phd-skills

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 (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.

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Paper search and acquisition use Paper Search CLI, installed with uv and Python 3.10+. See paper search setup. Other skills do not require it.

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, lost three days
• It claimed an experiment was diverging based on a non-converged proxy metric, killed it before downstream eval would have shown the truth
• It ran rm -rf on a path it had hallucinated from memory, lost local checkpoints

Other plugins give you more commands. This plugin gives you guardrails.

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Install

claude plugin marketplace add fcakyon/phd-skills
claude plugin install phd-skills@phd-skills

The plugin works correctly the moment it is installed. Optional: run /phd-skills:setup for a 30-second tour of what was auto-detected and to opt into extras (notifications, allowlist, LaTeX).

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Usage

Open Claude Code in your project directory, then:

• /phd-skills:reproduce arxiv 2508.12345 reproduce a paper from arxiv URL through replication runs
• "why is my loss diverging?" the debug skill auto-triggers, runs evidence-first probes
• "compare run alpha to baseline" the compare skill auto-triggers, aligns at the same epoch
• "launch the new training run" the launch skill auto-triggers, runs the pre-flight checklist
• /loop 30m check experiment logs, notify me if metrics beat the baseline or if loss starts to diverge

Notifications (task completion, background agents) forward to ntfy / Slack / email after /phd-skills:setup.

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What You Get

Commands

Command	What it does
/phd-skills:xray	Audit paper against code and data (5 parallel dimensions)
/phd-skills:factcheck	Verify BibTeX entries and cited claims against DBLP
/phd-skills:gaps <topic>	Literature gap analysis with web confirmation
/phd-skills:fortify [venue]	Select strongest ablations + anticipate reviewer questions
/phd-skills:setup	Auto-detection tour + optional extras
/phd-skills:help	Show all features at a glance

Skills (auto-trigger, just describe what you need)

When you say...	Skill activates
"reproduce this arxiv paper"	Reproduce
"why is X failing / diverging / OOMing"	Debug
"compare run A to baseline"	Compare
"launch a new training run" / "kick off training"	Launch
"design an ablation study"	Experiment Design
"find related papers on X"	Literature Research
"check if my numbers match the code"	Paper Verification
"review my methods section for consistency"	Paper Writing
"analyze dataset bias"	Dataset Curation
"prepare code for open-source release"	Research Publishing
"what will reviewers ask about this?"	Reviewer Defense
"setup latex for CVPR"	LaTeX Setup

Agents (Claude delegates automatically)

Agent	What it does	Special
paper-auditor	Cross-checks paper claims vs code and data	Runs in isolated worktree, remembers patterns across sessions
experiment-analyzer	Analyzes results from wandb / neptune / tensorboard / mlflow / local	Hands off to compare and debug skills for discipline

Research Guardrails (run silently, you never invoke these)

What it catches
Conclusions reviewed against actual artifacts by a fresh-context research peer
In-place edits to git-tracked source over SSH
Unverified commands or paths in outbound teammate messages
Project-internal jargon shapes in commits and docs
Timezone tokens that do not match the system clock
Pre-flight checklist on long ML training launches
Fabricated paths in destructive commands (rm / mv / dd / force-push)
Missing citation verification when editing .tex/.bib
LaTeX compilation errors after .tex edits
Unreviewed generated images/figures
Research state loss before context overflow

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How It Compares

	phd-skills	flonat/claude-research	Others
Commands to learn	6	39	13-20
Research integrity hooks	11 (agent + 10 auto-detect)	1	0
Paper reproduction (arxiv to runs)	Yes (7-stage skill)	No	No
Paper-code consistency audit	5-dimension parallel	Read-only, no code cross-ref	None
Experiment monitoring + SSH notifications	Yes (ntfy / slack / email)	No	No
External dependencies	Paper Search CLI for paper workflows	npm + pip + MCP servers	MCP required
Install time	30 seconds	10+ minutes	Varies

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Design Principles

1. Methodology over scripts. Skills teach the approach, Claude generates code for your specific setup (wandb, neptune, local files, whatever)
2. Human oversight first. Claude makes premature claims and jumps to conclusions. Every skill builds in verification checkpoints
3. Actionable output. Ranked suggestions with specific fixes, never just a list of findings

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License

MIT. Use it, fork it, adapt it to your research.

Thank you for the support!

Contributors