publication-chart-skill

name publication-chart-skill description This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants an existing scientific figure/table reviewed and upgraded. version 0.2.0 Publication Chart Skill Goal Use this skill to turn research results into publication-grade figures and tables with an end-to-end workflow. Primary production stack: pubfig for figures pubtab for publication tables This skill covers the full delivery chain: understand the scientific communication goal, choose the right artifact type, map the task to pubfig , pubtab , or both, generate concrete runnable instructions, export paper-ready assets, run publication QA, propose targeted revisions. Use this skill when Trigger this skill for requests like: “make a publication-quality figure” “choose the right chart for these results” “turn these results into a paper-ready figure” “make a benchmark / ablation / calibration / forest / heatmap / scatter / line / bar figure” “make a benchmark / appendix / ablation table from Excel” “convert this Excel table into publication-ready LaTeX” “prepare one summary figure plus one companion table for the results section” “review and improve this scientific figure/table” “I already have a weak chart / screenshot / draft plot — make it publication-ready” “export panels for a paper figure” Do not use this skill for Do not use this skill when the task is mainly: manuscript prose writing, statistical testing without artifact design, raw exploratory analysis with no publication deliverable, Figma-first layout work before the figure/table content is solid. For simple composite assembly after the figure content is already strong, use the optional secondary workflow in references/composite-assembly.md . Primary contract Inputs Expect some combination of: the scientific communication goal, available data shape, venue or style constraints, whether the artifact is a figure, table, or mixed deliverable, optional existing assets such as code, spreadsheets, .tex , screenshots, or draft plots, whether the user needs a first draft, a publication-ready artifact, or a review/revision pass. Outputs The minimum useful output is: the recommended figure/table form, the recommended pubfig / pubtab route, a minimal runnable code snippet or CLI command, explicit export filenames and formats, a publication QA summary, and, when needed, a revision plan. Default workflow 0. Probe the environment and artifact state Before generating anything, identify: whether pubfig or pubtab is actually available, whether the user already has code / spreadsheets / .tex / screenshots, whether the deliverable is a fresh build or a revision, whether the result needs exact values, fast visual perception, or both. Prefer the smallest environment check that helps execution. When the bundled helper script is available, use it first: python3 scripts/ensure_publication_tooling.py --require pubfig --json python3 scripts/ensure_publication_tooling.py --require pubtab --json Equivalent manual checks are still acceptable when needed: python -c "import pubfig; print(pubfig.__version__)" python -c "import pubtab; print(pubtab.__version__)" pubtab --help Report the result clearly as available or missing . If a dependency is missing and the task requires runnable execution: auto-install it by default , prefer the user’s active environment instead of guessing a random global interpreter, use python3 scripts/ensure_publication_tooling.py --require ... as the default bundled route when the script is present, let that helper choose uv vs python -m pip against the active interpreter, re-run the availability probe after installation, and only then continue with the artifact workflow. Equivalent concrete commands include: python3 scripts/ensure_publication_tooling.py --require pubfig python3 scripts/ensure_publication_tooling.py --require pubtab uv pip install pubfig uv pip install pubtab python -m pip install pubfig python -m pip install pubtab If auto-install fails, report the exact failure and then degrade gracefully. Do not block on a full environment audit. 1. Classify the task Classify the request along these axes: artifact type : figure / table / mixed deliverable maturity : exploratory draft / publication-ready generation / revision of an existing artifact structure : single panel / multi-panel / figure-plus-table package evidence mode : pattern perception / exact value lookup / both Do not jump into plotting code before the communication target is clear. Before plotting research results, lock the evidence contract: primary scientific claim, unit of analysis, primary metric and metric direction, whether repeated rows are independent, missing cells or incomplete comparison blocks, error-bar basis: subject, subject-task, fold, seed, run, or bootstrap sample, whether exact values need a companion table, whether the current evidence allows a winner/significance claim. If these are unclear, ask or produce an audit recommendation instead of a polished figure. Do not create a paper-ready plot while the unit of analysis, missing-cell handling, or error-bar basis is unresolved. 2. Choose the representation Choose the representation based on the scientific claim, not novelty or visual flair. Common families: comparison — grouped scatter, bar, line comparison, benchmark summary, companion table ablation — grouped comparison, dumbbell, paired comparison, compact table distribution — box, violin, raincloud, histogram, density, ECDF, QQ relationship — scatter, bubble, contour2d, hexbin trend — line, area evaluation / diagnostic — calibration, ROC, PR, Bland–Altman, forest plot, volcano composition / hierarchy — UpSet, stacked ratio, donut, radial hierarchy, circular grouped or stacked bars table — benchmark table, ablation table, dataset summary, appendix table, error breakdown Avoid weak defaults: avoid pie/donut when exact comparison matters and a bar/table is clearer, avoid radar unless the comparison is genuinely profile-like and low-cardinality, avoid 3D, decorative gradients, and dense legends used only for style, avoid forcing every result into a figure when a publication table communicates the evidence better. If the request is ambiguous, explicitly state what scientific claim the artifact is supposed to support. 3. Map to the toolchain Default mapping: Figures → pubfig Tables → pubtab Mixed deliverables → use both, with each artifact carrying a distinct role Tool roles: pubfig is the default figure engine for scientific plots and paper-ready export. pubtab is the default table engine for Excel ↔ LaTeX workflows, preview, and publication-ready table export. Figma/composite assembly is an optional secondary branch for multi-panel finishing. Route selection rules: prefer Python for pubfig figure generation, prefer CLI for pubtab when the task is file-driven, prefer Python for pubtab when the task is already inside a notebook or scripted pipeline, keep the figure and table responsibilities separate in mixed requests. 4. Generate concrete artifact instructions Prefer the smallest production-ready artifact first: minimal runnable Python for pubfig , or minimal CLI/Python for pubtab Then add publication parameters only when justified: labels, caption, width, export format, backend, preview, panel packaging, or composite layout. Keep filenames and suffixes explicit. Good defaults: figures: one pubfig call + one save_figure(...) multiple figure outputs: batch_export(...) tables: one pubtab xlsx2tex ... or pubtab.preview ... mixed requests: one figure route + one table route, clearly separated 5. Define the delivery contract For every response, make these explicit when possible: the claim the artifact supports, which part is handled by pubfig and which by pubtab , the output filenames, the output formats, whether the artifact is draft / final / revision, what still needs user-provided data or manuscript context. 6. Run publication QA After generation, check: title and legend density, axis labels and units, category ordering and baseline clarity, color accessibility and grayscale robustness, font / line-weight consistency, caption readiness, figure/table readability after downscaling, panel consistency for multi-panel figures, venue-fit issues such as width, crowding, or over-annotation. The QA output must be concrete. Do not say “looks better” without naming why. 7. Revise If the result is weak, revise with specific changes such as: switch chart family, remove chartjunk, reorder categories, move exact values into a table, split a crowded panel, add or simplify the caption, change export width, or convert the deliverable from figure-first to table-first. Missing dependency behavior If pubfig or pubtab is not available: do not fail immediately, first attempt automatic installation into the active environment, prefer python3 scripts/ensure_publication_tooling.py --require ... when the bundled script exists, explicitly state which dependency is missing, state which install command or helper route is being used, re-check availability after installation, if installation succeeds, continue with the runnable workflow, if installation fails, degrade to a design/specification workflow, provide pseudocode or draft commands, preserve the recommended figure/table structure, still provide QA and revision guidance. Composite assembly rule Treat composite or Figma assembly as secondary : use it when the user explicitly wants a multi-panel paper figure, or when panel-level export and layout polishing are genuinely needed. Do not escalate simple figure tasks into composite/Figma workflows by default. Output style rules Prefer direct, implementation-usable outputs. Explain the why of chart/table choice briefly, then give the runnable route. When execution matters, include a short environment status block such as pubfig: available/missing , pubtab: available/missing . If a dependency is missing, state the exact helper command or install command, perform the installation, and report the post-install status. When a table is stronger than a figure, say so explicitly. When a figure is stronger than a table, say so explicitly. When both are needed, assign them different communication roles. Keep revision guidance actionable and falsifiable. Recommended response shape A strong response using this skill usually has 6 parts: Artifact decision — figure / table / paired deliverable, and why Tool route — pubfig , pubtab , or both Minimal implementation — runnable code or CLI Export plan — filenames, formats, width/backend/preview choices Publication QA — what to verify before paper submission Revision plan — what to change if the current artifact is weak Resources Load these as needed: references/workflow.md — full end-to-end decision order and delivery contract references/chart-selection.md — task-to-chart mapping and anti-patterns references/execution-and-verification.md — environment probing, forced install behavior, and runnable verification scripts/ensure_publication_tooling.py — bundled probe + auto-install helper for pubfig / pubtab references/pubfig-recipes.md — shortest useful figure patterns and export routes references/pubtab-recipes.md — shortest useful table routes and backend guidance references/source-guides/pubfig-architecture.md — package layout and figure-generation boundaries from source references/source-guides/pubfig-api-map.md — stable public pubfig surface and chart-family map from __init__.py references/source-guides/pubfig-export-flow.md — figure export, publication sizing, and panel-export flow from source references/source-guides/pubtab-architecture.md — package layout and forward/reverse conversion architecture from source references/source-guides/pubtab-cli-api-flow.md — CLI-to-API control flow and batch/sheet behavior from source references/source-guides/pubtab-backend-and-preview.md — backend/theme split and real preview compile pipeline from source references/publication-qa-checklist.md — figure/table QA checklist references/composite-assembly.md — optional multi-panel and Figma branch For prompt-shaped examples, see examples/ .