Get Lightrun AI Skills: Expert Workflows for AI Agents

Today we’re launching Lightrun AI Skills, structured, repeatable investigation workflows built for AI coding agents. With Lightrun MCP, agents like Claude Code, Codex, and Cursor can already instrument live production services and reason over live runtime evidence without a redeployment. But AI agents remain non-deterministic by design, using the same tool differently every session. Lightrun AI Skills fix that, putting Lightrun’s runtime engineering expertise directly into the agent reasoning layer.

Key Takeaways

• Lightrun MCP gives AI coding assistants the ability to instrument live production services and reason over runtime evidence without a redeployment.
• AI agents are non-deterministic by design. Without structure, even powerful tools produce inconsistent investigation outcomes.
• Lightrun AI Skills encode Lightrun’s runtime engineering expertise directly into the agent layer as structured, repeatable workflows.
• Every investigation guided by a skill follows the same evidence-first method, separating observed facts from inferred conclusions.
• The first skill is Live Runtime Debugging. More investigation types will follow.

Your AI Agent Can See Inside Production. Now It Needs to Know What to Do There.

A production investigation doesn’t just require access to a tool, it needs a clear method. Forming hypotheses before the work begins, validating the target environment, tying every signal to a specific question: experienced engineers do this implicitly. An AI agent doesn’t arrive with that discipline.

They learned it through failures, through the deployed fix that didn’t hold, the incident that came back, the 2am page that turned out to be caused by something the first diagnosis missed entirely.

AI agents have access to every tool, but they do not have scar tissue.

Stack Overflow’s 2025 Developer Survey found that only 31% of experienced developers trust AI accuracy in coding contexts. Lightrun’s own State of AI-Powered Engineering Report 2026 found that 60% of enterprise engineering teams still rely on tribal knowledge rather than AI tools to resolve high-severity issues in over half of incidents. The trust gap is real, and a significant part of it traces back to this: agents improvise when they should follow structure, and engineers have been burned enough times to notice.

The answer is not to limit what AI agents can do. It is to give them the expert method that makes their full capability reliable.

What Is a Lightrun AI Skill?

A Lightrun AI Skill is a reusable, structured workflow built for AI coding agents. It defines how each agent will conduct a specific type of investigation using Lightrun MCP: what to do first, how to form and test hypotheses, what evidence to collect before drawing a conclusion, and how to communicate what it found versus what it inferred.

Skills are not prompts. A prompt is a one-time instruction that shapes a single response. A skill is a repeatable workflow that an AI agent follows consistently across sessions, models, and contexts, regardless of how the conversation started.

Skills are also not scripts. The agent still reasons, adapts, and makes decisions based on what it finds. The skill locks in the structural scaffolding: the phases of an investigation, the gates between them, the evidentiary standard before a conclusion. It’s reasoning runs using the model, while its investigation structure follows the skill. That combination is what makes outcomes both consistent and trustworthy.

Lightrun MCP provides capability. Lightrun AI Skills guarantee method.

Why Expertise in the Agent Layer Changes Everything

Lightrun MCP gives AI coding agents something no other tool does: the ability to generate missing evidence on demand, at the exact failure line, in a live service, without touching the deployment pipeline. That is what separates runtime investigation from guessing.

Getting the most out of that capability requires the AI to know how to use this new power well.

Lightrun has spent years working with engineering teams on production investigations. That work produced a set of practices that reliably produce correct diagnoses: which sources to discover first, how to select the right runtime target when several candidates match, how to size a collection window so evidence arrives before the hypothesis expires, how to distinguish a confirmed root cause from a probable one, when to ask the engineer to reproduce the issue versus when to collect passively.

These practices exist because runtime investigation is unforgiving. An incorrect diagnosis is not a failed unit test. It is a deployed fix that does not hold, or expands the incident blast radius. An incident that recurs. An engineer who stops trusting their tools.

Skills put that institutional knowledge to work for every team using these agents, not as a constraint, but as an amplifier. The result is an agent that approaches any investigation the way a senior Lightrun engineer would: with hypotheses, with a plan, with a clear standard for what counts as evidence.

The typical flow:

1. A developer asks an AI agent to investigate an issue, 
2. The agent follows a Lightrun AI Skill,
3. The skill guides the agent through Lightrun MCP, 
4. The AI uses the MCP to retrieve live runtime context from the target application.

Lightrun AI Skills is a growing library. The first skill is Live Runtime Debugging. More will follow

The First Skill: Live Runtime Debugging

The Live Runtime Debugging Skill guides an AI coding agent through a deterministic investigation of runtime issues in live applications. It targets the class of problems that are hard to reproduce locally, that static analysis cannot explain, and that standard monitoring misses entirely because the failure produced no alert.

Use it when:

• An issue is only reproducible in staging or production.
• Static code analysis is not enough to explain the behavior.
• You need to validate a root-cause hypothesis with live expression values, call stacks, execution counts, durations, or custom metrics.
• You want a structured handoff that includes runtime facts, ruled-out hypotheses, confidence level, and a concrete next action.

The skill is available in the GitHub Lightrun AI repository and works with Cursor, Claude Code, and Codex. Install the package once; all current and future skills are included.

How the Live Runtime Debugging Skill Works

When you invoke the Live Runtime Debugging Skill, the agent follows a structured investigation flow:

1. Frame the investigation. The agent confirms the symptom, its impact, the expected behavior, and the specific question the investigation needs to answer. It lists at least two plausible hypotheses before selecting any tool.
2. Run the preflight check. Before any instrumentation, the agent calls get_runtime_sources to discover available Lightrun runtime sources. This step prevents the agent from inserting actions against the wrong service or environment, a common failure mode when agents skip source validation.
3. Select the target. The agent identifies the most relevant runtime source based on the service, environment, and expected trigger path. If several targets match, it asks you to choose.
4. Collect evidence against the active hypothesis. Every runtime action: every dynamic log, snapshot, and metric, is tied to a specific signal that confirms or weakens a hypothesis. The agent does not instrument broadly. It instruments precisely.
5. Ask for reproduction. The agent tells you when actions are live and asks you to reproduce the issue. It waits for evidence before advancing to the next hypothesis.
6. Produce the final handoff. The diagnosis includes the confirmed root cause, confidence level, supporting evidence, eliminated hypotheses and the reasoning behind each, remaining unknowns, and a concrete next action.

Each phase gates the next. The agent cannot skip preflight, or produce a diagnosis without evidence. That is not a limitation, nut what makes the AI’s output reliable.

What Determinism Changes In AI-Led Investigations

Without a skill, an agent investigation is a gamble. The agent might get it right. It might skip source validation, instrument the wrong service, and deliver a confident diagnosis that sends the team in the wrong direction entirely. The engineer deploys the fix. The incident recurs.

With a skill, every investigation follows the same evidentiary standard. The diagnosis comes with a stated confidence level, explicit supporting evidence, and a list of what was ruled out and why. When confidence is high, the fix holds. When it is not, the handoff tells the next person exactly where the investigation stands so they can pick it up without starting over.

That is the shift: from AI that might get it right to AI that shows its work. And that is the principle behind every skill Lightrun will build.

Getting Started

Install the Lightrun AI Skills package from the GitHub Lightrun AI repository. This repo includes installation guides for Cursor, Claude Code, and Codex. 

Once you have installed the skill, your AI agent will be able to access all current and future skills created by Lightrun.

Before using any skill, confirm that Lightrun MCP is configured for your AI client and that OAuth authorization is complete. The MCP quickstart guide covers setup.

Once installed, open a new chat with your AI agent and ask it to use the Live Runtime Debugging Skill for the issue you want to investigate. Describe the symptom, the target service, and what you already know. The skill takes it from there.