Claude

Claude code basics

Claude Code is Anthropic's AI coding environment and CLI that lets you work with Claude directly from your terminal and editor. My mental model is: install a small CLI, authenticate once, then use it to generate, refactor and run code while keeping my existing tooling.

Key resources:

Common CLI flows from the docs:

# Install the Claude Code CLI (example for macOS with Homebrew)
# See the official docs for the latest installation instructions per platform.

brew install anthropic-ai/anthropic/claude

# Authenticate once in your terminal
claude auth

# Ask Claude to explain a file
claude explain path/to/file.py

# Generate a new file from a short description
claude create path/to/new_script.py --prompt "Script that tails a log file and highlights errors"

# Refactor code in-place using an instruction
claude edit path/to/service.js --prompt "Extract configuration loading into a separate module"

I treat these commands as building blocks: use explain and edit for local refactors, and create when I want Claude to draft starter files that I will review carefully.

Claude skills

Claude skills are reusable capabilities you wrap around the model: each skill has a clear purpose, input/output schema and implementation. Anthropics SDK and hosted Skills product make it easier to turn patterns you use repeatedly into named, tested building blocks.

Key learning resources:

The SKILL.md template breaks a skill into: intent, inputs, outputs, sample calls, guardrails and implementation notes. This forces you to think about contracts instead of just prompts. 

# Example: running a Claude skill locally via the CLI (conceptual)

# List available skills
claude skills list

# Invoke a skill with JSON input
claude skills run summarize-log --input '{"path": "logs/app.log", "time_window": "last_10_min"}'

In practice I pair this with normal software engineering: unit tests around the skill's implementation, plus a few golden prompts/responses to catch regressions.

Plugins and integrations

The same building blocks show up across tools: Claude can call HTTP APIs, run shell commands, use external tools or talk to your own services. Whether you integrate via the Claude Code CLI, the Agent SDK or another orchestrator, the pattern is:

  • Describe tools (name, description, input schema) clearly.
  • Implement each tool as a small, reliable function.
  • Let Claude decide when to call them, but keep strict boundaries.
// Pseudo-code: registering a simple tool with the Claude Agent SDK

const tools = [
  {
    name: "get_incident_summary",
    description: "Look up an incident by ID and return a short summary.",
    input_schema: {
      type: "object",
      properties: { id: { type: "string" } },
      required: ["id"]
    },
    handler: async ({ id }) => {
      const incident = await fetchIncidentFromApi(id);
      return { summary: incident.summary, status: incident.status };
    }
  }
];

I try to keep tools narrow and composable: one tool to fetch data, another to apply a change, and logs/metrics around each call so I can audit how Claude is using them.

Notebook LLM

Notebook LLM is a pattern and emerging tooling where Claude helps you work directly inside computational notebooks: explaining code cells, generating new ones and wiring data workflows together.

Good introductions:

When I think about Notebook LLM, I treat it as "pair programming plus documentation" for data work: use Claude to draft cells, annotate transformations and keep narrative text in sync with the code. 

# Example: conceptual CLI pattern for Notebook LLM

# Start a notebook assistant session in a project directory
claude notebook start

# Ask Claude to generate a new analysis cell
claude notebook suggest --prompt "Load daily metrics from metrics.csv and plot a rolling 7-day average."

SEO and content workflows with Claude

Claude can be a strong assistant for SEO-style work when used carefully: keyword research, outline generation, drafts and on-page checks. I see it as a force multiplier for a human editor rather than an auto-publishing engine.

Helpful overview:

Example CLI-style prompts I would experiment with:

# Generate an outline for a technical SEO article
claude chat --prompt "Act as an SEO strategist. Propose an outline for an article about secure AI agents for enterprise, optimized for 'agentic AI security' and 'LLM threat modeling'."

# Review on-page SEO for an existing Markdown file
claude explain content/blog/agentic-ai-security.md --prompt "Review this post for on-page SEO: headings, internal links and clarity. Suggest concrete edits, but keep my voice."

For anything public-facing, I would always do a careful human review and align with brand and legal guidelines before publishing.