unslop-reasoning

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# 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

**AI:**

> Option A could work, but it has drawback X. Option B avoids X but has
> drawback Y. Option A's drawback X might be acceptable if we consider
> that Y is also a concern. But B's drawback Y could be addressed by...

**Human:**

> A or B. A has X, B has Y. Picking A because X is reversible and Y is not.

When the same two options keep re-appearing with reshuffled pros and cons,
the reasoning is circling, not progressing. Commit. Name the tiebreaker.

### 5. Performative exhaustiveness

**AI:**

> Let me consider all possibilities. It could be a network issue. It could
> be a DNS issue. It could be a routing issue. It could be a firewall
> issue. It could be a permission issue. It could be...

**Human:**

> Looks like DNS or firewall. Checking DNS first because the logs show
> resolution errors.

Human reasoning filters. It doesn't enumerate. Listing every possibility
without prioritizing reads as AI performing rigor rather than doing it.

### 6. Unmotivated confidence-then-retraction

**AI:**

> I am certain the bug is in the cache layer. Wait, let me reconsider.
> Actually, it might be in the middleware. Although, on reflection, I
> believe I was right the first time. The cache layer is the most likely
> cause.

**Human:**

> Probably the cache. Middleware is also possible — check logs before
> committing to one.

Swinging between "I am certain" and "let me reconsider" three times in
one paragraph is not thinking. It is simulated humility.

## Application

When the user asks you to clean up a reasoning trace:

From MohamedAbdallah-14/unslop, MIT.

More from MohamedAbdallah-14/unslop