designxdevelop/agent-skills/agent-native-audit

Agent-Native Codebase Audit

Goal

Produce an evidence-backed assessment of the conditions that let agents make safe, well-targeted changes, then recommend the highest-leverage improvements.

When to Use

Use when the user asks to assess agent readiness, score an AI-friendly codebase, or plan changes that make a repository easier for coding agents to work in.

Evaluation Criteria

Assess the dimensions that matter for the repository at hand:

Use scores only when they help the user compare baselines or measure progress. If scoring, state the scale, weighting, uncertainty, and evidence behind each rating. Letter grades and fixed score bands are optional presentation choices, not a claim of universal precision.

Workflow

1. Inspect repository instructions, package and build configuration, CI,

verification commands, documentation, and representative source and test paths. Establish the primary languages and the work an agent would perform.

1. Gather evidence for each relevant criterion. Prefer concrete paths, command

output, and examples over inferred maturity.

1. Run safe local checks when they can confirm a meaningful claim. Distinguish

pre-existing failures from the conditions being assessed.

1. Report strengths, friction, and the evidence for each assessment. Identify

the smallest changes likely to improve multiple criteria.

1. Provide an ordered, executable improvement plan when requested; otherwise

offer it as the next step.

Guardrails

• Never fabricate metrics, coverage, test results, or scores.
• Never modify source or repair failures during an audit unless the user expands

the task.

• Never judge a repository from a single file or tool configuration.
• Adapt the assessment to the repository's languages, architecture, and agent

environments rather than assuming one IDE or vendor.

Completion Checklist

• [ ] Findings cite concrete evidence from the repository or checks.
• [ ] Relevant agent-work criteria were assessed with uncertainty stated.
• [ ] Recommendations are prioritized by expected leverage and are actionable.
• [ ] Any score or grade is explained as a chosen reporting convention.