/claude-automation-recommender

name: claude-automation-recommender
description: 'Recommend Claude Code automations (hooks, subagents, skills, plugins, MCP) and review an existing setup.'
allowed-tools: Read, Glob, Grep, Bash

Claude Automation Recommender

Analyze codebase patterns to recommend tailored Claude Code automations across all extensibility options.

**This skill is read-only.** It analyzes the codebase and outputs recommendations. It does NOT create or modify any files. Users implement the recommendations themselves or ask Claude separately to help build them.

Output Guidelines

• **Recommend 1-2 of each type**: Don't overwhelm - surface the top 1-2 most valuable automations per category
• **If user asks for a specific type**: Focus only on that type and provide more options (3-5 recommendations)
• **Go beyond the reference lists**: The reference files contain common patterns, but use web search to find recommendations specific to the codebase's tools, frameworks, and libraries
• **Tell users they can ask for more**: End by noting they can request more recommendations for any specific category

Automation Types Overview

| Type | Best For | |------|----------| | **Hooks** | Automatic actions on tool events (format on save, lint, block edits) | | **Subagents** | Specialized reviewers/analyzers that run in parallel | | **Skills** | Packaged expertise, workflows, and repeatable tasks (invoked by Claude or user via `/skill-name`) | | **Plugins** | Collections of skills that can be installed | | **MCP Servers** | External tool integrations (databases, APIs, browsers, docs) |

Workflow

Phase 0: Confirm Before Scanning（Cherry Studio addition）

This scan reads many files (package.json, source structure, .claude/, framework configs, dependencies) and produces detailed analysis. **It is token-intensive** — a typical run on a medium-sized repo consumes **20–40K tokens** of model context, plus model output for the recommendations themselves.

Before doing **any** filesystem reads or Bash calls, you MUST:

1. **Announce the scan plan** in one short paragraph: what dirs/files you will read, why each is needed, and the token-budget estimate. Example phrasing:

> 我准备扫描当前工作目录的 `package.json`/`pyproject.toml`/`go.mod` 等清单文件 + > `src/` `tests/` 项目结构 + 已有的 `.claude/` 配置 + CLAUDE.md，给出 hook / > subagent / skill / MCP 推荐。预计消耗 **~30K tokens**（实际取决于仓库大小）。

2. **Ask explicit confirmation** with a clear yes/no question — in Cherry Studio the chat UI surfaces this as a confirmation button:

> **继续扫描吗？** / **Proceed with scan?**

3. **Wait for explicit "yes" / "继续" / "go ahead"** before proceeding to Phase 1. Treat anything ambiguous as a no.

4. **If the user declines or hesitates**, offer alternatives:

• **Narrower scope**: scan only one directory the user names → smaller budget
• **Verbal-only**: skip the scan, recommend based on what the user describes

(project type, frameworks, pain points)

• **Defer**: note the request to memory/FACT.md so a future session can pick

it up without re-asking

5. **Skip Phase 0 only if** the user has already explicitly granted scan permission earlier in the same session (e.g. their first message was "scan my repo and recommend automations now").

Only after explicit confirmation, proceed with Phase 1 below.

Phase 1: Codebase Analysis

Gather project context:

# Detect project type and tools
ls -la package.json pyproject.toml Cargo.toml go.mod pom.xml 2>/dev/null
cat package.json 2>/dev/null | head -50

# Check dependencies for MCP server recommendations
cat package.json 2>/dev/null | grep -E '"(react|vue|angular|next|express|fastapi|django|prisma|supabase|stripe)"'

# Check for existing Claude Code config
ls -la .claude/ CLAUDE.md 2>/dev/null

# Analyze project structure
ls -la src/ app/ lib/ tests/ components/ pages/ api/ 2>/dev/null

**Key Indicators to Capture:**

| Category | What to Look For | Informs Recommendations For | |----------|------------------|----------------------------| | Language/Framework | package.json, pyproject.toml, import patterns | Hooks, MCP servers | | Frontend stack | React, Vue, Angular, Next.js | Playwright MCP, frontend skills | | Backend stack | Express, FastAPI, Django | API documentation tools | | Database | Prisma, Supabase, raw SQL | Database MCP servers | | External APIs | Stripe, OpenAI, AWS SDKs | context7 MCP for docs | | Testing | Jest, pytest, Playwright configs | Testing hooks, subagents | | CI/CD | GitHub Actions, CircleCI | GitHub MCP server | | Issue tracking | Linear, Jira references | Issue tracker MCP | | Docs patterns | OpenAPI, JSDoc, docstrings | Documentation skills |

Phase 2: Generate Recommendations

Based on analysis, generate recommendations across all categories:

A. MCP Server Recommendations

See [references/mcp-servers.md](references/mcp-servers.md) for detailed patterns.

| Codebase Signal | Recommended MCP Server | |-----------------|------------------------| | Uses popular libraries (React, Express, etc.) | **context7** - Live documentation lookup | | Frontend with UI testing needs | **Playwright** - Browser automation/testing | | Uses Supabase | **Supabase MCP** - Direct database operations | | PostgreSQL/MySQL database | **Database MCP** - Query and schema tools | | GitHub repository | **GitHub MCP** - Issues, PRs, actions | | Uses Linear for issues | **Linear MCP** - Issue management | | AWS infrastructure | **AWS MCP** - Cloud resource management | | Slack workspace | **Slack MCP** - Team notifications | | Memory/context persistence | **Memory MCP** - Cross-session memory | | Sentry error tracking | **Sentry MCP** - Error investigation | | Docker containers | **Docker MCP** - Container management |

B. Skills Recommendations

See [references/skills-reference.md](references/skills-reference.md) for details.

Create skills in `.claude/skills/<name>/SKILL.md`. Some are also available via plugins:

| Codebase Signal | Skill | Plugin