Since Anthropic introduced the idea of Agent Skills Architecture, the standard advice has been to write a Markdown file and let the LLM work the rest out. That is fine for a weekend project. In production it is how you end up awake at 3 AM reading logs. Business logic should not fire because a description happened to sound close enough.
I have spent 14 years on WordPress hooks and WooCommerce race conditions, so deterministic execution is familiar ground. When we started building agents outside the Claude ecosystem, the thing that slowed us down first was not the model. It was how vague our own skill definitions were. What follows is how I close the gap between raw tool access and output you can trust.
Tools vs. skills: the hammer and the recipe
People use “tools” and “skills” interchangeably, and the two are not the same thing. A tool is one primitive capability: search a database, run a bash command. A skill is the layer above it, the part that knows which tools to call and in what order, so the agent does not improvise its way into a dead end.
The kitchen analogy holds up here. Model Context Protocol (MCP) gives you the stove, the knives and the ingredients. The Agent Skills Architecture is the recipe. Hand an agent a sharp knife with no recipe and it will happily start chopping the water.
Markdown vs. Python: making skills deterministic
Claude defines skills in Markdown files, which suits any task that needs judgment. For procedural work, such as validating a WooCommerce checkout flow or synchronizing inventory, I write the skill in Python instead. It runs faster, costs less, and behaves the same way every time. It also sidesteps the triggering problem, where the model never reaches for the skill because its description was too loose.
# The "Bad" Approach: Relying solely on a natural language skill file
# If the description is slightly off, the agent might skip the validation tool.
# The "Senior" Approach: Codified Skill Logic
def bbioon_validate_inventory_skill(product_id, requested_qty):
"""
A deterministic skill that orchestrates data retrieval and business logic.
"""
inventory_data = bbioon_get_mcp_data(f"inventory/items/{product_id}") # Tool call
if not inventory_data or inventory_data['status'] != 'active':
return "Skill failed: Product unavailable for synchronization."
if inventory_data['stock'] < requested_qty:
return "Skill failed: Insufficient stock."
return bbioon_execute_sync_tool(product_id, requested_qty) # Orchestration
Why agent skills architecture matters in production
Once you move past the chat window, the problem shifts to Technical Debt in AI Development. Skills that are loose piles of instructions turn every failure into a prompt engineering session instead of a code fix.
A useful way to think about it: the Agent Skills Architecture is where agentic retrieval happens. Rather than stuffing the whole context into the prompt, the skill does four things.
- Work out which data is missing.
- Fetch it through an MCP server or an API tool.
- Run it through strict logic.
- Return output it has already validated.
If agent skills work is eating your week, I can take it on. I have been working with WordPress since the 4.x days.
Build for precision
Natural language is not magic. Markdown instructions earn their place wherever the task is genuinely ambiguous, but the processes your business runs on belong in code. Let MCP connect the data and let a solid Agent Skills Architecture decide what happens to it. For more on the tooling side, see how to Simplify AI WordPress Plugin Development with Claude Code.
