Production Debugging with AI Skills
When something breaks in production, the natural next step of any engineer is to debug it. And with an LLM, you have a collaborator, with the added benefit that it speaks every tool in your stack fluently.
The model knows gcloud logging read,jq filters, and how to trace stack frames. The problem is that it lacks system-specific knowledge, covering things like which log fields matter, which identifiers correlate, or where to look first.
Here’s how we use Skills to bridge that gap. They point pre-trained intuition at your infrastructure, using what the model already knows.
The Naive Approach
The first instinct when debugging with LLMs is to paste logs directly into the conversation. The model reads them, identifies patterns, and suggests fixes.
This works for small problems, and the model is really good at handling a few dozen lines of output and a clear error message.
But production debugging generates volume. A GCS query returns hundreds of log entries. Each entry contains timestamps, context fields, stack traces, nested JSON. The signal-to-noise ratio collapses. Then we end up with the model burning its context window on noise.
More subtly, the model then starts guessing. Faced with incomplete information, it fills gaps with plausible-sounding explanations. The very capability that makes it useful — generating coherent narratives from partial data — becomes a liability when you need precision.
The Skill Pattern
The solution is to externalize system-specific knowledge into a structure the model can load on demand: a skill.
The model already knows how to use gcloud, so teaching the basics of well-defined and well-documented knowledge is not the purpose of a skill. A skill, instead, tells the model which fields to query, which identifiers correlate, and when todelegate verbose data elsewhere. It encodes institutional memory — the lessons from previous production fires — into a reusable artifact.
In essence, the skill defines the things that are system-specific or unique to your implementation.
Three things happen when you structure knowledge this way:
Context hygiene. Skills can instruct the model to spawn subagents for verbose operations. Instead of dumping logs into the main context, the model delegates retrieval and receives a summary. The reasoning context stays clean.
Methodology enforcement. Skills can pair with each other. A debugging skill defines the investigation process:investigate, analyze, hypothesize, fix. A domain-specific skill supplies the particulars. The model applies a consistent methodology without human intervention.
Just-in-time loading. The skill activates only when needed. You don’t burn tokens on debugging instructions while writing features.
How It Works
The pattern looks like this:
Interact with the running system. Use CLI tools, curl endpoints, or whatever interfaces expose application state. The model already knows these tools, and you’re just pointing it at your infrastructure.
Query production logs. Your cloud provider has a CLI: gcloud, aws logs, kubectl. The model knows the syntax, and the skill supplies the schema: which fields to filter, which identifiers correlate, how to structure the query.
Cross-reference with code. When logs point to a file or function, the model reads the source and maps stack traces to implementations.
Delegate verbose data. When log volume threatens context, the skill instructs the model to spawn a subagent. The subagent retrieves and summarizes. The main context receives only the signal.
What the model brings: fluency with every tool in your stack.
What the skill provides: domain-specific overlays, delegation patterns, institutional memory.
The Takeaway
Most teams using LLMs for debugging treat the model as a generic assistant that paste output and get suggestions. This works until it doesn’t.
The introduction of skills allows us to change this framing. Instead of hoping the model figures out your infrastructure during every debugging session, you encode that knowledge once. The skill captures what you’d otherwise have to re-explain every time an incident occurs: which log fields matter, which identifiers correlate, when to delegate, which methodology to apply.
The model brings the fluency while the skill brings the domain. Together, they handle production debugging without burning context or guessing at incomplete information.
What makes this pattern non-obvious is that the skill isn’t teaching capability. The model already knows gcloud, jq, curl. The skill points that capability at your specific problem — leveraging pre-trained intuition at your infrastructure.
This article was originally published on March 30, 2026 on Medium.