claude-automation-recommender skill

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

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:

read, why each is needed, and the token-budget estimate. Example phrasing:

1. Announce the scan plan in one short paragraph: what dirs/files you will

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）。

the chat UI surfaces this as a confirmation button:

1. Ask explicit confirmation with a clear yes/no question — in Cherry Studio

继续扫描吗？ / Proceed with scan?

Treat anything ambiguous as a no.

1. Wait for explicit "yes" / "继续" / "go ahead" before proceeding to Phase 1.

1. If the user declines or hesitates, offer alternatives:

(project type, frameworks, pain points)

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

it up without re-asking

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

permission earlier in the same session (e.g. their first message was "scan my repo and recommend automations now").

1. Skip Phase 0 only if the user has already explicitly granted scan

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:

Phase 2: Generate Recommendations

Based on analysis, generate recommendations across all categories:

A. MCP Server Recommendations

See references/mcp-servers.md for detailed patterns.

B. Skills Recommendations

See references/skills-reference.md for details.

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

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

C. Hooks Recommendations

See references/hooks-patterns.md for configurations.

D. Subagent Recommendations

See references/subagent-templates.md for templates.

E. Plugin Recommendations

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

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)

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)