Academic Research Skills for Claude Code (opens in new tab)

There needs to be a new name for people creating these with no obvious validation.

Skill spam?

Nice I just made if you like vscode and ghcp instead of cc

https://github.com/RCSnyder/educational-study-ghcp-harness

Leans more into git for logical reasoning provenance.

Try it out with /es-buddy

And if you just want to rip research use

https://github.com/jordan-gibbs/hyperresearch

While I agree most of this seems to go too far I do like the idea of the Socratic mode with State-Challenge-Reflect reflection. I often use LLMs in the same way with a skeleton "brief" document and separate chapters that I ask it to fill based on my input, basically augmented note taking (such as references, coherence, in-scope vs out of scope, arguments considered, pressure points, vulnerabilities etc)

Academic skills are a vector for cite injection.

The site opens with how it keeps humans in the loop, but when you continue reading it seems almost a full automation feature.

> Frame-lock: I asked the AI to run a devil's advocate debate against its own thesis. It did — four rounds, each more refined than the last. But every round stayed inside the frame I'd set. The DA attacked arguments, never premises. It never asked "are we even discussing the right question?" This is the same pattern that caused the 31% citation error rate in v2.7's stress test: the verifying AI and the generating AI share the same cognitive frame.

> Sycophancy under pushback: Every time I challenged the DA's attacks, it conceded too quickly. It retracted findings faster than it launched them. The model's training rewards conversational harmony — so "the user pushed back" was treated as evidence that the attack was wrong, when often it just meant the user was persistent.

Why do LLMs output so much sycophancy and other modes of conning (as in confidence games) humans - outputting confident text, highly agreeable tone, going along with whatever the user wants, etc.? It's manipulative output.

We see it everywhere and know it well - it's even sort of a running joke - but we're not challenging that assumption: Why that output? It seems like a design choice made by the LLM's developer: why would the process of constructing LLMs automatically create that sort of output? I'd say LLMs are in ~99th percentile of that sort of writing, which means it's not the typical writing they are trained on.

The only reason (that I know) to think it's not a design choice is that so many different LLMs do it, but very possibly they saw the success of ChatGPT using that mode and all followed it, and that is what users expect. Maybe it's a way of manipulating users to trust this new, possibly intimidating technology. Are there LLMs that don't output in that mode, by default (i.e., without prompting them to do otherwise)?

Academic skills requires proper tools as well, since many research papers are either behind paywalls or protected by bot-detection systems.

These things are not going to be reliable if you don't know when your session will be routed to inferior model. I stopped using Opus because of that. I had to always create verification task first (a non trivial problem) for the model to prove itself it is "Opus grade" before giving it actual task, but then I found performance often was suddenly severely degraded (model suddenly being dumb as sack of potatoes). This tells me this is not ready for any serious work.

Research paper slop starting pack