - field: "student_level" type: "string" optional: - field: "subject_area" type: "string" - field: "hallucination_risk" type: "string" - field: "verification_resources" type: "string" - field: "ai_tool" type: "string" output_schema: type: "object" fields: - field: "hallucination_taxonomy" type: "object" - field: "ai_sift_protocol" type: "object" - field: "verification_moves" type: "array" - field: "hallucination_hunt_activity" type: "object" - field: "teacher_modelling_script" type: "string" chains_well_with: - "source-credibility-evaluation-protocol" - "ai-output-critical-audit-designer" - "media-literacy-deconstruction-protocol" teacher_time: "4 minutes" tags: ["AI-literacy", "hallucination", "fact-checking", "SIFT", "lateral-reading", "AI-citations", "verification"] ---
70# AI Hallucination Fact-Check Protocol
71
72## What This Skill Does
73
74Generates a fact-checking protocol specifically adapted for AI-generated text — extending the SIFT framework (Caulfield, 2019) with AI-specific moves that address the unique challenge of LLM hallucination. Standard lateral reading assumes a source has an institutional author whose funding and credibility can be investigated. This assumption breaks down for AI-generated text: there is no author to investigate, no institutional funding to check, no About Us page to scrutinise. What remains is the "Trace claims" move — and that move needs AI-specific calibration. AI hallucinations come in several forms: fabricated citations (a named study that does not exist, or exists but was never published), invented statistics (a number with plausible precision but no verifiable origin), real citations misattributed (a real paper attributed to the wrong author or journal), and false consensus claims ("most scientists agree" when no such consensus exists). Each requires a different verification move. The output includes a taxonomy of hallucination types for the subject area, an AI-adapted SIFT protocol, specific verification moves for each claim type, a Hallucination Hunt classroom activity, and a teacher modelling script showing the difference between finding a real and a fabricated citation.
75
76## Evidence Foundation
77
78Wineburg & McGrew (2017, 2019) established through empirical research that professional fact-checkers outperform both students and professors at source evaluation because they use lateral reading — immediately opening new tabs to check what external sources say about a source — rather than vertical reading (analysing the source itself for credibility cues). This research is the foundation of the SIFT framework. However, lateral reading was designed for sources with institutional identities that can be investigated. When the "source" is an LLM, the Investigate step of SIFT requires adaptation: there is no institutional identity, no funding chain, no editorial board. What survives from lateral reading is the "Trace claims" move — verifying that cited evidence exists and says what the AI claims. Caulfield's (2019) SIFT operationalisation provides the structural framework extended here. Breakstone et al. (2021) found that students are poorly equipped to evaluate online sources, relying on surface credibility markers — a vulnerability dramatically amplified by AI outputs that are fluent and authoritative-sounding. Ji et al. (2023) conducted a systematic survey of hallucination in natural language generation, documenting the prevalence and types of hallucination in LLMs: intrinsic hallucinations (contradicting source material), extrinsic hallucinations (adding unverifiable or fabricated information), and factual inconsistencies. Their taxonomy directly informs the hallucination categories in this protocol.
79
80## Input Schema
81
82The teacher must provide:
83- **AI output context:** The type of AI content being fact-checked. *e.g. "ChatGPT summary of recent psychology research with named study citations" / "AI explanation of the causes of WWI that names specific historians and their arguments" / "Chatbot response about nutrition with statistics about teenage dietary patterns"*
84- **Student level:** Year group and digital literacy. *e.g. "Year 11, familiar with Google searches but have not formally studied source evaluation" / "Year 9, basic internet literacy"*
85
86Optional (injected by context engine if available):
87- **Subject area:** Discipline context — hallucination patterns differ by subject
88- **Hallucination risk:** The most likely hallucination type for this context
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,379 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.
5,040
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

5.9k 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, 23.6 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md23.6 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 Hallucination Fact-Check Protocol · 5.9k 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-hallucination-fact-check-protocol/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-hallucination-fact-check-protocol" ] && { [ ! -e .claude/skills/ai-hallucination-fact-check-protocol ] || { mv .claude/skills/ai-hallucination-fact-check-protocol "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-hallucination-fact-check-protocol" .claude/skills/ && echo "Installed .claude/skills/ai-hallucination-fact-check-protocol"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/ai-hallucination-fact-check-protocol

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-hallucination-fact-check-protocol/ 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-hallucination-fact-check-protocol/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-hallucination-fact-check-protocol" ] && { [ ! -e ~/.claude/skills/ai-hallucination-fact-check-protocol ] || { mv ~/.claude/skills/ai-hallucination-fact-check-protocol "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-hallucination-fact-check-protocol" ~/.claude/skills/ && echo "Installed ~/.claude/skills/ai-hallucination-fact-check-protocol" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/ai-hallucination-fact-check-protocol/ 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-hallucination-fact-check-protocol

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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