Apply PyGraphistry graph ML/AI workflows such as UMAP, DBSCAN, embedding-based anomaly analysis, and fit/transform pipelines on nodes or edges. Use for feature-driven exploration, clustering, anomaly triage, and graph-AI notebook workflows.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pygraphistry-core-pack:pygraphistry-aiThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- First route with `../pygraphistry/references/pygraphistry-readthedocs-toc.md`.
../pygraphistry/references/pygraphistry-readthedocs-toc.md.../pygraphistry/references/pygraphistry-readthedocs-top-level.tsv for section-level shortcuts.../pygraphistry/references/pygraphistry-readthedocs-sitemap.xml when a needed page is missing.cat * and serial micro-reads.https://pygraphistry.readthedocs.io/en/latest/... links.umap, embed, optional dbscan).# Similarity embedding / projection
g2 = graphistry.nodes(df, 'id').umap(X=['f1', 'f2', 'f3'])
g2.plot()
# Fit/transform flow for consistent projection on new batches
g_train = graphistry.nodes(df_train, 'id').umap(X=['f1', 'f2'])
g_batch = g_train.transform_umap(df_batch, return_graph=True)
g_batch.plot()
# Semantic search over embedded features
g2 = graphistry.nodes(df, 'id').umap(X=['text_col'])
results_df, query_vector = g2.search('suspicious login pattern')
# Text-first workflow: featurize then search/cluster
g2 = graphistry.nodes(df, 'id').featurize(kind='nodes', X=['title', 'body']).umap(kind='nodes').dbscan()
hits, qv = g2.search('credential stuffing campaign')
# Precomputed embedding columns
embedding_cols = [c for c in df.columns if c.startswith('emb_')]
g2 = graphistry.nodes(df, 'id').umap(X=embedding_cols)
g_new = g2.transform_umap(df_new, return_graph=True)
X=...) for reproducibility.featurize(...).umap(...).search(...) when queries are natural language.X=[...]) before recomputing.npx claudepluginhub graphistry/graphistry-skills --plugin pygraphistry-core-packRuns Neo4j Graph Data Science algorithms via Python client or Cypher on embedded GDS plugin. Covers graph projection, catalog ops, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and memory estimation.
Provides workflows and best practices for 73 Gephi MCP tools to build, analyze, style, layout, and export network graphs in Gephi Desktop.
NetworkX toolkit for graph creation, centrality, shortest paths, community detection, generators, and I/O. Use for 100K+ node graphs with igraph/graph-tool; for GNNs use PyG.