---

name: superduper description: "Use Superduper to build database-integrated AI applications, component workflows, listeners, vector indexes, and first-party plugin integrations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Superduper

Use this repo skill when a task names Superduper, superduper-framework, or APIs such as superduper(...), Datalayer, ObjectModel, Listener, VectorIndex, first-party superduper_* plugins, database-integrated AI apps, RAG-style component graphs, or vector search over backend records.

Before using this skill

• Install the base package with Python 3.10+:

  python -m pip install superduper-framework
  

• Install at least one data-backend plugin for real Datalayer work. For a no-service local smoke, install the MongoDB plugin and use mongomock://...:

  python -m pip install superduper_mongodb
  python - <<'PY'
  from superduper import superduper
  db = superduper("mongomock://skill-smoke", initialize_cluster=False, force_apply=True)
  print(type(db).__name__)
  PY
  

• Do not rely on the superduper console command for this source snapshot. The package metadata declares a console script, but this version lacks superduper.__main__; use the Python API and bundled helper scripts instead.

Route by task

Task or signal	Read
Connection strings, config files, SUPERDUPER_* env vars, backend URI mapping, Datalayer, Document, Schema, Table, query operations, or safe scratch DB checks	datalayer-and-config
Component, ObjectModel, custom callable models, Listener, Dataset, Metric, Validation, Trainer, Application, cron/Streamlit components, or RAG-like workflow composition	components-and-workflows
Embedding listeners, VectorIndex, vector datatypes, table.like(..., vector_index=...), compatible query listeners, local vector search, nearest-neighbor troubleshooting	vector-search-and-retrieval
Choosing/installing/importing superduper_* plugins for MongoDB, SQL, Snowflake, Redis, vector DBs, OpenAI/Anthropic/Cohere/Jina, Pillow, sklearn, Torch, Transformers, vLLM, or custom plugins	plugins-and-integrations

Shared references and scripts

• Read references/api-overview.md for the public import surface, verified signatures, backend URI map, and where each API family is covered.
• Read references/troubleshooting.md for cross-cutting install/import/plugin/backend/CLI/config failures.
• Read references/repo-provenance.md before deciding whether this skill is current for a checkout or package version.
• Run scripts/check_superduper_env.py for a safe import/version/plugin/CLI diagnostic that does not open network connections or install packages.

Operating rules

1. Prefer Python API snippets over CLI commands for this repo snapshot.
2. Choose the minimum plugin for the user's backend or model provider; do not install every plugin unless the user explicitly asks for broad optional coverage.
3. Treat cloud/API/vector DB/LLM/GPU plugin paths as optional until credentials, services, model weights, and hardware are explicitly available.
4. Use scratch mongomock://... or other user-approved test URIs for destructive checks. Never call db.drop(force=True, data=True) against user or production data.
5. If a task combines routes, start with setup (datalayer-and-config), then component/model wiring (components-and-workflows), then retrieval (vector-search-and-retrieval), and finally provider/backend-specific plugin details (plugins-and-integrations).