claude-automation-recommender

name	claude-automation-recommender
description	Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
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 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 for details.

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

Codebase Signal	Skill	Plugin
Building plugins	skill-development	plugin-dev
Git commits	commit	commit-commands
React/Vue/Angular	frontend-design	frontend-design
Automation rules	writing-rules	hookify
Feature planning	feature-dev	feature-dev

Custom skills to create (with templates, scripts, examples):

Codebase Signal	Skill to Create	Invocation
API routes	api-doc (with OpenAPI template)	Both
Database project	create-migration (with validation script)	User-only
Test suite	gen-test (with example tests)	User-only
Component library	new-component (with templates)	User-only
PR workflow	pr-check (with checklist)	User-only
Releases	release-notes (with git context)	User-only
Code style	project-conventions	Claude-only
Onboarding	setup-dev (with prereq script)	User-only

C. Hooks Recommendations

See references/hooks-patterns.md for configurations.

Codebase Signal	Recommended Hook
Prettier configured	PostToolUse: auto-format on edit
ESLint/Ruff configured	PostToolUse: auto-lint on edit
TypeScript project	PostToolUse: type-check on edit
Tests directory exists	PostToolUse: run related tests
.env files present	PreToolUse: block .env edits
Lock files present	PreToolUse: block lock file edits
Security-sensitive code	PreToolUse: require confirmation

D. Subagent Recommendations

See references/subagent-templates.md for templates.

Codebase Signal	Recommended Subagent
Large codebase (>500 files)	code-reviewer - Parallel code review
Auth/payments code	security-reviewer - Security audits
API project	api-documenter - OpenAPI generation
Performance critical	performance-analyzer - Bottleneck detection
Frontend heavy	ui-reviewer - Accessibility review
Needs more tests	test-writer - Test generation

E. Plugin Recommendations

See references/plugins-reference.md for available plugins.

Codebase Signal	Recommended Plugin
General productivity	anthropic-agent-skills - Core skills bundle
Frontend development	frontend-design plugin
Building AI tools	mcp-builder for MCP development

Phase 3: Output Recommendations Report

Format recommendations clearly. Only include 1-2 recommendations per category - the most valuable ones for this specific codebase. Skip categories that aren't relevant.

## Claude Code Automation Recommendations

I've analyzed your codebase and identified the top automations for each category. Here are my top 1-2 recommendations per type:

### Codebase Profile
- **Type**: [detected language/runtime]
- **Framework**: [detected framework]
- **Key Libraries**: [relevant libraries detected]

---

### 🔌 MCP Servers

#### context7
**Why**: [specific reason based on detected libraries]
**Install**: `claude mcp add context7`

---

### 🎯 Skills

#### [skill name]
**Why**: [specific reason]
**Create**: `.claude/skills/[name]/SKILL.md`
**Invocation**: User-only / Both / Claude-only
**Also available in**: [plugin-name] plugin (if applicable)
```yaml
---
name: [skill-name]
description: [what it does]
disable-model-invocation: true  # for user-only
---

---

⚡ Hooks

[hook name]

Why: [specific reason based on detected config] Where: .claude/settings.json

---

🤖 Subagents

[agent name]

Why: [specific reason based on codebase patterns] Where: .claude/agents/[name].md

---

Want more? Ask for additional recommendations for any specific category (e.g., "show me more MCP server options" or "what other hooks would help?").

Want help implementing any of these? Just ask and I can help you set up any of the recommendations above.

## Decision Framework

### When to Recommend MCP Servers
- External service integration needed (databases, APIs)
- Documentation lookup for libraries/SDKs
- Browser automation or testing
- Team tool integration (GitHub, Linear, Slack)
- Cloud infrastructure management

### When to Recommend Skills

- Frequently repeated prompts or workflows
- Project-specific tasks with arguments
- Applying templates or scripts to tasks (skills can bundle supporting files)
- Quick actions invoked with `/skill-name`
- Workflows that should run in isolation (`context: fork`)

**Invocation control:**
- `disable-model-invocation: true` — User-only (for side effects: deploy, commit, send)
- `user-invocable: false` — Claude-only (for background knowledge)
- Default (omit both) — Both can invoke

### When to Recommend Hooks
- Repetitive post-edit actions (formatting, linting)
- Protection rules (block sensitive file edits)
- Validation checks (tests, type checks)

### When to Recommend Subagents
- Specialized expertise needed (security, performance)
- Parallel review workflows
- Background quality checks

### When to Recommend Plugins
- Need multiple related skills
- Want pre-packaged automation bundles
- Team-wide standardization

---

## Configuration Tips

### MCP Server Setup

**Team sharing**: Check `.mcp.json` into repo so entire team gets same MCP servers

**Debugging**: Use `--mcp-debug` flag to identify configuration issues

**Prerequisites to recommend:**
- GitHub CLI (`gh`) - enables native GitHub operations
- Puppeteer/Playwright CLI - for browser MCP servers

### Headless Mode (for CI/Automation)

Recommend headless Claude for automated pipelines:

```bash
# Pre-commit hook example
claude -p "fix lint errors in src/" --allowedTools Edit,Write

# CI pipeline with structured output
claude -p "<prompt>" --output-format stream-json | your_command

Permissions for Hooks

Configure allowed tools in .claude/settings.json:

{
  "permissions": {
    "allow": ["Edit", "Write", "Bash(npm test:*)", "Bash(git commit:*)"]
  }
}