lightrun-ai

Answer questions about live production system behavior — current variable values, execution durations, hit counts, and value distributions — by instrumenting running services with Lightrun MCP tools. Use when the question requires live runtime data rather than static code analysis (e.g. "show recent requests to this endpoint", "show the runtime distribution for this operation", "what values appear for this expression in production?", "which branch runs for customer X?").

Use when reviewing a pull request with runtime or production evidence — for example to review a PR with runtime verification, gather production evidence, or simulate a patch on live samples. Reviews a pull request by diffing against the deployed code, checking historical coverage, collecting live samples when needed, and simulating the patch on captured production inputs.

Guide deterministic runtime investigations in environments using Lightrun MCP tools, with preflight gating, recovery/resume rules, evidence-first diagnosis, PR-first fix proposal delivery, and local source-code fallback only when PR creation is not possible.

Guide deterministic runtime investigations in live environments using Lightrun MCP tools, with preflight gating, recovery/resume rules, evidence-first diagnosis, and explicit blocker/handoff outputs.