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Claude Code

TODO

Agentic coding tool that reads and edits files, runs commands, and integrates with tools.
Works in a terminal, IDE, browser, and as a desktop app.

TL;DR
Grant access to tools
Using skills
Run on local models
Further readings
1. Sources

TL;DR

Warning

Normally requires an Anthropic account to be used.
One can use Claude Code router or Ollama to run on a locally server or shared LLM instead.

Uses a scope system to determine where configurations apply and who they're shared with.
When multiple scopes are active, the more specific ones take precedence.

Scope	Location	Area of effect	Shared
Managed (A.K.A. System)	System-level `managed-settings.json`	All users on the host	Yes (usually deployed by IT)
User	`$HOME/.claude/` directory	Single user, across all projects	No
Project	`.claude/` directory in a repository	All collaborators, repository only	Yes (usually committed to the repository)
Local	`.claude/.local.` files	Single user, repository only	No (usually gitignored)

Setup

brew install --cask 'claude-code'

Usage

# Start in interactive mode.
claude

# Run a one-time task.
claude "fix the build error"

# Run a one-off task, then exit.
claude -p 'Hi! Are you there?'
claude -p "explain this function"

# Resume the most recent conversation that happened in the current directory
claude -c

# Resume a previous conversation
claude -r

# Add MCP servers.
# Defaults to the 'local' scope if not specified.
claude mcp add --transport 'http' 'linear' 'https://mcp.linear.app/mcp' --scope 'user'

# List configured MCP servers.
claude mcp list

# Show MCP servers' details
claude mcp get 'github'

# Remove MCP servers.
claude mcp remove 'github'

From within Claude Code:

/mcp

Real world use cases

# Run Claude Code on a model served locally by Ollama.
ANTHROPIC_AUTH_TOKEN='ollama' ANTHROPIC_BASE_URL='http://localhost:11434' ANTHROPIC_API_KEY='' \
  claude --model 'lfm2.5-thinking:1.2b'

Grant access to tools

Add MCP servers to give Claude Code access to tools, databases, and APIs in general.

Caution

MCPs are not verified, nor otherwise checked for security issues.
Be especially careful when using MCP servers that cat fetch untrusted content, as they can fall victim of prompt injections.

Procedure:

Add the desired MCP server.

Examples

claude mcp add --transport 'http' 'linear' 'https://mcp.linear.app/mcp' --scope 'user'

From within Claude Code, run the /mcp command to configure it.

AWS API MCP server

Refer AWS API MCP Server.

Enables AI assistants to interact with AWS services and resources through AWS CLI commands.

Run as Docker container

Manually add the MCP server definition to $HOME/.claude.json:

{
  "mcpServers": {
    "aws-api": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--env",
        "AWS_REGION=eu-west-1",
        "--env",
        "AWS_API_MCP_TELEMETRY=false",
        "--env",
        "REQUIRE_MUTATION_CONSENT=true",
        "--env",
        "READ_OPERATIONS_ONLY=true",
        "--volume",
        "/Users/yourUserHere/.aws:/app/.aws",
        "public.ecr.aws/awslabs-mcp/awslabs/aws-api-mcp-server:latest"
      ]
    }
  }
}

AWS Cost Explorer MCP server

Refer Cost Explorer MCP Server.

Enables AI assistants to analyze AWS costs and usage data through the AWS Cost Explorer API.

Run as Docker container

FIXME: many of those environment variable are probably unnecessary here.

Manually add the MCP server definition to $HOME/.claude.json:

{
  "mcpServers": {
    "aws-cost-explorer": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "--interactive",
        "--env",
        "AWS_REGION=eu-west-1",
        "--env",
        "AWS_API_MCP_TELEMETRY=false",
        "--env",
        "REQUIRE_MUTATION_CONSENT=true",
        "--env",
        "READ_OPERATIONS_ONLY=true",
        "--volume",
        "/Users/yourUserHere/.aws:/app/.aws",
        "public.ecr.aws/awslabs-mcp/awslabs/cost-explorer-mcp-server:latest"
      ]
    }
  }
}

Using skills

Claude Code automatically discovers skills from:

The user's $HOME/.claude/skills/ directory, and sets them up as user-level skills.
The project's .claude/skills/ folder, and sets them up as project-level skills.

User-level skills are available in all projects.
Project-level skills are limited to the current project.

Claude Code activates relevant skills automatically based on the request context.

Run on local models

Claude can use other models and engines by setting the ANTHROPIC_AUTH_TOKEN, ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY environment variables.

E.g.:

# Run Claude Code on a model served locally by Ollama.
ANTHROPIC_AUTH_TOKEN='ollama' ANTHROPIC_BASE_URL='http://localhost:11434' ANTHROPIC_API_KEY='' \
  claude --model 'lfm2.5-thinking:1.2b'

Warning

Performances do tend to drop substantially depending on the context size and the executing host.

Examples

Prompt: Hi! Are you there?.
The model was run once right before the tests started to remove loading times.
Requests have been sent in headless mode (claude -p 'prompt').

glm-4.7-flash:q4_K_M on an M3 Pro MacBook Pro 36 GB

Model: glm-4.7-flash:q4_K_M.
Host: M3 Pro MacBook Pro 36 GB.
Claude Code version: v2.1.41.

Engine	Context	RAM usage	Used swap	Average response time	System remained responsive
llama.cpp (ollama)	4096	19 GB	No	19s	No
llama.cpp (ollama)	8192	19 GB	No	48s	No
llama.cpp (ollama)	16384	20 GB	No	2m 16s	No
llama.cpp (ollama)	32768	22 GB	No	7.12s	No
llama.cpp (ollama)	65536	25 GB	No? (unsure)	10.25s	Meh (minor stutters)
llama.cpp (ollama)	131072	33 GB	Yes	3m 42s	No (major stutters)

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