Gate LLM outputs by confidence and route high-stakes cases to human review.
npx skills add https://github.com/thejefflarson/soundcheck --skill overreliance
Please help me install this Agent Skill. Skill: overreliance Source: https://github.com/thejefflarson/soundcheck/tree/main/.claude/skills/overreliance Command: npx skills add https://github.com/thejefflarson/soundcheck --skill overreliance
LLMs often generate outputs that users treat as ground truth. This skill prevents systems from acting on unverified model results by enforcing explicit gates, disclaimers, and human review when the stakes are high.
Ask the AI to validate the current LLM output, apply a confidence gate, and route high-stakes results to human review before returning content.
High-intent search queries and answers about installing and using this skill.
To prevent treating LLM output as ground truth, apply confidence gates and domain risk checks to route high-stakes cases to human review. This ensures automated systems do not execute critical actions based solely on unverified model results.
The best way to add an AI disclaimer to UI-facing content is to automatically attach a mandatory "AI-generated — verify before acting" notice to every returned content piece. This ensures users are always aware that the output requires verification before use.
You can audit log LLM inputs and outputs for traceability by automatically recording all prompts, returned content, and confidence signals. This creates a full audit trail of automated decisions, enabling complete re-readability and review of model interactions.
Yes, you can route code review decisions to human reviewers when LLM confidence is low. By gating on confidence and domain risk, high-stakes code review outputs are intercepted and sent to humans rather than being returned as raw unverified decisions.
You should not use automated LLM decision-making without human oversight in high-stakes scenarios where domain risk is high or model confidence is low. In these cases, outputs must be routed through human review gates to prevent acting on unverified results.
vecearAutomate LLM evaluation with golden datasets, rubrics, thresholds, and regression reports.
htafollaAnalyze historical logs and reports to refine routing inference patterns.
cellarwoodSanitize user prompts via a Model Armor template using CLI inputs.
jayhjenkinsExecute implementation plans in batches with human review checkpoints.
