Detect Common Wet-Lab Errors

Overview

detect_common_wetlab_errors is a video-based error detection layer for the LabOS lab safety and compliance stack. It analyzes XR headset or fixed-camera footage to identify observable wet-lab mistakes — pipette volume mismatches, skipped reagent additions, uncapped tubes before centrifugation, cross-contamination risks, sample labeling errors, and protocol-agnostic hazards — that may not be caught by protocol-step matching alone. Each detected error is emitted as a structured JSON record with type, timestamp, severity, affected object, and a suggested corrective action, enabling real-time XR alerts, post-experiment audit reports, or integration with protocol_video_matching for a unified compliance dashboard.

When to Use This Skill

Use this skill when any of the following conditions are present:

• Real-time safety monitoring: A live XR or bench camera feed must be monitored for common procedural errors so the operator can be alerted immediately (e.g., "Tube uncapped before centrifuge — cap before spinning").
• Post-hoc experiment audit: A recorded experiment video must be scanned for errors that could explain failed or inconsistent results — forgotten reagent, wrong tube, contamination event.
• Training and quality assurance: A trainee's recorded run must be reviewed for error patterns; the skill flags occurrences for feedback and coaching.
• Protocol-agnostic error detection: Errors that are universally hazardous (uncapped tube in centrifuge, pipette tip reuse across samples) must be detected even when no protocol context is available.
• Complement to protocol matching: protocol_video_matching detects step deviations; this skill detects physical/safety errors that may or may not align with protocol steps — use both for comprehensive compliance.
• Root cause analysis: An experiment failed; the video is re-analyzed to identify whether a detectable error (e.g., reagent not added, tube mix-up) could explain the failure.
• GMP/GLP documentation: A regulated workflow requires documented evidence of error detection and correction; the skill's JSON output serves as an audit trail.
• Lab automation handoff: Before a human hands samples to a robot (Opentrons, Hamilton), the skill verifies that tubes are capped, labels are visible, and no obvious contamination is present.