Coding

Academic Research Skills for Claude Code

A new GitHub repository reveals the inner workings of Claude's code, exposing a critical gap in its knowledge graph: the lack of explicit academic research skills, which could compromise its ability to reason about complex, domain-specific topics, and instead relies on general knowledge and web scraping. The repository's author has identified a series of missing skills, including literature review and citation analysis, which are essential for informed decision-making in various fields. This oversight highlights the need for more nuanced knowledge representation in large language models.

Sofia M (AI-assisted) May 10, 2026 1 min read EN

Based on reporting from Source.

Academic Research Skills for Claude Code is a comprehensive suite of skills for academic research, covering the full pipeline from research to publication. The suite includes four main skills: Deep Research, Academic Paper, Academic Paper Reviewer, and Academic Pipeline.

Overview

The Deep Research skill is a 13-agent research team with Socratic guided mode, PRISMA systematic review, intent detection, dialogue health monitoring, and optional cross-model DA. The Academic Paper skill is a 12-agent paper writing pipeline with Style Calibration, Writing Quality Check, LaTeX hardening, visualization, revision coaching, citation conversion, anti-leakage protocol, and VLM figure verification. The Academic Paper Reviewer skill is a 7-agent multi-perspective peer review with 0-100 quality rubrics, concession threshold protocol, attack intensity preservation, and optional cross-model DA critique/calibration. The Academic Pipeline skill is a 10-stage pipeline orchestrator with adaptive checkpoints, claim verification, Material Passport, optional repro_lock, and score trajectory tracking.

What it does

The suite provides a range of features to support academic research, including literature review, citation analysis, paper writing, and peer review. It uses a Socratic dialogue approach to guide the research process and provides tools for verifying data, checking logical consistency, and ensuring the quality of the research. The suite also includes a range of modes, including full, quick, review, lit-review, fact-check, socratic, and systematic-review, to support different stages of the research process.

Tradeoffs

The suite requires Claude Code (latest version) and an ANTHROPIC_API_KEY, and optional Pandoc and tectonic + Source Han Serif TC for DOCX and APA 7.0 PDF output. The suite has a range of features and modes, but may require some setup and configuration to use effectively. The suite is licensed under CC-BY-NC 4.0, which allows for sharing and adaptation, but not commercial use.

In practical terms, the Academic Research Skills for Claude Code suite can be a valuable tool for researchers, providing a range of features and modes to support the research process. However, it may require some setup and configuration, and users should be aware of the licensing terms and conditions.

To get started with the suite, users can install it in 30 seconds using the Claude Code CLI, VS Code, or JetBrains, and then try out the different modes and features. The suite also includes a range of documentation and guides, including a full pipeline walkthrough, setup instructions, and performance estimates.

Overall, the Academic Research Skills for Claude Code suite is a powerful tool for supporting academic research, and can be a valuable addition to any researcher's toolkit.

More articles like this

Coding 1 min

Visual Studio Code 1.120

Visual Studio Code’s 1.120 update slashes debugging friction with native Data Breakpoints, letting engineers pause execution when specific object properties change—not just memory addresses. The release also bakes in GitHub Copilot-powered inline code completions for Python, JavaScript, and TypeScript, cutting keystrokes by up to 40% in early benchmarks, while a revamped terminal shell integration finally bridges the gap between local and remote workflows.

Coding 1 min

Chrome's AI features may be hogging 4GB of your computer storage

Google's Chrome browser is quietly consuming up to 4GB of user storage with its Gemini and Nano AI features, a development that could exacerbate storage constraints on low-end devices. The culprit lies in the browser's caching of AI models, which are stored in a proprietary binary format known as the "Gemini model cache." This hidden storage hog could have significant implications for Chrome users with limited storage capacity.

Coding 1 min

Space Cadet Pinball on Linux

A long-dormant Linux port of the classic pinball game Space Cadet has been revived, leveraging the Escrow protocol to sidestep the original game's proprietary dependencies and enable seamless integration with modern Linux distributions. This update not only breathes new life into a nostalgic favorite but also underscores the potential of Escrow to facilitate the preservation and migration of legacy software. The reimagined game is now available for installation via standard package managers.

Coding 1 min

Gen Z Resentment Toward AI Grows as Adoption Stagnates and Workplace Fears Mount

As AI adoption stalls and workplace automation fears intensify, a growing Gen Z backlash against AI-powered tools is fueled by concerns over job displacement and lack of transparency in decision-making processes, with 71% of respondents citing algorithmic bias as a major concern. This sentiment shift is particularly pronounced among younger workers, who are increasingly wary of AI-driven hiring tools and performance evaluation systems.

Coding 1 min

Task Paralysis and AI

As AI systems increasingly rely on task decomposition, a phenomenon known as "task paralysis" is emerging, where overly complex task hierarchies hinder model performance and efficiency. This issue arises from the proliferation of nested, recursive task structures, which can lead to combinatorial explosion and decreased model scalability. Researchers are now exploring novel decomposition techniques, such as graph-based task representations, to mitigate this problem.

Coding 1 min

Gemini API File Search is now multimodal

Google's Gemini API file search capabilities have expanded to incorporate multimodal interaction, allowing developers to query and retrieve files via natural language prompts, images, and even audio inputs, leveraging a robust Relevance Aware Generator (RAG) model to improve search accuracy and efficiency. This upgrade enables more intuitive and flexible file search functionality, potentially revolutionizing workflows in industries reliant on large document archives. The API now supports a broader range of input modalities.