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Research
Command

/fortify

Select strongest ablations and anticipate reviewer questions. Reads paper and experiment results to prepare for peer review.

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phd-skills
4236 skills2 agents6 commands
Install
> /plugin marketplace add fcakyon/phd-skills
> /plugin install phd-skills@phd-skills

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/fortify

Context preview

What this command does when you run it.

Select strongest ablations and anticipate reviewer questions. Reads paper and experiment results to prepare for peer review.

Command definition

fortify.md
description: >
  Select strongest ablations and anticipate reviewer questions.
  Reads paper and experiment results to prepare for peer review.
argument-hint: "<venue name (optional)>"
allowed-tools: Agent, Read, Glob, Grep, Bash, WebSearch, WebFetch

Paper Fortification

You are helping strengthen a paper before submission by selecting the best ablations and preparing for reviewer questions.

Step 1: Read the Paper

  • Find and read all .tex files
  • Identify the claims made in the paper (especially in contributions and experiments)
  • Note the evaluation metrics and baselines used

Step 2: Find Experiment Results

  • Search for result files: JSON, CSV, wandb logs, checkpoint directories
  • Look for training configs that define ablation variants
  • Identify all available experimental runs and their outcomes

Step 3: Rank Ablations

For each available ablation or experiment:

| Run | Factor Changed | Primary Metric Delta | Narrative Strength | Include? | |-----|---------------|--------------------|--------------------|----------|

Ranking criteria: 1. **Impact magnitude**: largest metric improvement = most convincing 2. **Narrative strength**: directly supports a specific paper claim 3. **Uniqueness**: shows something no other ablation shows 4. **Anticipated questions**: preemptively answers likely reviewer concerns

Recommend: main paper (top 3-5) vs supplementary (rest).

Step 4: Generate Reviewer Questions

Consider the venue: $ARGUMENTS (if provided, adjust expectations accordingly).

Generate the **top 10 most likely reviewer questions**:

1. [Question]
   Why they'd ask: [motivation]
   Answerable now: [Yes — point to data / No — needs new experiment]
   Draft response: [2-3 sentences if answerable]

Step 5: Identify Weaknesses

List specific weaknesses a reviewer might flag:

  • Missing baselines
  • Claims not fully supported by evidence
  • Scalability or generalization concerns
  • Missing statistical significance

Step 6: Output

## Fortification Report

### Recommended Ablation Subset
[Table with justification for each]

### Top 10 Anticipated Questions
[Numbered list with draft responses]

### Weaknesses to Address
[Prioritized list with suggested text edits]

### Suggested Edits
[Specific paragraph-level improvements]
Read more
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