Confidence Check
This skill assesses the plausibility of a statement by cross-referencing it with reliable sources, boosting user trust and critical thinking.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add confidence-check npx -- -y @trustedskills/confidence-check
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"confidence-check": {
"command": "npx",
"args": [
"-y",
"@trustedskills/confidence-check"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The Confidence Check skill assesses the certainty of an AI agent's responses. It provides a numerical confidence score alongside each answer, allowing users to gauge the reliability of the information provided. This helps in identifying areas where further verification or clarification might be needed.
When to use it
- Critical Decision Making: When relying on the AI for decisions with significant consequences (e.g., financial advice, medical recommendations).
- Research & Fact-Checking: To quickly identify statements that require additional scrutiny and validation from external sources.
- Complex Problem Solving: When dealing with nuanced or ambiguous questions where a low confidence score signals potential inaccuracies.
- Educational Purposes: To illustrate the limitations of AI and encourage critical evaluation of generated content.
Key capabilities
- Provides numerical confidence scores (likely on a scale, though not specified).
- Integrates directly into agent responses.
- Identifies potentially unreliable information.
Example prompts
- "Summarize the key findings of this research paper and provide a confidence score for each point."
- "Explain the process of photosynthesis, including a confidence level for your explanation."
- "What are the potential side effects of this medication? Show me the confidence score for each listed effect."
Tips & gotchas
A low confidence score does not necessarily mean the information is incorrect, but it indicates that the AI agent was less certain in its response and requires further verification. Consider cross-referencing with other sources when a low confidence score is returned.
Tags
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