Figure composer
SkillMediaCompose or improve a publication-grade multi-panel scientific figure from a claim, concrete data paths, or an existing image. Use for figure outlining, parallel panel rendering, exact-grid composition, visual inspection, and adversarial figure review. Use figure-style for one standalone plot and paper-narrative for whole-paper figure ordering.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Figure composer skill
What this skill tells your AI
The instructions your AI receives, as published by xuzhougeng/wisp-science in skills/figure-composer/SKILL.md and read by ahel’s review.
Load figure-style with this skill. The sidecar provides pure geometry,
composition, task-building, and review-schema helpers. It does not call models,
delegate Agents, resolve artifacts, or inspect images from Python.
Inputs
Require a one-sentence claim, target width in millimetres, and concrete
project-relative or absolute data paths. Never use artifact ids as paths. For an
existing figure, inspect the real image with view_image and write the outline
yourself; pixels cannot reveal the source data path.
Workflow
- Build an outline matching
figure_outline_schema(). Put real paths indata_path; usenullfor schematics. - Make panel
athe conceptual hook and panelbthe primary evidence. Use a 12-column grid and one row per sub-claim. - Build one instruction per panel with
panel_task(...). - If
delegate_tasksis advertised, submit the independent panel tasks as one batch. Grant each task the minimum advertised capabilities needed, normallyvisualizationplusproject_read. Require a concrete PNG filename in each output schema. If delegation is unavailable, render the panels sequentially withpython. - Compose returned paths with
compose_figure(...). Do not pass placeholder markers to the composer. - Use
compose_crops(...)with Pillow to save temporary crop files, then callview_imageon the composite and every crop. Fix seams, clipped labels, aliases, empty space, and misplaced panel letters before review. - Build one reviewer instruction with
composite_review_task(...). Delegate it withimage_inspection,project_read, andreasoningwhen those capability ids are advertised; otherwise perform the review in the current Agent. - Apply outline revisions and regenerate only affected panels. Stop after three rounds or when there are no blockers and at most two major findings.
Outline example
{
"claim": "Treatment restores the disease-associated trajectory.",
"width_mm": 180,
"ncol": 12,
"row_heights_mm": [42, 60],
"panels": [
{
"letter": "a",
"role": "schematic",
"row": 0,
"col": 0,
"colspan": 12,
"chart_family": "study schematic",
"message": "The experiment tests trajectory rescue.",
"data_path": null,
"ask": "Show cohorts, treatment, sampling, and comparison."
},
{
"letter": "b",
"role": "primary",
"row": 1,
"col": 0,
"colspan": 12,
"chart_family": "trajectory plot",
"message": "Treatment moves cells toward the healthy trajectory.",
"data_path": "results/trajectory.csv",
"ask": "Plot disease, treated, and healthy cells with confidence bands."
}
]
}
Boundaries
- Use
delegate_tasksonly as an explicit Wisp tool; never call delegation frompython. - Use
view_imageonly on a concrete local image file. - Keep data preparation in normal project files. Use
run_in_contextonly when a deterministic render or preprocessing job is long enough to require a persisted Run; Agent delegation itself is not a Run. - Save the accepted composite to a stable project path and report that path.
Signals
- GitHub stars
- 1k
- Forks
- 117
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
figure-composer-xuzhougeng- Source
- github.com/xuzhougeng/wisp-science