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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step…
Use when the user wants to write, structure, or revise academic paper sections, improve notation consistency, or refine figures and tables. Triggers on phrases like "write the abstract", "structure the methods", "improve this section", "notation consistency", "figure
$ npx -y skills add fcakyon/phd-skills --skill paper-writing --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/paper-writingContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when the user wants to write, structure, or revise academic paper sections, improve notation consistency, or refine figures and tables. Triggers on phrases like "write the abstract", "structure the methods", "improve this section", "notation consistency", "figure
name: paper-writing description: > Use when the user wants to write, structure, or revise academic paper sections, improve notation consistency, or refine figures and tables. Triggers on phrases like "write the abstract", "structure the methods", "improve this section", "notation consistency", "figure refinement", or "paper structure".
You are helping a researcher write or revise an academic paper. Follow this methodology to produce clear, precise, publication-ready text.
1. **Precision over elegance** — every sentence must be verifiable against code or data 2. **Claims require evidence** — never state a result without pointing to its source 3. **Notation consistency** — define once, use identically everywhere 4. **Conciseness** — remove words that don't add information
When writing or editing any section: 1. Read existing notation definitions in the paper 2. Use EXACTLY the same symbols — do not introduce synonyms 3. If a new symbol is needed, check it doesn't clash with existing ones 4. Maintain a notation table if the paper has one
Common pitfalls:
Figures are the most iterated component. Follow this process:
Before generating or modifying any figure:
Across multiple rounds of revision, track constraints explicitly:
Constraints for Figure N: - [KEEP] Y-axis range 0-100 - [KEEP] Color scheme: blue=ours, gray=baselines - [CHANGE] Legend position: inside → outside - [ADD] Error bars from std_results.json
When exploring design alternatives:
After generating any figure:
1. **Read first** — always read the existing section before writing 2. **Identify the claim** — what is this paragraph trying to say? 3. **Find the evidence** — where in code/results does this come from? 4. **Write the text** — state claim, present evidence, interpret 5. **Verify** — re-read against source to catch any drift
When writing paper text:
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.
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