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Guide coding agents to validate changes with evidence-first execution.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "old-coder" yet — see the docs or source repo.
Evaluate the changes in this repository with an evidence-first approach: do not read through the code first. List the tests, build steps, and key commands to run, execute them, then locate likely issues from the results and suggest the next fixes.
A run-first evaluation flow with execution results, identified issues, and suggested next fixes.
Create a 'run the gauntlet' checklist for this project. Order the commands from fast to slow, such as formatting checks, unit tests, integration tests, and build steps, and explain what pass or fail means at each stage.
A staged validation checklist that helps the agent judge code quality from observable results instead of guesses.
The project recently introduced a regression. Use the old-coder strategy: do not start by reading large parts of the implementation. First design and run minimal reproduction steps, related tests, and log checks, then summarize the most likely root cause from the evidence.
A regression analysis report grounded in reproduction steps, test results, and log evidence.
When asking an AI to handle a code issue, developers can require it to run tests, builds, or other checks first, then decide how to change the code based on results. This skill emphasizes an evidence-first development style.
Teams can organize linting, tests, and builds into a staged gauntlet so agents or contributors pass evidence-based checks before deeper analysis. This is useful for quickly surfacing regressions and failure points.
It provides an evidence-first development strategy for coding agents. The core idea is to run tests, builds, or other validation steps first, then judge the code from the results instead of reading large amounts of code first.
The difference is the priority order: it emphasizes 'don't read the code first—make it run the gauntlet.' In other words, inspect executable evidence first, then decide whether to dive into implementation details.
The provided information does not include installation steps or explicit prerequisites. For runtime details, dependencies, or repository setup, see the source repository.
Use sandboxing and data-driven metrics to reproducibly benchmark AI coding agents.
Delegate coding tasks to background AI coding agents for implementation and changes.
Optimize coding agents with performance, security, memory, and research-first workflows.
Provides verified memory for coding agents with code-backed, stale-safe claims.
Make AI coding agents production-ready with quality and readiness checks.
Gives coding agents a deterministic call graph to reduce breakage and token waste.