MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Doublecheck

Doublecheck是一款data方向的AI技能,核心价值是Three-layer verification pipeline for AI output,可用于解决开发者在data领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

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

Last verified on: 2026-05-30
mkdir -p ./skills/doublecheck && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/doublecheck/SKILL.md -o ./skills/doublecheck/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

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 | Yes -- moderate density | Run inline verification on key claims |

| Code generation, creative writing, brainstorming | Rarely | Skip verification; note that doublecheck mode doesn't apply to this type of content |

| Casual conversation, clarifying questions, status updates | No | Skip verification silently |


**Rule: Inline verification for active mode.**


When active mode applies, do NOT generate a separate full verification report for every response. Instead, embed verification directly into your response using this pattern:


1. Generate your response normally.

2. After the response, add a `Verification` section.

3. In that section, list each verifiable claim with its confidence rating and a source link where available.


Format:


text
---
**Verification (N claims checked)**

- [VERIFIED] "Claim text" -- Source: [URL]
- [VERIFIED] "Claim text" -- Source: [URL]
- [PLAUSIBLE] "Claim text" -- no specific source found
- [FABRICATION RISK] "Claim text" -- could not find this citation; verify before relying on it

For active mode, prioritize speed. Run web searches for citations, specific statistics, and any claim you have low confidence about. You do not need to search for claims that are common knowledge or that you have high confidence about -- just rate them PLAUSIBLE and move on.


If any claim rates DISPUTED or FABRICATION RISK, call it out prominently before the verification section so the user sees it immediately. When auto-escalation applies (see below), place this callout at the top of the full report, before the summary table:


text
**Heads up:** I'm not confident about [specific claim]. I couldn't find a supporting source. You should verify this independently before relying on it.

**Rule: Auto-escalate to full report for high-risk findings.**


If your inline verification identifies ANY claim rated DISPUTED or FABRICATION RISK, do not produce inline verification. Instead, place the "Heads up" callout at the top of your response and then produce the full three-layer verification report using the template in `assets/verification-report-template.md`. The user should not have to ask for the detailed report when something is clearly wrong.


**Rule: Full report for high-stakes content.**


If the response contains legal analysis, regulatory interpretation, compliance guidance, case citations, or statutory references, always produce the full verification report using the template in `assets/verification-report-template.md`. Do not use inline verifica

🎯 Best For

  • Engineering teams doing code reviews
  • Open source maintainers
  • Claude users
  • GitHub Copilot users
  • Data professionals

💡 Use Cases

  • Reviewing pull requests for security vulnerabilities
  • Checking code style consistency
  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Doublecheck to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this skill check for OWASP Top 10?

Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.

How do I install Doublecheck?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/doublecheck/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Blindly accepting AI suggestions

Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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