scout_skill

Overview

This skill rapidly inspects a codebase to produce a concise, structured reconnaissance report. It surfaces languages, frameworks, entry points, file and line counts, infrastructure clues, and basic health indicators so you can understand a project fast. Use it as the first step when onboarding or preparing to make changes.

How this skill works

The skill scans the project folder, auto-detecting files and config manifests (e.g., package.json, requirements.txt, pyproject.toml, Dockerfiles, CI configs). It aggregates language and framework signals, counts files and lines, identifies likely entry points and directory structure, and evaluates health indicators like README, tests, CI, and .env exposure. The output is a labeled, human- and machine-friendly report that guides follow-up actions.

When to use it

• First time opening a new repository or branch
• Onboarding a teammate to quickly map the codebase
• Before planning changes or refactors to understand entry points and dependencies
• To generate a high-level project overview for stakeholders
• To check basic project health (tests, CI, licenses, secrets)

Best practices

• Run the scan from the repository root to ensure full coverage
• Use the report to prioritize deeper checks (tests, security, performance) rather than as the only audit
• Combine scout output with targeted skills (docs, quality audit, security) for remediation
• Treat detected .env files or secrets as urgent: remove, rotate, and add to .gitignore
• Keep the repository up to date (README, license, CI) so health indicators remain useful

Example use cases

• Onboard to a Claude Code plugins repo and get a one-page summary of stack and structure
• Pre-PR checklist: confirm entry points, tests, and CI before opening a change
• Triage a forked project: detect missing docs, test coverage, or exposed env files
• Plan migration or upgrade by identifying frameworks, build tools, and infra configs
• Feed the detected stack and entry points into documentation or audit workflows