pygraphistry

name

pygraphistry

description

TOC router for PyGraphistry Python SDK tasks. Use when asked to "plot a graph", "visualize my edges", "load a dataframe into graphistry", "import graphistry", "run UMAP on my graph", "query my graph with GFQL or Cypher", "pick an execution engine", "make my GFQL query faster", "connect graphistry to Neo4j/Splunk/Kusto", or any Python SDK graph workflow. Also triggers on "graphistry.register", "g.plot()", ".gfql()", "chain-list", "hypergraph", "engine='polars'", "polars-gpu", "cudf", or "pygraphistry". Dispatches to pygraphistry-core (auth/ETL/plot), pygraphistry-gfql (queries, engines, indexes), pygraphistry-visualization (styling/sharing), pygraphistry-ai (UMAP/DBSCAN/embeddings), or pygraphistry-connectors (external DBs). Proactively suggest when the user shares an edges/nodes DataFrame and asks about graph analysis or visualization.

PyGraphistry Router

Use this skill as a dispatcher to specialized skills. Treat this as the Python SDK entrypoint; for a cross-interface entrypoint use graphistry.

Route By Intent

• Setup/auth/first plot from tables: use pygraphistry-core.
• Direct Graphistry REST endpoint requests (curl, /api/v2/..., JWT/Bearer, upload/session/url params): immediately use graphistry-rest-api and skip ReadTheDocs discovery.
• Styling/layout/static output/privacy and sharing: use pygraphistry-visualization.
• Pattern matching, GFQL/Cypher queries, Let/DAG bindings, GRAPH constructors, predicates, remote graph queries, or choosing pandas/polars/cudf/polars-gpu: use pygraphistry-gfql.
• UMAP/DBSCAN/embedding/anomaly and graph-AI notebooks: use pygraphistry-ai.
• Database/platform integrations (Neo4j, Splunk, Kusto, Databricks, SQL, etc.): use pygraphistry-connectors.

Fast Targeted Fetch Protocol

• REST-intent exception: do not run this protocol for REST tasks routed to graphistry-rest-api.
• Start from references/pygraphistry-readthedocs-toc.md; do not crawl broad docs first.
• Use references/pygraphistry-readthedocs-top-level.tsv for section-level shortcuts.
• Pick exactly one primary skill and at most two secondary docs before fetching content.
• Do one batched discovery read first (TOC + top-level index), then pick targets.
• After discovery, do at most one deep-page read per iteration.
• Prefer section indexes (.../gfql/index.html, .../visualization/index.html) before deep pages.
• For routing replies, prefer ReadTheDocs URLs (https://pygraphistry.readthedocs.io/...) over GitHub/local file paths unless the user explicitly asks for source code links.
• Keep the first routing response compact (typically 3-5 lines): selected skill + top links.
• Escalate to deeper page fetches only after the user confirms direction or asks for detail.
• Avoid serial micro-reads across many files when one batched lookup can answer routing.
• Avoid blind full dumps (cat *, full sitemap dumps) that bloat context without improving routing quality.
• For canonical URL verification, prefer local snapshot evidence first; use web fallback when the user requests freshness or local mapping is missing.

Default Safety Rules

• Read credentials from environment variables; do not hardcode secrets in tracked files.
• Prefer api=3 for modern features.
• Prefer a plain type column on both nodes and edges for legend/category defaults.
• Set explicit privacy mode before sharing links for sensitive data.
• For large graphs, reduce columns/rows before upload and visualize focused subgraphs first.

Canonical Docs

• Main docs: https://pygraphistry.readthedocs.io/en/latest/
• Docs TOC snapshot: references/pygraphistry-readthedocs-toc.md
• ReadTheDocs version sitemap: https://pygraphistry.readthedocs.io/sitemap.xml
• 10 minutes core: https://pygraphistry.readthedocs.io/en/latest/10min.html
• Visualization: https://pygraphistry.readthedocs.io/en/latest/visualization/index.html
• GFQL: https://pygraphistry.readthedocs.io/en/latest/gfql/index.html
• Plugins/connectors: https://pygraphistry.readthedocs.io/en/latest/plugins.html