figure-results-review

Original：🇺🇸 English

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Review experimental results, plots, tables, and figures before they are shown in a meeting, paper, report, or presentation. Use this skill whenever the user wants to present results, check a figure, interpret experiment plots, prepare result slides, validate captions, audit axes/legends/error bars, or make sure results are connected to hypotheses and experimental setup.

2installs

Sourcea-green-hand-jack/phd-skills

Added on2026-04-25

NPX Install

npx skill4agent add a-green-hand-jack/phd-skills figure-results-review

SKILL.md Content

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Figure Results Review

Purpose

Help the user present experimental results clearly and scientifically. This skill follows the handbook's result presentation and figure standards: explain setup before results, define variables and hypotheses, label axes with units, use captions and legends, include uncertainty when appropriate, and point out key trends.

The output is a figure/result review note and a corrected presentation plan.

When to Use

User has plots, tables, or result slides to show
User wants feedback on a figure
User is preparing experimental results for a paper or talk
User is unsure how to explain a result
User wants to check whether a plot is publication-ready

Workflow

Stage 1: Establish the Result Context

Ask:

What claim is this result meant to support?
What is the experimental setup?
What are the baselines?
What metrics are used?
What should the audience notice first?

Do not review aesthetics before the scientific claim is clear.

Stage 2: Figure Checklist

For each figure, check:

Descriptive title
X-axis label with units
Y-axis label with units
Legend for all symbols/colors
Readable font size
Accessible color scheme
Error bars or confidence intervals where appropriate
Caption explains what is shown and what to conclude
Scale/range does not distort the comparison
Consistent model colors and ordering across subplots

Stage 3: Results Interpretation

Ask the user to state:

Main trend
Quantitative effect size
Comparison to baseline
Whether it confirms or contradicts the hypothesis
Plausible confounders
Negative or surprising findings

If the user says "it improves performance," push for exact metric deltas and conditions.

Stage 4: Presentation Sequence

Create a speaking order:

Experimental setting
Variables and metrics
Hypothesis
Figure caption in plain language
Axes and legend
Main trend
Takeaway and limitation

This prevents the common mistake of jumping straight to a plot and forcing the audience to decode it.

Stage 5: Produce the Artifact

Save to

~/phd-log/results/YYYY-MM-DD-[short-topic].md

.

markdown

# Figure / Results Review — [Short Topic]

## Claim
[What this result is meant to support]

## Experimental setup
- Dataset:
- Baselines:
- Metrics:
- Variables:
- Hypothesis:

## Figure checklist
| Figure | Issue | Fix | Priority |
|---|---|---|---|

## Interpretation
- Main trend:
- Effect size:
- Baseline comparison:
- Confounders:
- Negative/surprising results:
- Takeaway:

## Presentation script
1. Setup:
2. Metric:
3. Hypothesis:
4. Figure explanation:
5. Trend:
6. Limitation:
7. Next step:

## Fix list
- [ ] [figure/result fix]

Tone

Be exacting. A beautiful figure that does not explain the setup or claim is not ready.

What Not to Do

Do not accept unlabeled axes.
Do not let the user present a result without the experimental setting.
Do not infer a causal claim from a weak comparison.
Do not hide negative results when they clarify the research direction.