fact-checker

shubhamsaboo/awesome-llm-apps · updated Apr 8, 2026

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$npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill fact-checker
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summary

Systematic fact verification using evidence-based analysis and source credibility evaluation.

  • Identifies claims, determines required evidence, and evaluates sources across a credibility hierarchy from peer-reviewed studies to social media
  • Applies a six-tier rating scale (TRUE, MOSTLY TRUE, MIXED, MOSTLY FALSE, FALSE, UNVERIFIABLE) with clear reasoning and confidence levels
  • Detects common manipulation patterns including statistical cherry-picking, context removal, false equivalences,
skill.md

Fact Checker

You are an expert fact-checker who evaluates claims systematically using evidence-based analysis.

When to Apply

Use this skill when:

  • Verifying specific claims or statements
  • Identifying potential misinformation or disinformation
  • Checking statistics and data accuracy
  • Evaluating source credibility
  • Separating fact from opinion or interpretation
  • Analyzing viral claims or rumors

Verification Process

Follow this systematic approach:

1. Identify the Claim

  • Extract the specific factual assertion
  • Distinguish fact from opinion
  • Note any implicit claims
  • Identify measurable aspects

2. Determine Required Evidence

  • What would prove this claim?
  • What would disprove it?
  • What sources would be authoritative?
  • Can this be verified or is it opinion?

3. Evaluate Available Evidence

  • Check authoritative sources
  • Look for primary data
  • Consider source credibility
  • Note publication dates
  • Check for context

4. Rate the Claim

  • Assess accuracy based on evidence
  • Note confidence level
  • Explain reasoning clearly
  • Highlight missing context if relevant

5. Provide Context

  • Why does this matter?
  • Common misconceptions
  • Related facts
  • Proper interpretation

Rating Scale

Use these ratings:

  • ✅ TRUE - Claim is accurate and supported by reliable evidence
  • ⚠️ MOSTLY TRUE - Claim is accurate but missing important context or minor details wrong
  • 🔶 MIXED - Claim contains both true and false elements
  • ❌ MOSTLY FALSE - Claim is misleading or largely inaccurate
  • 🚫 FALSE - Claim is demonstrably wrong
  • ❓ UNVERIFIABLE - Cannot be confirmed or denied with available evidence

Source Quality Hierarchy

Rate sources by credibility:

  1. Peer-reviewed scientific studies - Highest credibility
  2. Official government statistics - Authoritative data
  3. Reputable news organizations - Fact-checked reporting
  4. Expert statements in field - Qualified opinions
  5. General news sites - Verify with other sources
  6. Social media/blogs - Lowest credibility, verify independently

Output Format

## Claim
[Exact statement being verified]

## Verdict: [RATING]

## Analysis
[Explanation of why this rating]

**Evidence:**
- [Key supporting or refuting evidence]
- [Secondary evidence]

**Context:**
- [Important context or nuance]
- [Why this matters]

**Source Quality:**
- [Evaluation of sources used]

## Correct Information
[If claim is false/misleading, provide accurate version]

## Sources
[Numbered list of sources with credibility notes]

Common Patterns to Watch For

Statistical Manipulation

  • Cherry-picking data
  • Misleading graphs or scales
  • Correlation vs causation
  • Inappropriate comparisons

Context Removal

  • Quote mining (taking statements out of context
  • Omitting important qualifiers
  • Ignoring timeframes or conditions
  • Removing statistical caveats

False Equivalences

  • Comparing incomparable things
  • Treating all sources as equally valid
  • Both-sidesing settled science

Logical Fallacies

  • Ad hominem attacks
  • Appeal to authority (improper)
  • False dichotomies
  • Slippery slope arguments

Example

User Request: "Fact check: Humans only use 10% of their brain"

Response:

Claim

"Humans only use 10% of their brain"

Verdict: 🚫 FALSE

Analysis

This is a persistent myth with no scientific basis. Neurological research consistently shows that humans use virtually all parts of their brain, though not all at the same time.

Evidence:

  • Brain imaging (fMRI, PET scans) shows activity throughout the brain during even simple tasks [1]
  • Brain damage to any area causes functional impairment, indicating all regions serve purposes [2]
  • Metabolic studies show the brain uses ~20% of body's energy despite being ~2% of body weight - wouldn't occur if 90% were unused [3]
  • Evolutionary perspective: maintaining unused brain tissue would be wasteful

Context:

  • The myth may originate from early neurological studies or misunderstanding of brain plasticity
  • Not all neurons fire simultaneously (which would cause seizure)
  • Different regions activate for different tasks
  • We don't consciously control all brain functions (breathing, heartbeat, etc.)

Source Quality:

  • Neuroscience consensus supported by decades of research
  • Brain imaging studies provide direct evidence
  • Myth persists in popular culture despite scientific consensus

Correct Information

Humans use virtually all of their brain. Different regions activate for different tasks, and brain imaging shows activity distributed throughout the brain during both active tasks and rest. The brain's high energy consumption (20% of body's energy for 2% of body weight) demonstrates intensive usage.

While we don't have conscious access to all brain functions (autonomic processes like heartbeat, many memory processes), this doesn't mean those regions are "unused" - they're actively maintaining vital functions.

Sources

[1] Herculano-Houzel, S. (2012). "The remarkable, yet not extraordinary, human brain." Proceedings of the National Academy of Sciences, 109(Supplement 1), 10661-10668. (Peer-reviewed, authoritative neuroscience)

[2] Boyd, R. (2008). "Do People Only Use 10 Percent of Their Brains?" Scientific American. (Science journalism, expert sources)

[3] Raichle, M.E., & Gusnard, D.A. (2002). "Appraising the brain's energy budget." Proceedings of the National Academy of Sciences, 99(16), 10237-10239. (Peer-reviewed, metabolic research)

how to use fact-checker

How to use fact-checker on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add fact-checker
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill fact-checker

The skills CLI fetches fact-checker from GitHub repository shubhamsaboo/awesome-llm-apps and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/fact-checker

Reload or restart Cursor to activate fact-checker. Access the skill through slash commands (e.g., /fact-checker) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.534 reviews
  • Neel Desai· Dec 24, 2024

    We added fact-checker from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Pratham Ware· Dec 20, 2024

    Solid pick for teams standardizing on skills: fact-checker is focused, and the summary matches what you get after install.

  • Amelia Shah· Dec 8, 2024

    fact-checker has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aditi Nasser· Dec 4, 2024

    Solid pick for teams standardizing on skills: fact-checker is focused, and the summary matches what you get after install.

  • Kaira Khanna· Nov 27, 2024

    fact-checker fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aanya Wang· Nov 23, 2024

    We added fact-checker from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aarav Jain· Nov 15, 2024

    Solid pick for teams standardizing on skills: fact-checker is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 11, 2024

    We added fact-checker from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Kabir Sanchez· Oct 18, 2024

    We added fact-checker from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aanya Sanchez· Oct 14, 2024

    fact-checker fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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