Skill files for AI agents (including Claude Code and OpenAI Codex) to better use the Graphistry ecosystem.
Graphistry is a graph intelligence ecosystem with fast-moving capabilities across graph ETL/shaping, visualization, GFQL graph querying, and AI workflows. These skills help agents use more of that surface area correctly and reach good results faster.
Strong frontier models often already know core Graphistry/PyGraphistry patterns due to ecosystem maturity and backward compatibility. The skills add high-value guidance on newer features, preferred workflow patterns, and safer/more reliable execution details.
graphistry: umbrella router across interfaces (SDK + REST; JS-ready routing path).graphistry-rest-api: REST specialist for auth, upload lifecycle, URL controls, sessions, and sharing safety.pygraphistry: Python SDK router.pygraphistry-core: auth, shaping, and first plot workflows.pygraphistry-visualization: bindings/encodings/layout/privacy/share patterns.pygraphistry-gfql: GFQL extraction/pattern workflows — chain-list syntax, Cypher strings, Let/DAG bindings, GRAPH constructors, remote execution.pygraphistry-ai: embedding, UMAP/DBSCAN, anomaly workflows.pygraphistry-connectors: connector/integration workflows.
Recommended (mixed SDK + REST usage):
npx skills add graphistry/graphistry-skills \
--agent codex \
--agent claude-code \
--skill graphistry \
--skill graphistry-rest-api \
--skill pygraphistry \
--skill pygraphistry-core \
--skill pygraphistry-gfql \
--skill pygraphistry-visualization \
--skill pygraphistry-ai \
--skill pygraphistry-connectors \
--yesThis repository intentionally includes two skill tiers:
- User-facing published skills:
pygraphistry*(the install snippet above lists these). - Internal maintainer skills live under
.agents/skills/internal/(for example:.agents/skills/internal/plan,.agents/skills/internal/eval-otel,.agents/skills/internal/benchmarks) and are markedmetadata.internal: true.
Internal maintainer skills are kept in-repo for contributor workflows and are not part of the default end-user install set.
REST-only minimal install:
npx skills add graphistry/graphistry-skills \
--agent codex \
--agent claude-code \
--skill graphistry \
--skill graphistry-rest-api \
--yes- For REST endpoint tasks, ask directly for endpoint-level output (for example: "show curl for PersonalKey -> JWT and upload dataset with private sharing URL").
- For Python SDK tasks, ask for PyGraphistry workflows (for example: "table to graph + plot with bindings + privacy").
- For mixed workflows, ask for both in one prompt;
graphistryroutes to the right specialist skill.
Run from a project where these skills are installed and graphistry + pandas are available.
export GRAPHISTRY_USERNAME="your_user"
export GRAPHISTRY_PASSWORD="your_pass"
export GRAPHISTRY_SERVER="hub.graphistry.com"
export GRAPHISTRY_PROTOCOL="https"
PROMPT='Using Bash tool calls, run (without creating files) a tiny PyGraphistry
cyber hunt demo (5-10 rows) with realistic devices/users/processes/ips/domains
and event entities that include explicit event_time timestamps, include node and
edge type fields, style with icons plus risk coloring, set
graphistry.privacy(mode='"'"'public'"'"', notify=False), call plot(render=False),
and print only the final live URL.'
claude -p \
--model opus \
--permission-mode bypassPermissions \
--tools Bash \
"$PROMPT"Sample output (validated on 2026-02-21, model=opus, runtime ~68.2s):
https://hub.graphistry.com/graph/graph.html?dataset=17743ba9ff3549729fdb4d9c1c071bbc&type=arrow&viztoken=e968954a-c0e5-4206-85a6-3d950817a6d4&usertag=ef9e6f8d-pygraphistry-0.50.6&splashAfter=1771659185&info=true
Example prompt:
Provide a concise Graphistry REST snippet that:
1) gets JWT via /api-token-auth/ from env vars,
2) uploads dataset via /api/v2/upload/datasets/ with private visibility,
3) prints graph.html?dataset=... URL.
These skills are regularly benchmarked and tuned against standard Graphistry user journeys (baseline vs skills, multiple runtimes/models).
For reproducible commands and sweep workflows, see DEVELOP.md.
Current checked-in benchmark packs show skills improving pass rates significantly:
| Skills ON | Skills OFF | Delta | |
|---|---|---|---|
| Pass rate | 45/63 (71%) | 12/63 (19%) | +52pp (3.7x) |
| Avg score | 0.90 | 0.66 | +0.24 |
| Avg latency | 23.4s | 46.6s | 2x faster |
| Regressions | 0 |
33 cases flip from fail to pass with skills. Zero cases regress.
- PyGraphistry suite (baseline isolation sweep,
codex,skills=both, 56 cases × 2):skills=on: 91% pass (51/56), avg47.4sskills=off: 52% pass (29/56), avg46.4s- Delta: +39pp pass rate improvement
- REST suite (phase2 full sweep,
codex, REST journeys,skills=both, 33 cases × 2):skills=on: 90.9% pass (30/33), avg13.0sskills=off: 27.3% pass (9/33), avg17.0s- Delta: +63.6pp pass rate improvement
- GFQL expansion suite (
claude, GFQL Cypher/Let/DAG/functional journeys,skills=both, 33 cases × 2):skills=on: 82% pass (27/33), avg score 0.95skills=off: 6% pass (2/33)- Delta: +76pp pass rate improvement
- Separate functional execution check (code actually runs with pygraphistry): 4/7 produce correct results
See:
- benchmarks/reports/2026-03-01-baseline-isolation-sweep.md - PyGraphistry suite benchmark
- benchmarks/reports/2026-03-07-rest-phase2-full-sweep.md - REST suite benchmark
- benchmarks/reports/2026-03-21-gfql-expansion.md - GFQL expansion benchmark
- benchmarks/README.md - full benchmark pack history