How to Build Claude Code Verification Loops With Skills

Anthropic’s new verification loops framework allows you to build iterative workflows directly in Claude Code. Instead of writing simple prompts, you can design systems where Claude autonomously gathers context, runs tests, and fixes its own errors before returning a response. This release shifts the focus from prompt engineering to loop engineering.

Using the Native Verification Skill

Claude Code now ships with a native /verify skill. This bundled skill builds your application, runs it, and observes the output for deterministic signals from type checkers, linters, or test suites. If a check fails, Claude automatically catches the error codes and enters a loop to gather more context and attempt a fix without human intervention.

You can invoke this behavior directly in your session. The system natively supports catching warnings from compilers and other environment tools. You can read more about the difference between these patterns in our guide on agent skills vs cursor rules.

Building Custom Verification Loops

When the bundled skills do not fit your workflow, you can encode manual verification steps into custom skills. These are packaged as a folder containing a SKILL.md file with your specific instructions, alongside optional Python code or templates.

Because Claude Code skills follow the open standard for agent skills, they enable cross-tool compatibility. For instance, Rider 2026.2 now natively supports these skills for specialized .NET testing and profiling.

Loop Types

Verification loops generally fall into four categories:

Loop Type	Execution Pattern
Turn-based	Executes a set number of iterations per user turn.
Goal-based	Runs continuously until a specific outcome is validated by deterministic tests.
Time-based	Operates within a specific time window.
Proactive	Triggers automatically based on background observations.

Goal-based loops represent the most robust pattern for code verification. The agent receives an outcome requirement and iterates through fixes until the test suite passes.

Managing Autonomy with Effort Levels and Hooks

Unbounded loops consume significant compute. You can control how much work Claude performs per request using effort levels. Higher effort levels permit more extensive file reading, testing, and self-verification loops before the agent pauses to check in with you. Lower levels force earlier user confirmation.

You can further control loop execution by configuring lifecycle hooks in your settings.json file. Available hooks include SessionStart, PreToolUse, and PostToolUse.

Security and Tradeoffs

Autonomous execution introduces security risks, especially when executing unknown code or utilizing community-built skills. Use tools like NVIDIA’s open-source SkillSpector v2.3.1 to scan and verify the security of your agent skills before integrating them into production environments.

Additionally, deep verification loops increase token consumption. Balance the autonomy of goal-based loops against your API limits by setting strict effort boundaries on standard tasks.

Start by running the built-in /verify skill on your local test suite to observe how the native error catching handles your specific compiler output.