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pygraphistry-visualization笔画可视化

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

490

周安装

20

GitHub Stars

1

下载量

158
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:pygraphistry-visualization(笔画可视化)
来源仓库:https://github.com/graphistry/graphistry-skills
仓库路径:skills/pygraphistry-visualization
安装命令:
npx skills add https://github.com/graphistry/graphistry-skills --skill pygraphistry-visualization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/graphistry/graphistry-skills --skill pygraphistry-visualization

简介

用于辅助数据整理、表格处理、CSV/Excel 分析和图表准备。

  • 适合让 Agent 清洗字段、汇总数据、发现异常或生成统计口径。
  • 通过 GitHub 安装,需确认数据来源和字段含义后使用。
  • 涉及敏感数据或批量写回时,应先确认权限和脱敏边界。
  • 使用时避免把样本数据当作全量事实进行判断。pygraphistry-visualization 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

PyGraphistry Visualization

Doc routing (local + canonical)

  • First route with ../pygraphistry/references/pygraphistry-readthedocs-toc.md.
  • Use ../pygraphistry/references/pygraphistry-readthedocs-top-level.tsv for section-level shortcuts.
  • Only scan ../pygraphistry/references/pygraphistry-readthedocs-sitemap.xml when a needed page is missing.
  • Use one batched discovery read before deep-page reads; avoid cat * and serial micro-reads.
  • Use local icon lookup notes from references/fa-icons.md.
  • In user-facing answers, prefer canonical https://pygraphistry.readthedocs.io/en/latest/... links.

Core pattern

g2 = (
    # Keep a plain 'type' column on both nodes and edges for legend-friendly defaults
    g.bind(point_label='label', point_color='type', edge_color='type')
     .encode_point_color('type', categorical_mapping={'agent': '#3b82f6'}, default_mapping='#94a3b8')
     # Optional: default node sizing is often degree for exploratory passes
     .encode_point_size('degree')
     .settings(url_params={'play': 3000, 'info': True})
)
g2.plot()

External layout pattern

# nodes_df contains x/y layout columns
g2 = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id').bind(point_x='x', point_y='y').settings(url_params={'play': 0})
g2.plot()

URL parameters reference

Use settings(url_params={...}) to control visualization behavior. Full reference: https://hub.graphistry.com/docs/api/1/rest/url/#urloptions

Layout

ParamTypeDefaultRangeDescription
playint50000-10000 (0, 1000, 2000, 5000)Layout duration ms. 0=fixed
lockedXboolfalseLock X axis (with bind(point_x=...))
lockedYboolfalseLock Y axis (with bind(point_y=...))
lockedRboolfalseLock radial position
linLogboolfalseStrong separation; good for <1000 nodes
scalingRatiofloat1.00.1-10 (0.5, 1, 2, 5)Expansion ratio. Combine with linLog
strongGravityboolfalseCompact layout with pull to center
dissuadeHubsboolfalseReduce hub dominance in layout
gravityfloat1.00.1-10 (0.1, 1, 2, 10)Pull strength toward center
edgeInfluencefloat1.00-10 (0, 0.7, 1, 2, 5, 7)Edge weight impact on layout
precisionVsSpeedfloat1.00.1-10 (0.1, 1, 10)Higher=precise but slower
left/right/top/bottomintautoManual camera bounds on load

Scene / Rendering

ParamTypeDefaultRangeDescription
pointSizefloat1.00.1-10 (0.3, 0.5, 1, 2, 3)Point size multiplier (not encoding)
pointOpacityfloat1.00-1 (0.3, 0.5, 0.8, 1)Node transparency
pointStrokeWidthfloat00-5 (0, 1, 2)Node border width
edgeCurvaturefloat00-1 (0, 0.5, 1)Edge bending amount
edgeOpacityfloat1.00-1 (0.3, 0.5, 0.8, 1)Edge transparency
showArrowsbooltrueShow edge direction arrows
neighborhoodHighlightstrbothincoming/outgoing/both/nodeHover highlight mode
neighborhoodHighlightHopsint11-5 (1, 2, 3)Hops in hover highlight

Labels / Points of Interest

ParamTypeDefaultRangeDescription
showLabelsbooltrueToggle all label visibility
showLabelOnHoverbooltrueShow labels only on hover
showPointsOfInterestbooltrueHighlight key nodes as POI
showPointsOfInterestLabelbooltrueShow labels on POI nodes
pointsOfInterestMaxint50-100 (0, 5, 10, 20)Max POIs. 0=disable
shortenLabelsbooltrueTruncate long labels
showLabelPropertiesOnHoverboolfalseShow extra properties on hover
labelOpacityfloat1.00-1 (0.5, 0.8, 1)Label transparency
labelColorstrhex no # (000000, FFFFFF)Label text color
labelBackgroundstrhex no # (000000, FFFFFF)Label bg color

Note: URL params use hex without #. Python API (encode_*, palette) uses # prefix.

UI Controls

ParamTypeDefaultDescription
menubooltrueShow all menus
infobooltrueShow graph size stats
showHistogramsbooltrueShow histogram panel
showInspectorbooltrueShow entity inspector
showCollectionsboolfalseShow collections panel

Examples

# Ring layout with strong separation for small graphs (<1000 nodes)
g2 = g.settings(url_params={'play': 3000, 'linLog': True, 'scalingRatio': 2.0})

# Fixed position layout (external coordinates)
g2 = g.bind(point_x='x', point_y='y').settings(url_params={'play': 0, 'lockedX': True, 'lockedY': True})

# Disable POI labels entirely
g2 = g.settings(url_params={'showLabels': False, 'pointsOfInterestMax': 0})

# Larger points, more transparent edges
g2 = g.settings(url_params={'pointSize': 3.0, 'edgeOpacity': 0.3})

# Minimal UI for embedding
g2 = g.settings(url_params={'menu': False, 'info': False, 'showHistograms': False, 'showInspector': False})

Icon/badge pattern

g2 = (
    g.encode_point_icon('type', categorical_mapping={'person': 'user', 'org': 'building'})
     .encode_point_badge('risk', categorical_mapping={'high': 'exclamation-triangle'})
)
g2.plot()

Use valid Font Awesome names and keep icon mappings category-driven by type. See references/fa-icons.md for lookup links and examples.

Continuous-color pattern (beyond categorical maps)

g2 = g.encode_edge_color('amount', palette=['#46327e', '#1fa187', '#fde724'], as_continuous=True)
g2.plot()

Focused subgraph views (collection-like workflow)

# Use GFQL slices to publish multiple focused views from one base graph (use .gfql(), not deprecated .chain())
high_risk = g.gfql([...]).name('high-risk-slice')
partner_flow = g.gfql([...]).name('partner-flow-slice')
urls = [high_risk.plot(render=False), partner_flow.plot(render=False)]

Privacy-safe sharing pattern

graphistry.privacy(mode='private')
plot_url = g.plot(render=False)
  • Use explicit graphistry.privacy(mode='private'|'organization'|'public') before plotting share links.
  • Do not treat plot() kwargs like as_files or memoize as privacy controls.

Common tasks

  • Encodings: encode_point_color, encode_edge_color, encode_point_size, encode_point_icon, encode_point_badge.
  • Layouts: runtime force layout settings, ring/GIB/modularity, graphviz/igraph/cugraph plugin layouts.
  • Static outputs: plot_static() for SVG/PNG and text engines like graphviz-dot/mermaid-code.
  • Sharing controls: graphistry.privacy(mode='private'|'organization'|'public').
  • For advanced gradients, use palette=[...] with as_continuous=True on encode_point_color/encode_edge_color.
  • For large investigations, generate multiple focused GFQL slices instead of one overloaded plot.

Big-graph defaults

  • Filter and aggregate before plotting.
  • Keep only essential columns (drop large text blobs unless needed).
  • Use focused subgraphs (time slice, one-hop neighborhood, top-k signals).
  • Prefer plain type columns on both nodes and edges so legends and default category encodings stay stable.
  • Avoid dotted column names like node.type / edge.type; prefer plain names.
  • Use native datetime types for time encodings and time-sliced comparisons.

Canonical docs

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能力 4

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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