From lightrun-ai
Uses Lightrun MCP tools to answer questions about live production behavior: variable values, execution durations, hit counts, and value distributions. Use when static code analysis is insufficient.
How this skill is triggered — by the user, by Claude, or both
Slash command
/lightrun-ai:lightrun-ask-prodThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Query live production runtime to answer questions about system behavior using Lightrun's observability tools.
Query live production runtime to answer questions about system behavior using Lightrun's observability tools.
Follow MCP tool discovery. Read MCP tool schemas and descriptions at run time; do not assume fixed tool names or client prefixes.
Read the user's question and identify:
Select the Lightrun capability that best fits the question. More than one may be needed.
| If the question asks... | Use... |
|---|---|
| What is the current value of X? | Snapshot/expression capture capability discovered from the currently exposed MCP tools |
| How long does operation X take? | Execution duration capability discovered from the currently exposed MCP tools |
| How often does line X run? | Execution count capability discovered from the currently exposed MCP tools |
| What range of values does X take over time? | Distribution / numeric metric capability discovered from the currently exposed MCP tools |
Before calling any tool, identify the specific file path and line number that will yield the needed information.
Useful signals by question type:
If the code location is ambiguous, ask the user to clarify before proceeding. Do not guess.
Complete source discovery using read-only discovery tools (see Source discovery (preflight)). Follow tool-description usage flow and pagination when the pool or agent list is large.
Apply this selection logic:
Do not present a single-instance result as a global production answer. When full coverage is not available, describe the observed scope explicitly.
Call the appropriate Lightrun tool with:
Use runtime actions for evidence collection:
Interpret the returned data in the context of the user's question:
| Situation | Action |
|---|---|
| No agent pools found | Inform the user; ask them to verify the service is running and connected to Lightrun |
| Multiple plausible agent pools | Present the list to the user and ask which to use |
| Multiple plausible agents within a pool | Use agents, tags, and metadata from source discovery to narrow down; if still ambiguous, ask the user |
| A single source cannot answer a fleet-level question | Target a tag, custom source, or multiple agents that match the requested scope; if full coverage is unavailable, frame the answer in terms of selected sources |
| Tool returns no data | Keep the action active for a longer observation window; if still empty, consider whether the code path is being actively exercised |
| Longer observation window needed | State the observation window, action ID, selected source scope, signal being collected, and condition for checking results later |
| Line cannot be instrumented | Try an adjacent line; if still unavailable, explain the limitation to the user |
| Code location unclear | Ask the user before proceeding — do not guess |
cacheSize in the selected production instances.POST /api/v1/users.varX is true?calculateDiscount runs for customer X?npx claudepluginhub lightrun-platform/lightrun-ai --plugin lightrun-ask-prodPlaces live log probes on running services to capture runtime argument/variable values without redeploying. Useful for inspecting live data from production methods.
Monitors PostHog APM/OpenTelemetry spans for RED metric regressions (error rate, p95 latency, volume) per service/operation against seasonality-matched baselines, plus new error signatures and failing dependencies.
Guides reproducible runtime debugging workflows with Lightrun MCP tools, including hypothesis ranking, runtime evidence capture, blocker handling, and PR-first fix proposals.