> ## Documentation Index
> Fetch the complete documentation index at: https://evalgate.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Install an evaluation pack

> Turn detailed first-party domain scenarios into a governed release gate.

# Install an evaluation pack

Evaluation packs give you realistic, versioned coverage without starting from
an empty dataset. EvalGate ships first-party packs for healthcare, legal,
support, coding, and financial agents. Every pack contains multi-step
scenarios, fictional source fixtures, criterion-level rubrics, adversarial
variants, and critical-failure release rules.

If you are starting from an existing codebase, first
[scan the repository](../repository-intelligence/scan-repository), review the
evidence-linked recommendations, and then choose a pack.

## Install from the web app

1. Open **Evaluation packs** at `/eval-packs`.
2. Filter by domain and inspect the pack's scenarios, evidence fixtures,
   criteria, and release-gate thresholds.
3. Select **Install pack**.
4. Open the created evaluation and run it against the candidate AI change.

Installation is atomic and retry-safe. EvalGate creates a published Dataset
Hub version, immutable row snapshots, an active domain-pack evaluation,
active-gate test cases, and a snapshot binding. It does not create a partial
evaluation if installation fails.

<Warning>
  First-party fixtures are fictional evaluation evidence. Packs help test a
  system; they are not legal, clinical, or financial advice and do not prove
  regulatory compliance by themselves.
</Warning>

## Install from the CLI

```bash theme={null}
evalgate packs list --domain legal
evalgate packs show legal-agent-work-product
evalgate packs install legal-agent-work-product \
  --name "Legal agent release gate" \
  --idempotency-key legal-gate-v1
evalgate packs installations
```

The TypeScript package exposes the same workflow through `client.evalPacks`.
The Python package exposes it through `client.eval_packs`.

## Interpret a result

Domain packs score each criterion with its declared deterministic, trajectory,
or semantic evaluator. Semantic criteria use the configured judge ensemble and
must satisfy both the score and agreement thresholds. A candidate fails the
release gate when a critical criterion fails, judge agreement is insufficient,
or the required scenario pass rate is missed.

## Next step

Run the created evaluation, inspect failing criteria and their cited fixtures,
then use that evidence in the release decision.