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Audits codebases for AI agent readiness across five pillars—test coverage, file structure, end-to-end types, fast dev environments, automated enforcement—and guides stack-specific improvements.
npx claudepluginhub casper-studios/casper-marketplace --plugin casperHow this skill is triggered — by the user, by Claude, or both
Slash command
/casper:agent-ready-codebaseThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
Assesses codebase for AI agent readiness by detecting stacks, monorepos, git setup, and evaluating style, testing, code quality, secrets, and file sizes.
Runs Agent-Ready Codebase Assessment scoring codebase across 8 dimensions with parallel agents, producing weighted 0-100 score, band rating, and improvement roadmap. Supports Ruby, Python, PHP, TypeScript, JavaScript, Go, Java, Scala, Rust.
Assesses codebase readiness for agents/production across 8 pillars, verifies commands/GitHub settings, reports all issues, auto-fixes agent readiness.
Share bugs, ideas, or general feedback.
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".
Determine which mode to operate in based on context:
If the mode is unclear, ask.
To audit a codebase, work through these steps:
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.
Read references/checklist.md for detailed criteria per principle. For each principle, determine the current state:
utils, helpers, common). Assess whether filenames communicate domain purpose.any/untyped gaps.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" |
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.
When guiding improvements to a specific principle:
references/checklist.md for the relevant sectionThe most counterintuitive principle deserves emphasis. At 100% line coverage:
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.