doublecheck

Solid

Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.

Data & Documents 34,233 stars 4188 forks Updated today MIT

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Skill Content

# Doublecheck Run a three-layer verification pipeline on AI-generated output. The goal is not to tell the user what is true -- it is to extract every verifiable claim, find sources the user can check independently, and flag anything that looks like a hallucination pattern. ## Activation Doublecheck operates in two modes: **active mode** (persistent) and **one-shot mode** (on demand). ### Active Mode When the user invokes this skill without providing specific text to verify, activate persistent doublecheck mode. Respond with: > **Doublecheck is now active.** I'll verify factual claims in my responses before presenting them. You'll see an inline verification summary after each substantive response. Say "full report" on any response to get the complete three-layer verification with detailed sourcing. Turn it off anytime by saying "turn off doublecheck." Then follow ALL of the rules below for the remainder of the conversation: **Rule: Classify every response before sending it.** Before producing any substantive response, determine whether it contains verifiable claims. Classify the response: | Response type | Contains verifiable claims? | Action | |--------------|---------------------------|--------| | Factual analysis, legal guidance, regulatory interpretation, compliance guidance, or content with case citations or statutory references | Yes -- high density | Run full verification report (see high-stakes content rule below) | | Summary of a document, research, or data ...

Details

Author
github
Repository
github/awesome-copilot
Created
11 months ago
Last Updated
today
Language
Python
License
MIT

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