Skip to content
Open
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 31 additions & 0 deletions documents/patterns/poor-persons-skill.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,31 @@
---
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.
Loading