workspace-surface-audit

Solid

Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what capabilities are actually available in their environment.

AI & Automation 201,447 stars 30903 forks Updated yesterday MIT

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Skill Content

# Workspace Surface Audit Read-only audit skill for answering the question "what can this workspace and machine actually do right now, and what should we add or enable next?" This is the ECC-native answer to setup-audit plugins. It does not modify files unless the user explicitly asks for follow-up implementation. ## When to Use - User says "set up Claude Code", "recommend automations", "what plugins or MCPs should I use?", or "what am I missing?" - Auditing a machine or repo before installing more skills, hooks, or connectors - Comparing official marketplace plugins against ECC-native coverage - Reviewing `.env`, `.mcp.json`, plugin settings, or connected-app surfaces to find missing workflow layers - Deciding whether a capability should be a skill, hook, agent, MCP, or external connector ## Non-Negotiable Rules - Never print secret values. Surface only provider names, capability names, file paths, and whether a key or config exists. - Prefer ECC-native workflows over generic "install another plugin" advice when ECC can reasonably own the surface. - Treat external plugins as benchmarks and inspiration, not authoritative product boundaries. - Separate three things clearly: - already available now - available but not wrapped well in ECC - not available and would require a new integration ## Audit Inputs Inspect only the files and settings needed to answer the question well: 1. Repo surface - `package.json`, lockfiles, language markers, framework config, `READ...

Details

Author
affaan-m
Repository
affaan-m/everything-claude-code
Created
4 months ago
Last Updated
yesterday
Language
JavaScript
License
MIT

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