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 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** - Cl
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