Copy-paste is still the default in AI development. The standard advice is to feed context into every new chat by hand, and it eats the day. You spend half of it perfecting a system prompt, then the session ends and the prompt goes with it. Claude Skills are Anthropic’s answer to that, and they change the math on how you build with LLMs.
If you build custom plugins or maintain complicated WooCommerce setups, you know the routine. Twenty messages explaining your team’s coding standards, your directory structure and your deployment hooks. New chat, square one. It is tedious and it is expensive. With Claude Code and its skills system, those instructions become modular code instead of chat messages that expire.
The token economics of Claude Skills
The bottleneck in AI-assisted development is not the model’s intelligence. It is the context window. Every token you push into it costs money, adds latency and drags the output quality down. I ran the back-of-the-envelope numbers on a standard MCP (Model Context Protocol) setup: with 10-15 tools active (GitHub, Linear, AWS and so on), you are loading roughly 32,000 tokens of metadata into every single message.
At current rates, those idle tool descriptions come to about $160/month of pure overhead for one developer, before the AI has written a line of code. Claude Skills deal with this through lazy-loading. The agent reads only the skill’s name and a short description up front, then pulls in the full instructions when it decides they are relevant to the task in front of it.
Anatomy of a skill
A skill is a markdown file inside a .claude/skills/ directory, nothing more. A WordPress one might look like this:
---
name: bbioon-plugin-standard
description: Use when generating or refactoring WordPress plugins.
Defines directory structure, hook prefixing, and security standards.
---
1. Prefix all functions with `bbioon_`.
2. Follow PSR-12 coding standards.
3. Ensure all DB queries use `$wpdb->prepare()`.
4. Use `wp_safe_remote_get()` for external API calls.
5. Directory structure:
- /includes (Business logic)
- /assets (CSS/JS)
- /templates (UI)This is what keeps “context bloat” from turning into the maintainability disasters in AI-generated code. The agent picks up these rules when it needs to work on a plugin, and the rest of the conversation stays lean and fast.
Subagents: the isolated workers
One level up from skills sits the subagent. The main agent orchestrates, and a subagent is the specialist you bring in for one job. When I tell mine to “wrap up the feature branch and open a PR,” it does not do the work itself. It spins up a GitHub subagent.
That subagent gets its own clean context window. It reads the diffs, checks the team’s PR template and talks to the GitHub API. Once the PR is open, everything it accumulated on the way, all the intermediate reasoning and documentation reading, is discarded. The main agent receives the PR link and nothing else, so the main chat never fills up with git logs and API responses.
Why this matters for WordPress devs
Debugging a race condition in a WooCommerce checkout while holding twenty plugin docs in your head is a familiar kind of misery. Do it in a single chat window and the AI will eventually start inventing function names from the wrong library. Hand the work to specialized subagents instead, one for database optimization and one for frontend performance, and a single failure stays contained.
If Claude Skills are eating your dev hours, I can take that off your plate. I have been wrestling with WordPress since the 4.x days.
The architect’s takeaway
The interesting progress right now is in infrastructure, not in models. Going from Claude 3.5 to 3.7 matters far less than how you manage context. Claude Skills and subagents are what make an AI workflow affordable once a team is leaning on it every day: you write your expertise down once, in files, and the system pulls it in when it applies. That beats retyping it every morning.
