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Build reliable agents

Skill Victoriakaey/build-reliable-agents

15 engineering skills for building reliable LLM agents in Claude Code. Extracted from production failure modes — not theoretical best practices.

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A collection of engineering skills for building reliable LLM-based systems and full-stack AI applications. Use when starting a new AI project, designing an agent pipeline, writing or diagnosing prompts, exploring solutions to a technical problem, making prompt changes, debugging non-deterministic behavior, designing a database, selecting a model, deploying to production, reviewing code, or connecting an agent to a full-stack application.

SKILL.md

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build-reliable-agents

Overview

This skill collection captures patterns battle-tested in production LLM system development. Each sub-skill is a self-contained process for one recurring problem. They can be used independently or in combination.

When to Use Which Sub-Skill

SituationSub-Skill
Have an idea but don't know where to startai-system-design
Facing a problem with multiple possible solutionsproblem-exploration
Previous approach failed, need to reconsiderproblem-exploration
Deciding agent architecture (single vs multi, tool-use vs nodes)agent-architecture
Writing a new prompt from scratchprompt-design (Mode 1)
Existing prompt producing unstable or wrong outputsprompt-design (Mode 2)
Starting any implementation taskexperiment-driven-development
About to change a promptprompt-change-management
Need to compare two system versionsregression-testing
Designing a Critic, Judge, or Evaluator componentcritic-judge-design
Connecting agent to a web app, API, or third-party platformagent-integration
Designing or improving agent tools, observations, and evaluation signalsharness-design
Agent picks wrong tool, ignores data, or loops without progressharness-design (diagnosis ladder)
Designing a database schemadatabase-design
Choosing a model or deciding API vs localmodel-selection
Designing how an agent remembers informationmemory-system
Deploying to production or setting up CI/CDdevops
Reviewing code — Claude reviews or guided self-reviewcode-review
Starting a new LLM project from scratchai-system-design → agent-architecture → model-selection → database-design → experiment-driven-development
System behaving inconsistently across runsregression-testing → prompt-design (Mode 2)
Critic/Judge producing unstable judgmentscritic-judge-design → prompt-design (Mode 2)
Agent works locally but fails when integratedagent-integration → devops
Agent misbehaves — wrong tools, missed data, silent failuresharness-design → experiment-driven-development

Core Principle Across All Sub-Skills

LLM behavior is non-deterministic. Treat every change as an experiment, not a fix.

This means:

  • Every change has a before/after measurement
  • Regressions are expected and must be detected early
  • Prompt changes have blast radius beyond the component they touch
  • Deterministic components (validators, routers, formatters) are always preferable to LLM components when both can do the job

Sub-Skills

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.