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Instructions for Coding Agents: AGENTS.md
AGENTS.md is a file that complements the traditional README.md file in a project's repository. It provides detailed context and instructions that coding agents need, such as build steps, tests, and conventions. It is intentionally kept separate from the README.md to give agents a clear and predictable place for instructions, keep human-focused READMEs concise, and provide precise, agent-focused guidance. The AGENTS.md format is not proprietary and can be used by any coding agent or tool. The AGENTS.md file can include sections such as project overview, build and test commands, code style guidelines, testing instructions, and security considerations. It can also include any other instructions that would be helpful for a new teammate. If the project is a large monorepo, nested AGENTS.md files can be used for subprojects. AGENTS.md is an open format that emerged from collaborative efforts across the AI software development ecosystem. It is committed to benefiting the entire developer community, regardless of which coding agent is used. Coding agents such as OpenAI Codex, Amp, Jules from Google, Cursor, Factory, and many others are compatible with the AGENTS.md format. The AGENTS.md file can be added, updated, and configured for various coding agents as needed.
AI learns, Improves Code Review Process
Kieran Klaassen, general manager of Cora, writes about "compounding engineering," a concept where AI tools learn from every code review, bug fix, and pull request to improve development systems. By teaching tools and capturing decisions, failures are turned into upgrades, and the system becomes smarter. This approach has reduced time-to-ship on features and increased the number of bugs caught before production at Cora. Klaassen shares a five-step playbook for building compounding engineering systems. "Compounding engineering" is a method that transforms AI from an extra pair of hands into an entire team that gets faster, sharper, and more aligned with every task. At Cora, this approach has turned production errors into permanent fixes, extracted architectural decisions from collaborative work sessions, built review agents with different expertise, automated visual documentation, and parallelized feedback resolution. These changes have significantly reduced time-to-ship on features and increased the number of bugs caught before production. Klaassen provides a five-step playbook for implementing compounding engineering: 1. Teach through work: Capture and codify decisions to stop the AI from making the same mistakes again. CLAUDE.md and llms.txt files store preferences and high-level architectural decisions, turning them into permanent system knowledge. 2. Turn failures into upgrades: When something breaks, add tests, update rules, and write evaluations to make the tools smarter and prevent similar issues in the future. 3. Orchestrate in parallel: Utilize AI workers that scale on demand by spinning up specialized agents for specific tasks, optimizing workflows and compute costs. 4. Keep context lean but yours: Ensure context reflects the codebase, patterns, and hard-won lessons. Personalized, concise rules provide better guidance than generic ones. 5. Trust the process, verify output: Build trust in the system by verifying through tests, evals, and spot checks. Teach the system when it's wrong to improve output over time. By adopting compounding engineering, companies can achieve more with AI tools, reduce costs, and improve development systems. Klaassen encourages starting with one experiment log and gradually implementing more aspects of the process to see what compounds over time.
Validate and Run GitHub Actions Locally with wrkflw
The provided content is about a GitHub repository named wrkflw, created by bahdotsh. Wrkflw is a powerful command-line tool that allows users to validate and run GitHub Actions workflows locally, without requiring a full GitHub environment. This tool helps developers test their workflows directly on their machines before pushing changes to GitHub. It features a TUI (terminal user interface) for managing and monitoring workflow executions, validation of workflow files for syntax errors, execution of workflows using Docker or Podman containers, and multiple container runtime support. Wrkflw also offers job dependency resolution, container integration, GitHub context, rootless execution, and action support, including Docker container actions, JavaScript actions, composite actions, and local actions. The key features of wrkflw include: 1. TUI Interface: A full-featured terminal user interface for managing and monitoring workflow executions 2. Validate Workflow Files: Check for syntax errors and common mistakes in GitHub Actions workflow files with proper exit codes for CI/CD integration 3. Execute Workflows Locally: Run workflows directly on your machine using Docker or Podman containers 4. Multiple Container Runtimes: Support for Docker, Podman, and emulation mode for maximum flexibility Wrkflw also provides various requirements, installation methods, and usage examples, along with a roadmap for planned feature implementations in the future.
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