Streamlining repos for coding agents: small changes with outsized impact
As coding agents move from novelty to daily tool, familiar repository frictions start to surface. A short guide outlines three core objectives for repo design — speeding up iteration, preserving evergreen instructions, and making repositories legible for both humans and agents — and suggests a few practical ways to reach them without wholesale process upheaval.
Three practical tactics
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Push checks earlier: Invest in strong, opinionated linters and robust type checks (even for dynamically typed projects). The goal is to let agents detect and fix straightforward issues during “compile-time” analysis rather than during slow runtime cycles. The tradeoff is more human-facing nitpicks, but agents can resolve many of those automatically.
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Centralize repeatable commands with a justfile: Rather than scattering scripts across README notes or ad-hoc agent docs, a
justfileoffers an interoperable place for both agents and humans to run common tasks. A helpful refinement is to make command outputs economical — for example, redirecting verbose build logs to files to avoid wasting tokens. -
Consolidate project intent in docs/: Keep files like CODE_REVIEW.md, PRD.md, ROADMAP.md, and CAPTAINS_LOG.md under a
docs/folder. These documents steer agents toward consistent project-level decisions and reduce context bloat when referenced from agent instructions. Some frameworks (e.g., spec-kit) attempt to formalize this, but a lightweight, documentation-first approach can be preferable at early stages.
Context and caveats
These practices are presented as pragmatic experiments rather than rigid standards. There’s fragmentation in how agents are given commands and context (MCP, Claude Skills, AGENTS.md/CLAUDE.md), so interoperability is a practical consideration. The underlying posture is iterative: try conventions that speed feedback loops and make intent explicit, then refine based on what actually reduces headaches.
For a concise dive and the original examples, see the full write-up: Optimizing repos for AI
