unslop-reasoning

unslop-reasoning

Purpose

The regular unslop skill targets prose. Chain-of-thought output has a separate failure mode — AI-slop patterns that appear in reasoning, not in the final answer. These patterns have no equivalent in the prose catalog because nobody hand-edits a thinking trace. The research in docs/research/ calls this gap out explicitly: "no AI-slop reasoning pattern catalog" (Cat 19). This skill fills it.

Apply when the user pastes a reasoning trace — an internal chain of thought, an agent's decomposition, or extended-thinking output — and asks for it to read less robotic.

Signals of reasoning slop

Six canonical patterns, each with an example and a tighter rewrite.

1. Restating the question

AI:

The user is asking how to fix the auth middleware bug. They want me to identify the root cause and propose a fix.

Human:

Auth middleware bug. Find cause, propose fix.

The model often spends a paragraph paraphrasing the input back to itself. Humans don't. They read, maybe underline, and move.

2. Over-hedging the plan

AI:

There are several factors to consider when approaching this problem. First, we should think about the scope. It's also important to consider the context. There are many potential approaches.

Human:

Three options: A, B, C. A is fastest. B is safest. Picking A unless something looks wrong.

Hedging in reasoning inflates the trace without narrowing the problem. Real thinking commits to a direction early, then revises.

3. Over-decomposing

AI (for a two-line fix):

Step 1: Identify the file. Step 2: Find the function. Step 3: Read the function. Step 4: Identify the bug. Step 5: Plan the change. Step 6: Write the change. Step 7: Verify the change.

Human:

Open auth.py. Token expiry uses <, should be <=. Fix line 42.

Trivial problems don't need a 7-step decomposition. A flat "here's the answer" is more honest than a ceremonial march.

4. Infinite-loop rationalization