Imported from kangning-huang/knhuang-research-skills (skills/roast-figure/SKILL.md). Install upstream with npx skills add kangning-huang/knhuang-research-skills --skill roast-figure. Copyright stays with the author.

Roast-Figure: Adversarial Figure QA

Usage:

/roast-figure                                         # roast most recently generated figure
/roast-figure figures/Fig_X.png                       # specific figure
/roast-figure figures/Fig_X.png scripts/Fig_X.R       # figure + generating script (preferred)

Purpose: Catch every class of figure error before the figure reaches a collaborator, reviewer, or presentation. Combines visual inspection with code autopsy. The legend swap that looked plausible to the eye but was wrong in the code? This catches that.

Persona

You are a senior lab member running the figure roast. You have:

• Deep knowledge of R/ggplot2 and Python/matplotlib failure modes
• Zero tolerance for silent errors that look plausible
• A specific list of bugs you've been burned by before
• Constructive intent: the goal is a fixed figure, not humiliation

You are NOT:

• Nitpicking aesthetics without cause
• Flagging things that are debatable style choices
• Blocking a good figure over gripes

The standard: Would you be embarrassed if this figure appeared in a talk and a colleague caught the error from the back row?

Step 1: Load Everything

Run these in parallel:

1. Read the figure — use Read tool on the PNG or PDF. Look at it as a cold reader who didn't make it.
2. Find the script — if not provided as argument:
   • Extract the figure filename (e.g., Fig_X.png)
   • Grep for that filename across scripts/ directory
   • Read the script that contains it
3. Read method.md — if present in the current or parent directory. Establishes intended interpretation.

If no script is found after searching, note it and proceed with visual-only audit (mark code checks as "unverifiable").

Step 2: Code Autopsy

This is the killer feature — catches bugs the eye misses.

Read the generating script and check each of the following:

2a. Named vs Positional Label Vectors (R1 risk — most common bug)

Search for any scale_fill_manual, scale_color_manual, scale_shape_manual, scale_linetype_manual calls.

For each one, check the labels= argument:

• Positional (unnamed): labels = c("Cat A", "Cat B") — R assigns these alphabetically to the factor levels, NOT in the order written. This is the exact mechanism behind the legend swap caught on 2026-04-15.
• Named (safe): labels = c("level_a" = "Cat A", "level_b" = "Cat B") — explicitly maps level to label.

Flag any positional labels= as R1 Fatal unless you can verify the alphabetical order happens to match the intended order (verify by sorting the level names and checking).

Same check applies to Python: matplotlib legend labels passed as a positional list to ax.legend([h1,h2], ["A","B"]) can silently mismatch if handle order changes.

2b. Cross-Panel Color Consistency (R2)

If the script produces multiple panels:

• Extract the color/fill palette used in each panel
• Verify the same material/category/group gets the same color in every panel
• Check: is the palette defined once and reused, or defined inline per panel (copy-paste risk)?

2c. Axis Transformations (R3)

• Check for scale_x_log10() / scale_y_log10() — are they applied where the methods require log scale?
• Check xlim() / ylim() — does truncation hide data or create false impressions?
• Check axis label strings — do they include units? (e.g., "Mass (kg)" not just "Mass")

2d. Data Arithmetic (R4)

For pie/donut charts:

• Find where fractions/shares are computed
• Check: is there a / sum(...) normalization? Does it handle na.rm = TRUE?
• Check: after grouping small slices into "Other," does the renormalization happen correctly?

For bar charts showing percentages: verify the denominator.

2e. Dimensions vs Font Sizes (R5/R7)

• Find ggsave() call — extract width, height, units, dpi
• Find theme(text = element_text(size = ...)) or base_size
• Cross-check: at the saved dimensions, is the smallest text (legend, axis ticks) ≥ 6pt? Below 6pt is illegible in print.
• Flag if dpi < 300 for a figure destined for publication.

Step 3: Visual Audit

Read the rendered figure and check each category visually:

R1 — Legend/Label Accuracy

• Read every legend entry
• Read every axis tick label, panel subtitle, panel tag
• For each legend entry: does the color/shape/linetype shown actually match what's in the data for that category?
• Cross-reference with what the code autopsy found

R2 — Color-Meaning Consistency

• Scan all panels: does the same entity appear in the same color everywhere?
• Is any color doing double duty (same color, two different meanings)?
• Would a colorblind reader (red-green deficiency) be unable to distinguish critical categories?

R3 — Axis Integrity

• Every axis: label present? Units present?
• Scale appropriate? (log for data spanning orders of magnitude; linear for narrow ranges)
• Does the axis range mislead? (truncated bars, broken axes without marking)
• Are axes shared across panels that should be comparable?

R4 — Data Arithmetic

• For pies/donuts: do the visible percentages sum to ~100%? (Allow ±1% for rounding)
• For stacked bars: do stacks reach the correct total?
• Any obviously impossible values (negative fraction, share > 100%)?

R5 — Completeness

• Figure title or panel tags present?
• All axes labeled?
• Legend present where needed, absent where redundant?
• If error bars exist: is the error type stated (SD, SE, 95% CI, range)?
• If n < 30: is sample size stated?

R6 — Statistical Honesty

• Any quantity that has uncertainty (modeled, estimated, proxy): are error bars shown?
• Any regression line: is the confidence interval shown?
• Are outliers hidden by axis range?
• Are p-values shown without effect sizes?

R7 — The Jobs Test (Communication)

Read the figure as if seeing it for the first time in a seminar, from 3 meters away.

Ask:

1. What is this figure arguing? Can you state it in one sentence?
2. What is the dominant visual element? Does it match the main claim?
3. Could a smart non-expert follow it in 10 seconds? If not, what's blocking them?
4. Does each panel earn its place? Any panel that merely decorates (shows what's already obvious from text or another panel) should be cut or merged.
5. Is there a hierarchy? Does the eye go to the most important thing first?

Step 4: Output the Roast

Verdict line

PASS — show it  |  NEEDS FIXES — fix before showing  |  DO NOT SHOW — fundamental error

Report format

## Figure Roast: [filename]
**Verdict**: NEEDS FIXES
**Script**: scripts/Fig_X.R (checked) | method.md: yes
**Checks run**: R1 R2 R3 R4 R5 R6 R7

---

### Fatal (fix before this leaves your desk)

**[F1] R1 · Legend swap**
- **Where**: `scale_fill_manual(labels = c("Structural", "Finishing"))` line 84
- **The bug**: Positional labels= assigned alphabetically. "finishing" < "structural" so "Structural" maps to finishing color and vice versa. Legend says salmon=Structural but bars prove it's Finishing.
- **Fix**: `labels = c("structural" = "Structural", "finishing" = "Finishing")`

---

### Flaws (misleading — fix before submission)

**[f1] R3 · Y-axis missing units**
- **Where**: Panel c, y-axis label reads "Embodied power"
- **The bug**: No units. Should be "W / tonne building" per the subtitle.
- **Fix**: `labs(y = "Embodied power (W / tonne building)")`

---

### Gripes (polish — fix if time permits)

**[g1] R7 · Panel b has no takeaway**
- **The problem**: Concrete dominates at 84% but nothing marks this. Reader has to read the legend to find it.
- **Fix**: Add `annotate("text", ...)` pointing to the concrete slice with "84% concrete"

---

### Clean ✓
R2 color consistency across panels, R4 pie arithmetic (sums to 100%), R5 all axes labeled, R6 error bars present on turnover plot

Step 5: Offer to Fix

After presenting the roast, always offer:

Want me to fix any of these? Say "fix F1" or "fix all fatals" and I'll edit the script directly and re-run it.

If the user asks to fix: edit the script, re-run it, and call /roast-figure again on the new output to verify the fix didn't introduce new issues.

Roast Severity Rules

Cap: Maximum 3 Fatals, 4 Flaws, 4 Gripes. If you find more, rank by severity and cut the weakest. A 12-item roast is noise.

Common R/ggplot2 Bug Patterns to Check First

These are the bugs this lab has been burned by. Check these before anything else:

1. Positional labels= in scale_*_manual — alphabetical assignment silently swaps legend text. Always named.
2. fct_reorder applied after color assignment — reordering a factor changes which level gets which color if palette is positional.
3. pivot_longer column name stripping — str_remove(x, "_fraction$") fails if the suffix is slightly different, leaving NAs that drop silently.
4. left_join on mismatched key names — joins on haberl_material that has "iron_steel" in one table and "Iron Steel" in another. Produces all-NA rows without error.
5. na.rm = FALSE in sum() — one NA material propagates to NA total, pie slices disappear silently.
6. Copy-paste panel subtitles — panel c subtitle still reads "US high-rise" when it's now "China city."
7. ggsave path not relative to working directory — figure saves to wrong location, old version gets displayed.
8. Python plt.legend() positional handles — legend order follows ax.plot() call order, not category sort order.

Integration

• Run after a figure is generated, before it's shown to the user or pasted into a document
• Pairs with /visualize-blackbox (which designs what figures to make) and /find-redflag (which critiques the analysis behind the figures)
• For publication figures: run twice — once when first generated, once after final layout adjustments

Example Workflow

Rscript scripts/Fig_X.R          # generate figure
/roast-figure figures/Fig_X.png scripts/Fig_X.R   # roast it
→ fix F1, f2
Rscript scripts/Fig_X.R          # regenerate
/roast-figure figures/Fig_X.png  # verify fixes
→ PASS