agentic-readiness-assessment

Agentic Readiness Assessment

Agentic Readiness Assessment is an agent plugin that evaluates a software repository for readiness to be developed and maintained by AI coding agents. It inspects the repository in place and produces an evidence-based readiness scorecard together with actionable recommendations.

Built by Exadel.

Description

The assessment establishes, with evidence, whether an AI coding agent can independently understand this repository, find the right change points, build a reproducible environment, validate its work, and prepare a change for delivery.

It scores 14 areas, weighted, for an applicable maximum of 105 points:

Areas that do not apply to the repository's archetype are marked N/A and subtracted from the maximum, so the normalized score stays comparable across different kinds of repository.

Three gates sit above the score. Gate 1 anchors on setup, Gate 2 on the deliverable, Gate 3 on whichever area holds the archetype's primary verification surface. A gate passes only when its anchor area is fully verified, so a high average can never hide a failed gate.

Evidence, not configuration. A command that was not executed is a claim. The assessment runs safe commands and records each one with its working directory, result and artifacts. A capability is verified only when a check proved it, never because a config file mentions it.

A probe proves the loop. One small reversible edit confirms the repository can actually take a change from edit to green, then it is reverted. Nothing else is modified.

Guardrails have a hard cap. A permission bypass, a blanket allow-all policy, an unguarded secret file or a committed plaintext credential caps area 13 at 2 regardless of what else is in place, and each one becomes its own P0 or P1 fix record. Paths are reported, values never are.

The run produces a status of Ready, Partially ready or Not ready, paired with a verification confidence of High, Medium or Low, so a weak score and a weakly-evidenced score never read the same.

The plugin ships a single Agent Skill, agentic-readiness-assessment. The skill runs entirely locally: it reads the repository in your working directory, requires no backend or MCP server, and sends no repository contents anywhere.

Supported clients

This is a skill written in the open Agent Skills format. It includes native manifests for Cursor-compatible Agent Plugins, Codex, and Claude Code:

• plugin.json for the portable Agent Plugins format used by Cursor;
• .codex-plugin/plugin.json for ChatGPT and Codex;
• .claude-plugin/plugin.json for Claude Code.

The three packages point to the same skill and require no backend or MCP server.

Clients that read the Agent Skills format directly, including Gemini CLI, Antigravity, Zed, Warp, Windsurf and Amp, load the skill from skills/agentic-readiness-assessment/ with no conversion. A gemini-extension.json manifest is present for Gemini CLI's extension installer.

Installation

Cursor

Once published, install it from the Cursor marketplace.

Claude Code

Add this repository as a marketplace and install the plugin:

claude plugin marketplace add exadel-inc/agentic-readiness-assessment
claude plugin install agentic-readiness-assessment@exadel-agent-plugins

To try it without adding a marketplace, clone the repository and load it directly with claude --plugin-dir ..

ChatGPT and Codex

Public installation will be available after OpenAI review and publication. The native Codex package is already present in the repository for submission and local packaging.

Gemini CLI

gemini extensions install https://github.com/exadel-inc/agentic-readiness-assessment

Or install the skill on its own, without the extension manifest:

gemini skills install https://github.com/exadel-inc/agentic-readiness-assessment --path skills/agentic-readiness-assessment

GitHub Copilot CLI

copilot plugin marketplace add exadel-inc/agentic-readiness-assessment

JetBrains Junie

Open /extensions, go to the Marketplaces tab, choose Add marketplace and paste the repository URL. Junie reads the .claude-plugin/marketplace.json in this repository.

Any client that reads Agent Skills

npx skills add exadel-inc/agentic-readiness-assessment

For Zed, Warp, Windsurf, Amp and anything else that discovers skills on the filesystem, copy skills/agentic-readiness-assessment/ into the client's skills directory, usually .agents/skills/ in the repository you want to assess.

From source

Clone the repository and point a compatible client at the repository root:

git clone https://github.com/exadel-inc/agentic-readiness-assessment.git

Getting Started

Ask your agent for an assessment from inside the repository you want to evaluate:

• "Run an agentic readiness assessment on this repository."
• "Generate an AI-readiness scorecard for this codebase."
• "How ready is this repo for AI coding agents, and what should we fix first?"

There are no arguments or configuration. The skill reads the repository in your current working directory.

It writes exactly one file: reports/agentic-readiness.md, creating reports/ if it does not exist. That single document holds both the decision content and the supporting evidence, in thirteen fixed sections:

1. Verdict
2. Run and Scope
3. Glossary
4. Readiness Scorecard
5. Mandatory Gates
6. What to Fix
7. What Can Be Delegated Today
8. What Is Already Good
9. Surface Resolution
10. Golden Path
11. Probe
12. Commands Executed
13. Confidence and Limits

Sections 1 to 8 are the decision document, ordered so a reader has the whole readiness picture before reaching the action list. Sections 9 to 13 are the evidence behind it.

Nothing else in your repository is changed. The one exception is the probe, a single reversible edit that is reverted before the report is written.

Example report

examples/fs-demo-project/agentic-readiness.md is a full run against a Next.js 14 application with Prisma and PostgreSQL. It scored 71 of 100, Partially ready, at Medium confidence. The golden path and the test suite verified cleanly; a port conflict on the audit host blocked the database and, with it, the probe. That is the useful case to read, because the report separates what the repository is missing from what this particular machine could not prove.

More runs, and what was redacted before publishing, in examples/.

System Requirements

• An agent client that reads the Agent Skills format (see Supported clients).
• No additional runtime dependencies. The skill uses prompt-driven analysis only.

Data handling and privacy

• The skill performs read-only analysis of the repository