npx claudepluginhub mohamedabdallah-14/unslop --plugin unslopThis skill uses the workspace's default tool permissions.
The regular unslop skill targets prose. Chain-of-thought output has a
Exposes Claude's reasoning as auditable traces with atomic claims, assumption ratings, weakest links, confidence decomposition, and falsification conditions. Triggers on 'reasoning', 'why', 'trace'.
Exposes Claude's reasoning as auditable traces with atomic claims, assumption ratings, weakest links, decision branches, confidence decomposition, and falsification conditions. Use on 'reasoning', 'why', 'trace' queries or /swing-trace.
Facilitates Socratic questioning to surface assumptions, challenge positions, debug mental models, and resolve uncertainty in decisions, designs, or debugging.
Share bugs, ideas, or general feedback.
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.
Six canonical patterns, each with an example and a tighter rewrite.
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.
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.
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.
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.
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.
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.
When the user asks you to clean up a reasoning trace:
/unslop skill. This is for the visible
thinking that precedes the answer.Cat 19 (Agentic Autonomous Thinking) names the missing-catalog gap directly: "there are well-documented blacklists for AI-slop prose (stock phrases, sycophancy, hedging stacks — Cat 01, 16). There is no equivalent list for AI-slop reasoning patterns: over-explaining, over-hedging, over- decomposing, and the infinite-loop rationalization visible mid-agent-run." This skill is the first pass at that catalog. It is a starting point, not a final answer.
Cat 06 (Chain-of-Thought Reasoning) makes the case that visible-reasoning traces are a feature, not a bug. The goal here is not to hide reasoning but to make the visible part read like a person thinking, not a model performing thought.