Inspectcat
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AI-Ready Repositories

Make repository context usable for coding agents

Inspectcat gives engineering teams a concrete readiness report before they ask AI tools to make changes in unfamiliar codebases.

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100

point readiness score

12

default checks

2

scan modes

Repository scan

github.com/acme/app

README context quality

Ready

AGENTS.md operating guide

Blocked

Typecheck command

Blocked

Architecture map

Needs review

WebMCP safety gate

Ready

Readiness score: 72 / 100

Try a repository

Start with a real repository URL

Paste a public GitHub repository URL to run the same readiness scan described on this page: repository context, AGENTS.md guidance, validation commands, architecture signals, and WebMCP safety checks.

Public repositories are available here. Create an organization to scan private repositories from a workspace.

Operating model

From repository scan to maintained AI context

Inspectcat turns raw repository checks into a practical loop for improving agent readiness over time.

1

Scan the repository layer

Start from the codebases where AI work succeeds or fails: setup context, validation commands, instructions, and architecture signals.

2

Roll findings into team visibility

Use readiness scores, failed checks, and rule settings to understand the blockers teams need to resolve first.

3

Turn gaps into maintained context

Use repeated scan findings to prioritize docs, operating guidance, and validation loops that make AI-assisted work more reliable.

Why it matters

Agents fail when the repository does not explain itself

Most failures are not model failures. They are missing commands, stale docs, unclear ownership, and hidden validation loops.

No stable validation loop

Without obvious test and typecheck commands, agents cannot verify their own work before handing it back.

Missing AI-facing instructions

README files help humans, but agents also need operating guidance, safety constraints, and repository-specific conventions.

Architecture is implicit

When boundaries live only in engineers' heads, AI tools spend context budget rediscovering the system.

Capabilities

Built around the signals Inspectcat can actually observe

Scan the files agents actually rely on

Inspectcat checks README context, AGENTS.md guidance, Cursor/Copilot instructions, package scripts, and architecture docs.

  • README purpose, setup, validation, and architecture signals
  • AGENTS.md operating guidance and safety constraints
  • Test and typecheck command discovery

Prioritize fixes by readiness impact

Findings are grouped by severity and importance so teams can fix high-leverage gaps first.

  • Readiness score with passed, failed, skipped, and locked checks
  • Critical, medium, and minor importance buckets
  • Organization scans can include semantic checks

Prepare web apps for AI-native surfaces

For web repositories, Inspectcat also looks for WebMCP tool registration, input schemas, descriptions, and safety gates.

  • Web surface detection
  • Tool registration and input schema checks
  • Safety hints for state-changing tools

Next step

Start with the repositories your team edits every week

Use Inspectcat to identify the practical readiness gaps that keep AI-assisted development shallow.

Start from DashboardView Platform

Usage, cost, and projection

AI Adoption Analytics

Bring OpenAI, Anthropic, and GitHub Copilot usage into one view for active users, service accounts, model mix, token volume, spend, and forecasted cost.

Generated documentation sites

Living Technical Docs

Define the docs you need, map the repositories behind the system, and generate maintained technical context for people and agents.