Agent Skills: Stop Writing SOPs, Start Building Boundary Systems

A recent post on r/ClaudeAI argues that the common instinct to fix agent failures by adding more skills, tools, prompts, or exception rules is counterproductive. The author claims this approach makes agents more fragile over time: context grows heavier, tool selection gets messier, and rules start conflicting with each other.

Skills as SOPs vs. Boundary Systems

The core problem, per the author, is that many developers write Skills like Standard Operating Procedures (SOPs):

Step 1: do this
Step 2: do that
If X happens, do Y
If Y happens, do Z
Don't do B unless A, except if C happens

This style works for deterministic workflows but fails for open-ended agent tasks. Instead, the author proposes shifting to a boundary system approach, where a good Skill answers these questions:

• When must this Skill be triggered?
• When should it absolutely not be used?
• What does success mean in business terms?
• What is the smallest toolset needed — with no ambiguity?
• Which facts must be verified through an API or external source?
• Where must the agent stop and ask a human for confirmation?

“We shouldn’t teach the model how to breathe. We should give it a clear map, clean tools, and obvious stop signs.”

Tools: Less Is More

The same principle applies to tool definitions. More tools does not automatically mean more capability. If boundaries between tools are fuzzy, the model burns context and reasoning budget just deciding which to call. The author's rule of thumb:

Minimum complete toolset, maximum boundary clarity.

Evals Over Procedural Correctness

A good Skill should not be judged by whether the agent followed the author's exact steps, but by whether it:

• Picked the right tool
• Passed the right parameters
• Verified the right facts
• Stopped when it was supposed to stop

The author concludes: a bad Skill is an SOP that keeps getting longer; a good Skill is a tested boundary system. They ask the community how others are handling this — whether Skills are kept small and modular or turned into long instruction packs, and how to tell if a Skill is actually improving the agent vs. creating more context debt.