xuzhougeng/wisp-science

figure-composer

Compose 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.

77Collecting
See how to use itView GitHub source
npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/figure-composer"

Quick start

Start using it in three steps

Install it or open the source, trigger it with a clear task, then follow the source workflow.

1

Install the Skill

npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/figure-composer"
2

Describe the task

Use figure-composer to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.

3

Follow the workflow

5 key workflow steps, examples, and cautions are distilled below.

Continue to the workflow

Direct answers

Answers to review before you install

What is figure-composer?

Compose 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.

Who should use figure-composer?

It is relevant to workflows involving Design, Operations.

How do you install figure-composer?

SkillSignal detected this source-specific command: npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/figure-composer". Inspect the repository and command before running it.

Which Agent platforms does it support?

The upstream source does not declare a dedicated Agent platform.

What permissions or risks should you review?

No obvious permission action was detected by the static rules. This is not proof that the Skill is safe.

What are the current evidence limits?

This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.

SkillSignal brief

Decide whether it fits your work first

Compose 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.

Useful in these contexts

Not yet included in a workflow collection

Core capabilities

DesignOperations

Distilled from the source

Understand this Skill in one minute

About 2 min · 5 sections

Core workflow

  1. 1

    Build an outline matching figureoutlineschema(). Put real paths in

  2. 2

    Make panel a the conceptual hook and panel b the primary evidence. Use a

  3. 3

    Build one instruction per panel with paneltask(...).

  4. 4

    If delegatetasks is advertised, submit the independent panel tasks as one

  5. 5

    Compose returned paths with composefigure(...). Do not pass placeholder

Repository stars
560
Repository forks
68
Quality
77/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

77/100
Documentation22/30
Specificity13/25
Maintenance20/20
Trust signals22/25

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View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 2 min

Figure composer

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

  1. Build an outline matching figure_outline_schema(). Put real paths in data_path; use null for schematics.
  2. Make panel a the conceptual hook and panel b the primary evidence. Use a 12-column grid and one row per sub-claim.
  3. Build one instruction per panel with panel_task(...).
  4. If delegate_tasks is advertised, submit the independent panel tasks as one batch. Grant each task the minimum advertised capabilities needed, normally visualization plus project_read. Require a concrete PNG filename in each output schema. If delegation is unavailable, render the panels sequentially with python.
  5. Compose returned paths with compose_figure(...). Do not pass placeholder markers to the composer.
  6. Use compose_crops(...) with Pillow to save temporary crop files, then call view_image on the composite and every crop. Fix seams, clipped labels, aliases, empty space, and misplaced panel letters before review.
  7. Build one reviewer instruction with composite_review_task(...). Delegate it with image_inspection, project_read, and reasoning when those capability ids are advertised; otherwise perform the review in the current Agent.
  8. 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_tasks only as an explicit Wisp tool; never call delegation from python.
  • Use view_image only on a concrete local image file.
  • Keep data preparation in normal project files. Use run_in_context only 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.
Skill path
skills/figure-composer/SKILL.md
Commit SHA
95d2c13d1665
Repository license
AGPL-3.0
Data collected