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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Galaxy-Dawn/claude-scholar --skill code-review-excellencegit clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote 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/code-review-excellence)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/code-review-excellence"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/code-review-excellence/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/code-review-excellence"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/code-review-excellence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.03200 |
| Opus 5.5 | $0.00017 | $0.01280 |
| Sonnet 5.5 | $0.00009 | $0.00640 |
| Haiku 4.5 | $0.00004 | $0.00320 |
Grade A, and why
code-review-excellence scanned grade A with 0 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 522 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Excellence
Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.
When to Use This Skill
- Reviewing pull requests and code changes
- Establishing code review standards for teams
- Mentoring junior developers through reviews
- Conducting architecture reviews
- Creating review checklists and guidelines
- Improving team collaboration
- Reducing code review cycle time
- Maintaining code quality standards
Core Principles
1. The Review Mindset
Goals of Code Review:
- Catch bugs and edge cases
- Ensure code maintainability
- Share knowledge across team
- Enforce coding standards
- Improve design and architecture
- Build team culture
Not the Goals:
- Show off knowledge
- Nitpick formatting (use linters)
- Block progress unnecessarily
- Rewrite to your preference
2. Effective Feedback
Good Feedback is:
- Specific and actionable
- Educational, not judgmental
- Focused on the code, not the person
- Balanced (praise good work too)
- Prioritized (critical vs nice-to-have)
❌ Bad: "This is wrong."
✅ Good: "This could cause a race condition when multiple users
access simultaneously. Consider using a mutex here."
❌ Bad: "Why didn't you use X pattern?"
✅ Good: "Have you considered the Repository pattern? It would
make this easier to test. Here's an example: [link]"
❌ Bad: "Rename this variable."
✅ Good: "[nit] Consider `userCount` instead of `uc` for
clarity. Not blocking if you prefer to keep it."
3. Review Scope
What to Review:
- Logic correctness and edge cases
- Security vulnerabilities
- Performance implications
- Test coverage and quality
- Error handling
- Documentation and comments
- API design and naming
- Architectural fit
What Not to Review Manually:
- Code formatting (use Prettier, Black, etc.)
- Import organization
- Linting violations
- Simple typos
Review Process
Phase 1: Context Gathering (2-3 minutes)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 522 lines · 43 tokens per session scan A a8e048bd3b8c
code-review-excellence is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,917 stars, last pushed 8d ago), licensed MIT. It adds 43 tokens to every session and 3,200 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…
atc-reproducibility
Use when building the reproducibility story for an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — pinning testbed and software environments, providing a turnkey path from the artifact to the headline numbers, and preparing an anonymized-but-runnable review package ahead of the…
workflows:review
Run multi-agent econometric review on estimation code, identification arguments, and research artifacts.
referee2
Systematic audit and review by Referee 2. Two modes — "deck" reviews slide presentations for rhetoric, visual quality, and compile cleanliness; "code" performs cross-language replication and econometric audit of empirical pipelines. Use when reviewing slides, auditing code, or verifying replication.
deep-audit
Deep consistency audit of the entire repository infrastructure. Launches 4 parallel specialist agents to find factual errors, code bugs, count mismatches, and cross-document inconsistencies. Then fixes all issues and loops until clean. Use when: after making broad changes, before releases, or when user says "audit"…
techdebt
Find and fix technical debt including duplicated code, dead code, outdated patterns, and code smells. Run at the end of sessions to clean up.