The Complete Beginner's Guide to Making Claude Code Fully Autonomous

Imagine starting a coding task, going to bed, and waking up to find the feature fully implemented, tested, and committed. That is not science fiction. It is what happens when you configure Claude Code for autonomous development.

Anthropic's open-source CLI tool, Claude Code, is more than a chat-in-your-terminal. Under the hood, it contains a sophisticated system of hooks, skills, agents, and self-running loops that can turn a single prompt into hours of unattended, high-quality software engineering work.

This guide breaks down every mechanism available to you, explains how each one works, and shows you how to wire them together. No prior experience with Claude Code internals required.

What Is Claude Code?

Claude Code is Anthropic's official command-line interface for Claude. You install it globally with npm install -g @anthropic-ai/claude-code, run claude in any project directory, and interact with an AI that can read your files, edit your code, run terminal commands, search the web, and manage git workflows through natural language.

Out of the box, Claude Code is interactive. You type a request, it responds, you approve each action. But the tool was designed from the ground up to support something far more powerful: fully autonomous operation where Claude plans, executes, validates, and iterates on its own.

The key to unlocking this is understanding the five building blocks of autonomy:

1. Hooks — Event-driven scripts that run automatically at specific lifecycle moments
2. Skills — Domain-specific knowledge that loads on demand
3. Agents — Autonomous subprocesses that handle complex sub-tasks
4. Self-Running Loops — Mechanisms that keep Claude working across multiple iterations
5. Permission Settings — Controls that let you grant or restrict autonomy safely

Let us walk through each one.

Building Block 1: Hooks — The Nervous System

Hooks are the most important mechanism for autonomous Claude Code. They are shell scripts or Python programs that execute automatically when specific events happen during a Claude session. Think of them as "if this, then that" rules for your AI coding assistant.

What Events Can You Hook Into?

Claude Code exposes the following hook events:

How Hooks Are Configured

Hooks live in a hooks.json file, typically inside your project's .claude/ directory or within a plugin. Here is an example of a PreToolUse hook that watches every file edit for security vulnerabilities:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Edit|Write",
        "hooks": [
          {
            "type": "command",
            "command": "python3 security_check.py",
            "timeout": 10
          }
        ]
      }
    ]
  }
}

The matcher field uses regex to target specific tools. In this case, the hook fires only when Claude tries to edit or write a file. The Python script receives the proposed changes as input and can return a deny response to block the edit, or a proceed response with additional context for Claude.

The Most Important Hook: Stop

The Stop hook is the foundation of autonomous iteration. Normally, when Claude finishes a task, it stops. A Stop hook intercepts that moment and can either allow the exit or feed a new prompt back to Claude, forcing it to continue working.

This is the mechanism that powers self-running loops, which we will cover in detail later.

Building Block 2: Skills — On-Demand Expertise

Skills are markdown files that contain domain-specific knowledge, workflows, and decision-making frameworks. They are not loaded into every conversation. Instead, they activate only when relevant, based on trigger conditions defined in their frontmatter.

This matters for autonomy because it means Claude can dynamically acquire the right knowledge for the task at hand without bloating its context with irrelevant information.

How Skills Work

A skill file lives in .claude/skills/ and looks like this:

---
name: deployment-workflow
description: Use when deploying to production or staging environments
---

## Deployment Checklist

1. Run the full test suite with `npm test`
2. Build the production bundle with `npm run build`
3. Verify no TypeScript errors with `npx tsc --noEmit`
4. Tag the release with semantic versioning
5. Deploy via Cloud Build trigger
6. Verify health check endpoints respond
7. Monitor error rates for 15 minutes post-deploy

When Claude encounters a task related to deployment, it loads this skill and follows the checklist precisely. Without the skill, Claude would use generic knowledge. With it, Claude follows your team's exact process.

What to Put in Skills

• Project conventions: File naming, directory structure, import patterns
• Testing workflows: Which test runner, what coverage thresholds, how to run specific test types
• Deployment processes: Build commands, environment variables, staging vs. production differences
• Code review standards: What your team looks for in PRs, common anti-patterns to avoid
• API integration patterns: How your app talks to external services, auth flows, error handling

Building Block 3: Agents — Parallel Autonomous Workers

An agent in Claude Code is an autonomous subprocess. When Claude encounters a complex task, it can spawn one or more agents to handle sub-tasks in parallel. Each agent has its own context, its own tool access, and its own execution thread.

Why Agents Matter for Autonomy

Consider a pull request review. A human reviewer checks for bugs, verifies test coverage, reads the code comments, validates the types, and looks for security issues. Doing all of this sequentially takes time. With agents, Claude can do all of it simultaneously:

Three agents running in parallel, each focused on a specific review dimension

The pr-review-toolkit plugin, which ships with Claude Code, runs six specialized agents in parallel:

1. Code Reviewer — Checks for bugs, logic errors, and code quality
2. Test Analyzer — Evaluates test coverage and identifies gaps
3. Silent Failure Hunter — Finds swallowed errors and inadequate error handling
4. Type Design Analyzer — Reviews type definitions for encapsulation and invariants
5. Comment Analyzer — Checks code comments for accuracy and maintainability
6. Code Simplifier — Identifies opportunities to reduce complexity

Each agent returns its findings, and the results are merged into a single, comprehensive review. What would take a human reviewer 30-60 minutes happens in under 2 minutes.

Agent Isolation with Worktrees

When multiple agents work on the same codebase, file conflicts are a risk. Claude Code solves this with git worktrees. Each agent can run in isolation: "worktree" mode, which gives it a separate copy of the repository. Changes from each agent are merged back when complete.

This is the same pattern that large engineering teams use for parallel feature development, but applied to AI agents.

Building Block 4: Self-Running Loops — The Ralph Pattern

This is where things get truly autonomous. The ralph-wiggum plugin (named with typical engineer humor) implements a pattern where Claude works in a continuous loop, reading its own previous output and iterating until the task is done.

How the Ralph Loop Works

The self-referential loop: Claude reads its own previous work and keeps iterating

The mechanics are elegantly simple:

1. You start a task with /ralph-loop and describe what you want
2. Claude works on the task: writes code, runs tests, makes commits
3. When Claude tries to stop, the Stop hook intercepts
4. The hook checks if the ralph loop is active (stored in .claude/ralph-loop.local.md)
5. If active, the hook feeds the original prompt back to Claude
6. Claude starts a new iteration, sees the git history from its previous iteration, and picks up where it left off
7. This continues until Claude determines the task is complete or hits a maximum iteration count

What Makes This Work So Well

The secret is self-referential feedback. Each iteration, Claude does not start from scratch. It reads the git diff from its last iteration, sees what it built, identifies what still needs work, and continues. The codebase itself becomes the memory between iterations.

This is fundamentally different from a simple retry loop. Claude is not repeating the same action hoping for a different result. It is building incrementally, like a human developer who checks in their work at the end of each session and picks it up the next morning.

Building Block 5: Permissions — Controlling the Blast Radius

Autonomy without safety controls is reckless. Claude Code provides a granular permission system that lets you decide exactly how much freedom Claude gets.

Three Permission Tiers

Tool Allowlisting in Custom Commands

Beyond global settings, you can restrict individual commands to specific tools. This is done with the allowed-tools frontmatter in command markdown files:

---
allowed-tools: Bash(git checkout:*), Bash(git add:*),
  Bash(git status), Bash(git push:*),
  Bash(git commit:*), Bash(gh pr create:*)
---

Commit all staged changes, push to origin,
and create a pull request.

This command can only use git and GitHub CLI. It cannot read files, edit code, or run arbitrary bash commands. Even in fully autonomous mode, it is constrained to exactly the operations it needs.

Putting It Together: Multi-Agent Feature Development

Now let us see how all five building blocks combine in a real workflow. The feature-dev plugin implements a seven-phase autonomous development cycle:

1. Discovery
   Claude asks clarifying questions about what you want to build. This is the only phase that requires human input.
2. Codebase Exploration
   Two to three code-explorer agents run in parallel, analyzing your existing codebase for patterns, conventions, and relevant code. They report back with architectural context.
3. Clarifying Questions
   Based on the exploration, Claude identifies ambiguities and asks for any final decisions before proceeding.
4. Architecture Design
   Two to three code-architect agents design the implementation approach in parallel, each proposing a different solution with tradeoffs. Claude selects the best approach.
5. Implementation
   After explicit approval, Claude builds the feature. It writes code, creates tests, handles edge cases, and makes commits along the way.
6. Quality Review
   Three code-reviewer agents run in parallel, checking for bugs, code quality issues, and adherence to project conventions. Issues are fixed automatically.
7. Summary
   Claude documents what was built, what decisions were made, and any follow-up items.

Phases 2 through 7 run autonomously. You approve the plan once, and Claude handles the rest. The entire cycle typically takes 10 to 30 minutes depending on feature complexity.

Running Claude Autonomously in CI/CD

Claude Code is not limited to your local terminal. Anthropic provides a GitHub Action that lets Claude run autonomously in your CI/CD pipeline. The Claude Code repository itself uses this extensively:

• Issue Triage: Every new issue is automatically analyzed and labeled by type (bug, feature, question), platform (Linux, macOS, Windows), and lifecycle stage
• Duplicate Detection: When a new issue is opened, Claude searches for similar existing issues and links them
• @claude Mentions: Anyone can tag @claude in an issue or PR comment, and Claude will respond with analysis, code suggestions, or fixes
• Oncall Triage: Every six hours, Claude scans for critical issues with high user engagement and applies an "oncall" label

Here is the basic structure of the GitHub Action workflow:

# .github/workflows/claude.yml
name: Claude Code
on:
  issue_comment:
    types: [created]
  pull_request_review_comment:
    types: [created]

jobs:
  claude:
    if: contains(github.event.comment.body, '@claude')
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: anthropics/claude-code-action@main
        with:
          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
          allowed_tools: "Bash(gh issue view:*),Bash(gh issue edit:*)"

With this in place, Claude responds to every @claude mention in your repository, scoped to exactly the tools you allow. It runs on GitHub's infrastructure, not your local machine.

The Plugin Ecosystem: Pre-Built Autonomy

You do not need to build everything from scratch. Claude Code ships with 12 official plugins, many of which add autonomous capabilities directly:

The CLAUDE.md File: Your Autonomy Blueprint

Every project can have a CLAUDE.md file at its root. This file is loaded into every Claude session and acts as the persistent instruction set. For autonomous work, this file is your most important configuration.

A well-structured CLAUDE.md should include:

• Build commands: How to build, test, lint, and deploy your project
• Project structure: Where key files live, what each directory contains
• Coding conventions: Naming, formatting, import ordering, error handling patterns
• Testing requirements: Minimum coverage, test naming, which test framework to use
• Git workflow: Branch naming, commit message format, PR requirements
• Do not touch: Files, directories, or patterns that should never be modified

The more specific your CLAUDE.md is, the more autonomous Claude can be. Ambiguity forces Claude to ask questions. Specificity lets it proceed confidently.

Getting Started: Your First Autonomous Setup

Here is a step-by-step path to autonomous Claude Code, ordered from least to most autonomous:

1. Install Claude Code and write your CLAUDE.md
   Run npm install -g @anthropic-ai/claude-code. Create a CLAUDE.md at your project root with build commands, testing instructions, and coding conventions. This alone will make Claude significantly more effective.
2. Install the security-guidance plugin
   This adds a PreToolUse hook that monitors every file edit for security vulnerabilities. It is a safety net that runs passively and costs nothing in workflow overhead.
3. Create project-specific skills
   Add markdown files to .claude/skills/ for your deployment process, testing workflow, and any domain-specific patterns. Each skill is loaded only when relevant.
4. Install the feature-dev plugin
   This gives you the /feature-dev command with seven-phase parallel agent development. You approve the plan once, then Claude handles exploration, architecture, implementation, and review autonomously.
5. Install the ralph-wiggum plugin
   This enables /ralph-loop for fully autonomous iteration. Start with small tasks (refactoring, adding tests, documentation) before graduating to full feature development.
6. Set up the GitHub Action
   Add .github/workflows/claude.yml to your repository. Start with @claude mention support for issues and PRs. Then add scheduled triage workflows as you build confidence.
7. Create custom hooks for your workflow
   Use the hookify plugin or write hooks manually. A Stop hook that verifies tests pass before allowing Claude to finish is a good first hook. A SessionStart hook that loads environment context is a good second.

The Philosophy: Hooks for Safety, Skills for Knowledge, Agents for Speed

If there is one takeaway from this guide, it is this formula:

These five mechanisms, working together, transform Claude Code from an interactive assistant into an autonomous development partner. The tool is already built. The plugins are already available. The patterns are already proven.

The only question is how much autonomy you are ready to unlock.