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phd-skills MCP Server

fcakyon/phd-skills

Score: 57.6 Rank #7394 MCP Server
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PhD Research Skills for Claude Code: paper reproduction, experiment design, paper review, result comparison and more.

Add AgentRank to Claude Code Discover and compare tools like fcakyon/phd-skills — your AI finds the right one automatically
Get API Access →
claude mcp add agentrank -- npx -y agentrank-mcp-server

Overview

fcakyon/phd-skills is a Shell tool licensed under MIT. PhD Research Skills for Claude Code: paper reproduction, experiment design, paper review, result comparison and more. Topics: academic, claude-code, phd, research, agents, ai, claude, hooks, mcp, plugin, skills, conference, journal, paper-reproduction, paper-review, claude-code-plugin, claude-skills, fable, opus, research-skills.

Ranked #7394 out of 24846 indexed tools.

Ecosystem

Shell MIT
academicclaude-codephdresearchagentsaiclaudehooksmcppluginskillsconferencejournalpaper-reproductionpaper-reviewclaude-code-pluginclaude-skillsfableopusresearch-skills

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 418

418 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → recently updated

Issue Health 25% 0%

0/1 issues closed → many open issues

Contributors 10% 2

2 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 36
Description Good
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

Expand your description to 150+ characters for better discoverability

Issue Health high impact

You have 1 open vs 0 closed issues — triaging stale issues improves health

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for fcakyon/phd-skills
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Matched Queries

topic:mcp

From the README

# phd-skills

Catch AI mistakes before they cost weeks of compute. Reproduce papers from arxiv. Debug runs evidence-first. Compare experiments at the right epoch. Launch with discipline.

Built by [Fatih Cagatay Akyon](https://scholar.google.com/citations?user=RHGyDE0AAAAJ)
(2000+ citations, 5 patents) after 300+ Claude Code sessions, tens of
critical AI mistakes caught the hard way, and thousands of hours of
PhD research. Every guardrail in this plugin traces to a real mistake.

---

## Why This Plugin Exists

Claude Code is powerful, but it makes research-specific mistakes that cost weeks of compute:

- It typed "done?" as "dont?" and launched an unwanted upload of thousands of files
- It analyzed my full dataset when I asked for a specific 4k/2k/2k split
- It claimed a test covered a bug it had never actually verified
- It never once looked at a figure it generated, just trusted the numbers
- It restarted a 50-hour training job without diffing the config against the reference run, l
Read full README on GitHub →

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