> ## 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.

# Scan a repository for AI systems

> Build an exact-commit evidence graph and find missing evaluation coverage.

# Scan a repository for AI systems

Repository Intelligence reads a connected GitHub repository at one immutable
commit, detects source-backed AI components, and recommends evaluation packs
for uncovered risks. It never executes repository code.

## Scan from the web app

1. Connect the repository through the EvalGate GitHub App.
2. Open **Repository intelligence** at `/repository-intelligence`.
3. Select the connected repository and start a scan. You can pin a 40-character
   commit SHA or scan the current default-branch head.
4. Review detected providers, model routes, agents, prompts, tools and MCP,
   retrieval and memory, guardrails, evaluation frameworks, telemetry, domain
   signals, and release assets.
5. Inspect every finding's exact file and line evidence before installing a
   recommended pack.

<Note>
  A source detection is not runtime confirmation. The result records graph
  completeness and truncation reasons so a partial scan cannot be presented as
  complete evidence.
</Note>

## Scan and ask from the CLI

Configure an EvalGate API key and connect GitHub before running this
authenticated workflow:

```bash theme={null}
evalgate repo repositories
evalgate repo scan --repository 42 --head-sha <40-character-sha> \
  --idempotency-key scan-42-release
evalgate repo scans --repository 42
evalgate repo show --repository 42 --scan <scan-id>
evalgate repo ask --repository 42 \
  --question "What AI models, agents, tools, and evals exist?" \
  --graph-version <graph-version-id>
```

The TypeScript package exposes the same workflow through
`client.repositoryIntelligence`. The Python package exposes it through
`client.repository_intelligence`.

Bot answers are bounded to one persisted graph version. Answers cite source
locators when evidence exists and return an explicit insufficient-evidence
state when it does not.

## Turn a finding into coverage

Open a recommended pack, review why its detector evidence caused the
recommendation, install it, and run the resulting active release gate. This
connects the repository commit to source evidence, evaluation coverage, and the
release decision.

Continue with [Install an evaluation pack](../evaluation-packs/install-pack)
after reviewing the recommendation and its source evidence.