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1npx skills add CherryHQ/cherry-studio --skillclaude-automation-recommender -a grok -y
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1Install this skill: https://github.com/CherryHQ/cherry-studio/tree/HEAD/resources/builtin-agents/cherry-assistant/.claude/skills/claude-automation-recommender
Download resources/builtin-agents/cherry-assistant/.claude/skills/claude-automation-recommender/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does claude-automation-recommender live?
The publisher keeps it at https://github.com/CherryHQ/cherry-studio.
Which agents can use claude-automation-recommender?
Claude, Cursor, Codex, and Grok Build can read a SKILL.md you add from the publisher's repository. Grok Bot installs it when you send the skill's GitHub folder link in a chat.
From the publisher
SKILL.md
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)
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:
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:
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?
Wait for explicit "yes" / "继续" / "go ahead" before proceeding to Phase 1.
Treat anything ambiguous as a no.
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
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:
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