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workspace-surface-audit

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.

workspace-surface-audit 是什麼?

workspace-surface-audit is a Claude Code agent skill that 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.

相容平台Claude CodeCodex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/workspace-surface-audit

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說明文件

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, README.md
    • .mcp.json, .lsp.json, .claude/settings*.json, .codex/*
    • AGENTS.md, CLAUDE.md, install manifests, hook configs
  2. Environment surface
    • .env* files in the active repo and obvious adjacent ECC workspaces
    • Surface only key names such as STRIPE_API_KEY, TWILIO_AUTH_TOKEN, FAL_KEY
  3. Connected tool surface
    • Installed plugins, enabled connectors, MCP servers, LSPs, and app integrations
  4. ECC surface
    • Existing skills, commands, hooks, agents, and install modules that already cover the need

Audit Process

Phase 1: Inventory What Exists

Produce a compact inventory:

  • active harness targets
  • installed plugins and connected apps
  • configured MCP servers
  • configured LSP servers
  • env-backed services implied by key names
  • existing ECC skills already relevant to the workspace

If a surface exists only as a primitive, call that out. Example:

  • "Stripe is available via connected app, but ECC lacks a billing-operator skill"
  • "Google Drive is connected, but there is no ECC-native Google Workspace operator workflow"

Phase 2: Benchmark Against Official and Installed Surfaces

Compare the workspace against:

  • official Claude plugins that overlap with setup, review, docs, design, or workflow quality
  • locally installed plugins in Claude or Codex
  • the user's currently connected app surfaces

Do not just list names. For each comparison, answer:

  1. what they actually do
  2. whether ECC already has parity
  3. whether ECC only has primitives
  4. whether ECC is missing the workflow entirely

Phase 3: Turn Gaps Into ECC Decisions

For every real gap, recommend the correct ECC-native shape:

Gap TypePreferred ECC Shape
Repeatable operator workflowSkill
Automatic enforcement or side-effectHook
Specialized delegated roleAgent
External tool bridgeMCP server or connector
Install/bootstrap guidanceSetup or audit skill

Default to user-facing skills that orchestrate existing tools when the need is operational rather than infrastructural.

Output Format

Return five sections in this order:

  1. Current surface
    • what is already usable right now
  2. Parity
    • where ECC already matches or exceeds the benchmark
  3. Primitive-only gaps
    • tools exist, but ECC lacks a clean operator skill
  4. Missing integrations
    • capability not available yet
  5. Top 3-5 next moves
    • concrete ECC-native additions, ordered by impact

Recommendation Rules

  • Recommend at most 1-2 highest-value ideas per category.
  • Favor skills with obvious user intent and business value:
    • setup audit
    • billing/customer ops
    • issue/program ops
    • Google Workspace ops
    • deployment/ops control
  • If a connector is company-specific, recommend it only when it is genuinely available or clearly useful to the user's workflow.
  • If ECC already has a strong primitive, propose a wrapper skill instead of inventing a brand-new subsystem.

Good Outcomes

  • The user can immediately see what is connected, what is missing, and what ECC should own next.
  • Recommendations are specific enough to implement in the repo without another discovery pass.
  • The final answer is organized around workflows, not API brands.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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