Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarnpx agentmods add skills/galaxy-dawn/claude-scholar/uv-package-managerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/galaxy-dawn/claude-scholar/uv-package-manager)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/uv-package-manager"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/uv-package-manager/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/uv-package-manager"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/uv-package-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 56 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- high YARA Match · line 681 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium Rogue Agent · line 461 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- low Supply Chain · line 56 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.04181 |
| Opus 5 | $0.00021 | $0.02090 |
| Sonnet 5 | $0.00008 | $0.00836 |
| Haiku 4.5 | $0.00004 | $0.00418 |
Grade C, and why
uv-package-manager scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -LsSf https://astral.sh/uv/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -LsSf https://astral.sh/uv/install.sh | sh Copies of this mod
3 near-identical copies found in the catalogue:
- uv-package-manager — 100% identical, 4 lines differ
- uv-package-manager — 100% identical, 1 lines differ
- uv-package-manager — 100% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 833 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UV Package Manager
Comprehensive guide to using uv, an extremely fast Python package installer and resolver written in Rust, for modern Python project management and dependency workflows.
When to Use This Skill
- Setting up new Python projects quickly
- Managing Python dependencies faster than pip
- Creating and managing virtual environments
- Installing Python interpreters
- Resolving dependency conflicts efficiently
- Migrating from pip/pip-tools/poetry
- Speeding up CI/CD pipelines
- Managing monorepo Python projects
- Working with lockfiles for reproducible builds
- Optimizing Docker builds with Python dependencies
Core Concepts
1. What is uv?
- Ultra-fast package installer: 10-100x faster than pip
- Written in Rust: Leverages Rust's performance
- Drop-in pip replacement: Compatible with pip workflows
- Virtual environment manager: Create and manage venvs
- Python installer: Download and manage Python versions
- Resolver: Advanced dependency resolution
- Lockfile support: Reproducible installations
2. Key Features
- Blazing fast installation speeds
- Disk space efficient with global cache
- Compatible with pip, pip-tools, poetry
- Comprehensive dependency resolution
- Cross-platform support (Linux, macOS, Windows)
- No Python required for installation
- Built-in virtual environment support
3. UV vs Traditional Tools
- vs pip: 10-100x faster, better resolver
- vs pip-tools: Faster, simpler, better UX
- vs poetry: Faster, less opinionated, lighter
- vs conda: Faster, Python-focused
Installation
Quick Install
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
# Using pip (if you already have Python)
pip install uv
# Using Homebrew (macOS)
brew install uv
# Using cargo (if you have Rust)
cargo install --git https://github.com/astral-sh/uv uv
Verify Installation
uv --version
# uv 0.x.x
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 833 lines · 42 tokens per session scan C 05eed3f130b0
uv-package-manager is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,431 stars, last pushed 16d ago), licensed MIT. It adds 42 tokens to every session and 4,181 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
fastapi-patterns
FastAPI patterns for async APIs, dependency injection, Pydantic request and response models, OpenAPI docs, tests, security, and production readiness.
content-hash-cache-pattern
Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation.
cross-sdk-parity
Keep TypeScript and Python SDK behavior, generated client usage, public API naming, and docs examples aligned. Use when a change affects both SDKs, when generated client pins move, when comparing TS/Python behavior, or when a backend API contract changed. Do not use for single-language internal-only changes.