---

name: queries description: "Routes TensorFlow Privacy users who need low-level DPQuery stacks, query composition, quantile clipping helpers, or tree aggregation mechanics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Queries

Use this sub-skill when the user needs to work directly with TensorFlow Privacy's DPQuery family or its tree-aggregation helpers.

Trigger phrases

• "custom DPQuery"
• "Gaussian query"
• "discrete Gaussian"
• "Skellam query"
• "normalized query"
• "nested query"
• "quantile adaptive clipping"
• "restart query"
• "tree aggregation"

What this sub-skill covers

• DPQuery and SumAggregationDPQuery
• Gaussian, discrete-Gaussian, distributed discrete-Gaussian, and Skellam query classes
• NoPrivacy* and NormalizedQuery helpers
• nested queries and restart indicators
• quantile estimator and quantile-adaptive clipping query classes
• tree aggregation query helpers and the lower-level tree_aggregation module

What it does not cover

• privacy budgets or epsilon/noise search -> ../privacy-accounting/
• training loops or model wrappers -> ../training/
• membership inference and secret-sharer analysis -> ../privacy-tests/
• fast gradient clipping internals -> ../fast-clipping/

Read this before you act

• references/api-reference.md for class constructors, helper signatures, and the safe query smoke pattern.
• references/troubleshooting.md for nesting, state, and restart failures.
• ../../references/install-and-scope.md for the minimum CPU runtime.

Typical workflow

1. Decide whether the user needs a turn-key query or a custom nested composition.
2. Pick the narrowest query class that matches the desired mechanism.
3. Use test_utils.run_query() when you need a tiny deterministic smoke check.
4. If the user is actually asking for privacy accounting, route them to privacy-accounting instead of building a query by hand.

Bundled helper

Run scripts/tiny_dp_query_smoke.py for a tiny deterministic query check. It exercises a Gaussian sum query and a no-privacy sum query on a scalar toy fixture.