Confidence Scoring
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
npx skills add majiayu000/claude-skill-registry --skill confidence-scoring --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Confidence Scoring This skill is covered in detail in the main **Model Explainability** skill. Please refer to: `44-ai-governance/model-explainability/SKILL.md` That skill covers: - SHAP and LIME for feature importance - Confidence scoring and interpretation - Calibration techniques - Explainability for different model types - LLM-specific explainability - Presenting explanations to users - Tools (SHAP, LIME, InterpretML, Captum) - Real-world explainability examples For confidence-specific topics, also see: - Confidence thresholds in `44-ai-governance/human-approval-flows` - Model risk management in `44-ai-governance/model-risk-management` --- ## Related Skills * `44-ai-governance/model-explainability` (Main skill) * `44-ai-governance/human-approval-flows` * `44-ai-governance/model-risk-management`
- Related Skills
What does the Confidence Scoring skill do?
See the main Model Explainability skill for comprehensive coverage of confidence scoring and calibration.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill confidence-scoring --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.