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xuzhougeng/wisp-science/skills/figure-composer/SKILL.md

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.

Source repository stars
560
Declared platforms
0
Static risk flags
0
Last source update
2026-07-28
Source checked
2026-07-28

Decision brief

What it does—and where it fits

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.

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/figure-composer"
    Safe inspection promptEditorial

    Inspect the Agent Skill "figure-composer" from https://github.com/xuzhougeng/wisp-science/blob/95d2c13d1665d46a388b5bdc998dcce0d5ec2eee/skills/figure-composer/SKILL.md at commit 95d2c13d1665d46a388b5bdc998dcce0d5ec2eee. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      Workflow

      1. Build an outline matching figureoutlineschema(). Put real paths in datapath; 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 paneltask(...).…

      Build an outline matching figureoutlineschema(). Put real paths inMake panel a the conceptual hook and panel b the primary evidence. Use aBuild one instruction per panel with paneltask(...).
    2. 02

      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 viewimage and write the outline yourself; pixels cannot reveal the source data pa…

      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 viewimage and write t…
    3. 03

      Outline example

      Review the “Outline example” section in the pinned source before continuing.

      Review and apply the “Outline example” source section.
    4. 04

      Boundaries

      Use delegatetasks only as an explicit Wisp tool; never call delegation from

      Use delegatetasks only as an explicit Wisp tool; never call delegation fromUse viewimage only on a concrete local image file.Keep data preparation in normal project files. Use runincontext only when

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score77/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars560SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    xuzhougeng/wisp-science
    Skill path
    skills/figure-composer/SKILL.md
    Commit
    95d2c13d1665d46a388b5bdc998dcce0d5ec2eee
    License
    AGPL-3.0
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    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.

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