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+---
+authors: [gregor_riegler]
+related_patterns:
+  - semantic-anchors
+  - extract-knowledge
+related_obstacles:
+  - limited-context-window
+  - hallucinations
+---
+
+# Poor Person's Skill
+
+## Problem
+You're about to work on a topic the agent has plenty of training knowledge about, but none of it is active in the current context. And you don't have a skill or knowledge document at hand. Without priming, the agent produces generic output and you only discover the gaps by correcting them mid-task.
+
+## Pattern
+Before giving the real instruction, ask the agent an open question: *"What do you already know about X?"*
+
+The answer itself is secondary. The point is that the agent's summary now sits in the context window, shaping everything that follows. You get most of the value of a dedicated skill file at the cost of one question — and you can see what the agent *thinks* it knows, so you can correct or supplement before the work starts.
+
+Use it when:
+- The topic is well-represented in training data (frameworks, methodologies, known libraries)
+- You don't have a skill or knowledge document at hand
+- You want a sanity check on the agent's baseline understanding before committing to an approach
+
+## Example
+Before asking the agent to break a feature into a first slice:
+
+> What is important for doing vertical slicing?
+
+The agent surfaces the usual concerns: keeping slices end-to-end, avoiding horizontal layers, picking the thinnest valuable path, deferring edge cases. Now when you ask it to propose a first slice, it draws on that surfaced knowledge instead of guessing. If the recall is wrong or thin, you've caught it before it shaped the work.