7 AI agent patterns to improve your coding workflow

Summary

Coding assistants often struggle when given excessively long prompts. To resolve this problem, developers can equip coding agents with modular skills. Instead of keeping all instructions in the prompt, the agent reads a short description of each skill and dynamically loads the complete skill file only when necessary. Skill files are plain text files stored locally in the project root under dot-agents slash skills, or globally in user configuration directories. Curated collections, such as the open-source Agent Skills repository and Google's official cloud skills repository, organize these capabilities into seven distinct design patterns.

1. 1.Domain knowledge. These skills supply deep expertise in specific subjects, such as frontend interface engineering, performance optimization, and API design, or Google Cloud services like Cloud Run, AlloyDB, BigQuery, and Cloud SQL. The agent loads domain rules dynamically during relevant tasks and drops them when finished, avoiding memory bloat.
2. 2.Tool wrappers. Tool wrappers provide agents with direct access to external developer tools, enabling faster feedback loops. Examples include integrating Chrome DevTools to inspect browser DOM changes, network traffic, and console errors automatically, or wrapping Git commands so the developer can focus on logic rather than version control syntax.
3. 3.Inversion. Inversion skills prevent agents from making assumptions by forcing them to pause and ask the developer clarifying questions. Patterns like context engineering, task planning, and debugging recovery stop the model from generating random code fixes, guiding it instead through structured root cause analysis and log examination. Tools like the grill-me skill in Antigravity prompt users for specific architectural details before proceeding.
4. 4.Generators. Generators structure creative AI output into standardized formats. They include spec-driven development, source-driven development, and documentation frameworks for architecture decision records, ensuring decisions and design templates remain consistent across projects.
5. 5.Reviewers. Reviewers serve as automated quality gates that evaluate generated code against defined checklists before merging or shipping. These skills analyze codebases for code smells, stylistic conventions, and security vulnerabilities through multi-layered hardening checks.
6. 6.Pipelines. Pipelines assemble complex multi-step workflows into sequential stages with strict execution guards. In a test-driven development pipeline, the agent is forced to follow three mandatory steps: first writing a failing unit test, second writing the code to pass the test, and third refactoring the implementation while keeping tests green.
7. 7.Meta skills. Meta skills manage and coordinate other skills. An example is the using-agent-skills router, which identifies current project needs and automatically invokes the appropriate sub-skills, or skills that generate new skills dynamically.

For unique project requirements, such as custom credit card handling policies, developers can author their own skills as plain text files. Step-by-step guidance is available through dedicated codelabs and evaluation whitepapers.