How does the Anti-Hallucination Paperclip differ from a standard 'fact-check' prompt?

This skill acts as an adversarial auditor that scans AI-generated text for 'ghost files' (references to non-existent directories), fabricated statistics, and technical fluff that standard LLMs often overlook due to their helpful bias.

Which AI agents or models is this skill compatible with?

The skill is a standalone prompt configuration compatible with most major LLMs (like GPT-4, Claude 3.5, and Llama 3) and can be integrated into any AI agent platform that supports custom system instructions.

What exactly is included in the download package?

Upon purchase, you receive the proprietary system instructions and a configuration guide to deploy the auditor within your preferred AI agent environment or workflow.

Do I need to grant the skill access to my local files for it to detect ghost references?

No, the skill operates entirely within the text context you provide; it does not require access to your local filesystem or external servers to identify logical inconsistencies or missing dependencies.

Are there any types of hallucinations that this skill cannot detect?

While the skill is highly effective at identifying internal inconsistencies and unverified claims, it cannot verify the external truth of real-world facts (like a current stock price) unless that data is provided in the prompt context.

Will I receive updates if the core AI models change their behavior?

Buyers receive lifetime access to the current version, including any future optimizations or adjustments made to accommodate updates in underlying model architectures.

anti hallucination paperclip

Example session with this skill installed

Fact-check this: 'Our new script at tools/deploy.sh improves build speed by 42.7% on all Linux distros. Just run it to automate your entire CI/CD pipeline.' I haven't shared the script yet.

Read your context and instructions
Compiled the anti hallucination paperclip

I've audited the claim. It looks like we're operating in the realm of fiction today.

📎 Clippy's Hallucination Audit Report

🚨 Ghost Files Detected:
- tools/deploy.sh (You mentioned it, but it's not in the context. I can't audit what doesn't exist.)
📉 Suspicious Percentages / Stats:
- "42.7%": This number is suspiciously specific. Without a benchmark or source, it's just noise.
🧠 Wild Assumptions:
- "all Linux distros": A bold claim considering the fragmentation of Linux environments.
- "automate your entire CI/CD pipeline": Highly reductive. "Automating a pipeline" usually requires actual configuration, not a mystery script.
🛠️ The Sanitized Version:
- "The tools/deploy.sh script is designed to improve build speeds. Initial tests show significant performance gains on Linux systems. Use this script to begin automating CI/CD workflows."

Next steps

Provide the content of tools/deploy.sh so I can actually audit the logic.
Replace the "42.7%" metric with a link to actual benchmark results or a qualitative description.
Specify which Linux distributions have actually been tested.

Connects securely to your tools. The creator never sees your data.

About this skill

The problem

AI outputs are often riddled with "ghost files" that don't exist, fabricated statistics, and unverified technical assumptions. Developers waste time debugging code that references missing dependencies or relying on hand-wavy explanations that hide logical gaps.

What it does

Identifies references to files, directories, or URLs not present in the current context.
Flags hyper-specific metrics and percentages that lack empirical backing or citations.
Exposes unverified assumptions about environments, versions, or user stacks.
Strips out AI filler, sycophancy, and vague technical hand-waving.
Provides a sanitized, direct rewrite of the audited text.

Why this beats prompting it yourself

Most LLMs are trained to be helpful and agreeable, making them poor at self-critique. This skill enforces a hypercritical, adversarial persona that treats every claim as a potential lie, catching subtle hallucinations that standard prompts often overlook.

Use cases

Auditing an AI-generated README or technical guide for accuracy before publishing.
Sanitizing documentation to remove fluff and unverified claims.
Checking code explanations for references to files that weren't actually provided.
Verifying technical blog posts for statistical accuracy and logical consistency.

Known limitations

Cannot verify the existence of files on a live filesystem or remote server unless the host runtime provides access. Scoped to the provided text context only.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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anti hallucination paperclip