agent-ready-codebase

casper-studios/casper-marketplace · updated Apr 8, 2026

$npx skills add https://github.com/casper-studios/casper-marketplace --skill agent-ready-codebase
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summary

When agents struggle with a codebase, they are reflecting and amplifying the codebase's existing weaknesses. This skill evaluates codebases against five principles that determine agent effectiveness, and provides concrete guidance to improve each one. It adapts to the project's language and stack.

skill.md

Agent-Ready Codebase

Overview

When agents struggle with a codebase, they are reflecting and amplifying the codebase's existing weaknesses. This skill evaluates codebases against five principles that determine agent effectiveness, and provides concrete guidance to improve each one. It adapts to the project's language and stack.

Based on "AI Is Forcing Us To Write Good Code".

Mode Selection

Determine which mode to operate in based on context:

  • Audit: The user has an existing codebase and wants to know where it stands. Evaluate all five principles and produce a scorecard with specific findings.
  • Guide: The user wants to improve a specific principle or set up a new project. Provide targeted, actionable steps for their stack.

If the mode is unclear, ask.

The Five Principles

  1. 100% Test Coverage -- Force every line of code to demonstrate its behavior with an executable example
  2. Thoughtful File Structure -- Make the filesystem a navigable interface for agents
  3. End-to-End Types -- Eliminate illegal states and shrink the agent's search space
  4. Fast, Ephemeral, Concurrent Dev Environments -- Keep feedback loops short and enable parallel agent workflows
  5. Automated Enforcement -- Remove degrees of freedom from the agent via linters, formatters, and hooks

Audit Workflow

To audit a codebase, work through these steps:

1. Detect the Stack

Identify the primary language, test framework, build system, and database by examining project files (e.g. package.json, go.mod, Gemfile, pyproject.toml, Cargo.toml). This determines which tooling recommendations apply.

2. Evaluate Each Principle

Read references/checklist.md for detailed criteria per principle. For each principle, determine the current state:

  • Test Coverage: Run or inspect coverage tooling. Look for CI enforcement. Report the current percentage and whether uncovered lines are identifiable.
  • File Structure: Sample the directory tree. Measure file sizes. Flag catch-all files (utils, helpers, common). Assess whether filenames communicate domain purpose.
  • Type System: Check for strict mode, semantic type names, API contract schemas, database constraints. Identify any/untyped gaps.
  • Dev Environments: Check for single-command setup, test suite runtime, port/DB isolation, worktree or container support.
  • Automated Enforcement: Check for linter/formatter configs, CI pipelines, git hooks, agent hooks.

3. Produce the Scorecard

Present findings as a table with one row per principle:

Principle Rating Key Finding
Test Coverage Strong / Adequate / Weak e.g. "87% coverage, no CI enforcement"
File Structure Strong / Adequate / Weak e.g. "3 files over 500 lines, 2 catch-all utils files"
Types Strong / Adequate / Weak e.g. "Strict TS, but no API schema generation"
Dev Environments Strong / Adequate / Weak e.g. "Manual 8-step setup, no concurrent support"
Enforcement Strong / Adequate / Weak e.g. "ESLint configured but not in CI"

4. Prioritize Improvements

Rank the weakest principles and suggest concrete next steps for the top 2-3. Each recommendation should reference the project's actual stack and tooling.

Guide Workflow

When guiding improvements to a specific principle:

  1. Read references/checklist.md for the relevant section
  2. Assess current state of that principle in the project
  3. Provide a concrete, ordered list of changes for the project's stack
  4. Where possible, show exact commands or config snippets

Key Insight: Why 100% Coverage

The most counterintuitive principle deserves emphasis. At 100% line coverage:

  • There is a phase change: uncovered lines are always from recent changes, removing all ambiguity about what needs testing
  • The coverage report becomes a simple todo list of tests still needed
  • It is not about proving "no bugs" -- it forces the author to demonstrate how every line behaves
  • Unreachable code surfaces immediately and gets deleted
  • Code reviews become easier because reviewers see concrete behavior examples
  • Once achieved, 100% is remarkably easy to maintain -- the coverage report enumerates exactly what lines need testing

Resources

references/

  • checklist.md -- Detailed evaluation criteria for each of the five principles, including stack-specific tooling, key indicators (Strong/Adequate/Weak), and guidance. Load this file when performing an audit or providing detailed guidance on any principle.

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.630 reviews
  • Pratham Ware· Dec 12, 2024

    agent-ready-codebase is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Daniel Bansal· Dec 4, 2024

    agent-ready-codebase fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Daniel Srinivasan· Nov 23, 2024

    We added agent-ready-codebase from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hassan Haddad· Oct 14, 2024

    agent-ready-codebase reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yash Thakker· Sep 9, 2024

    agent-ready-codebase fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aisha Perez· Sep 5, 2024

    Registry listing for agent-ready-codebase matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Dev Bhatia· Sep 1, 2024

    agent-ready-codebase fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Aug 28, 2024

    agent-ready-codebase has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Hassan Sharma· Aug 24, 2024

    Useful defaults in agent-ready-codebase — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • James Jain· Aug 20, 2024

    agent-ready-codebase has been reliable in day-to-day use. Documentation quality is above average for community skills.

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