What
When eval returns data that would benefit from visualization (arrays, DataFrames, distributions), automatically suggest or generate a plot alongside the numeric result.
Why
In a real Julia REPL, you'd naturally call plot(x) after computing something interesting. An AI agent should do the same — when it computes a 1000-element vector, the raw numbers aren't useful, but a lineplot or histogram would be.
How it could work
Option A: PostToolUse skill guidance
The simplest approach — add skill instructions that tell Claude:
- After eval returns a large array → suggest
lineplot or histogram
- After eval returns a matrix → suggest
heatmap
- After eval returns a DataFrame → suggest
describe() + column histograms
Option B: Server-side detection
More integrated — capture_eval_on_worker could detect the result type and size, and append a hint to the tool response:
julia> X = randn(1000, 3)
1000×3 Matrix{Float64}:
...
[Hint: This is a 1000×3 numeric matrix. Try: heatmap(X) or scatterplot(X[:,1], X[:,2])]
Option C: Auto-plot for specific types
The most aggressive — when eval returns certain types and UnicodePlots is loaded, automatically generate and append a plot to the result. This would need a way to opt out.
Recommendation
Start with Option A (skill-based guidance) since it requires zero code changes. Graduate to Option B if agents consistently fail to suggest visualizations on their own.
What
When
evalreturns data that would benefit from visualization (arrays, DataFrames, distributions), automatically suggest or generate a plot alongside the numeric result.Why
In a real Julia REPL, you'd naturally call
plot(x)after computing something interesting. An AI agent should do the same — when it computes a 1000-element vector, the raw numbers aren't useful, but a lineplot or histogram would be.How it could work
Option A: PostToolUse skill guidance
The simplest approach — add skill instructions that tell Claude:
lineplotorhistogramheatmapdescribe()+ column histogramsOption B: Server-side detection
More integrated —
capture_eval_on_workercould detect the result type and size, and append a hint to the tool response:Option C: Auto-plot for specific types
The most aggressive — when eval returns certain types and UnicodePlots is loaded, automatically generate and append a plot to the result. This would need a way to opt out.
Recommendation
Start with Option A (skill-based guidance) since it requires zero code changes. Graduate to Option B if agents consistently fail to suggest visualizations on their own.