Building verification loops in Claude Code with skills

Summary

In agentic coding sessions with Claude Code, the workflow follows a repeating cycle: a developer requests a change, the model gathers context from the codebase, implements the modification, and then verifies the outcome before returning a response. When verification fails or additional issues surface, the process loops back to gather more context and refine the work.

Claude already performs part of this verification by observing deterministic signals already present in a project, such as type checkers, linters, tests, and runtime errors. Steps that cannot be inferred from these signals remain manual checks that developers perform repeatedly when reviewing new features or changes.

These manual checks can be converted into explicit verification loops. The process begins by writing down the repeated corrections or quality rules applied after each implementation, expressed in plain English as one would document them for a new team member. The resulting instructions are then encoded as skills that Claude can apply autonomously.

The captured rules may include both general best practices and highly specific constraints, such as rejecting any database migration that drops a column without a corresponding backfill step. Once encoded, the skills allow the model to detect deviations, attempt fixes, and iterate without further prompting, freeing the developer to move to other tasks while the agent closes its own feedback loop.

Why it matters

It provides highly actionable insights for AI product teams and builders on how to improve agentic coding workflows using Claude Code. Dutch AI practitioners can leverage these verification loops to increase development efficiency and enforce project-specific quality standards autonomously.