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

# Analytics

The **Analytics** dashboard provides engineering leaders and teams with end-to-end visibility into code review turnaround, defect prevention, and AI review efficacy. Access it anytime directly from the main workspace navigation under **Analytics**.

<Frame>
  <img src="https://mintcdn.com/blame-085dcc89/BdOAgOSo_6AxOKqS/images/Group-1-1.jpg?fit=max&auto=format&n=BdOAgOSo_6AxOKqS&q=85&s=83a4912cc6cda736268c9e6432a9c798" alt="Analytics DB" title="Analytics DB" lightAlt="Analytics DB" darkAlt="Analytics DB" width="2400" height="1260" data-path="images/Group-1-1.jpg" />
</Frame>

***

## Filters & Scoping

Use the top filter bar to drill down into specific teams, projects, or development cycles.

| Filter            | Options                                                     | Description                                                                 |
| :---------------- | :---------------------------------------------------------- | :-------------------------------------------------------------------------- |
| **Organizations** | All organizations or specific GitHub orgs                   | Scopes analytics across multi-tenant or multi-org installations.            |
| **Repositories**  | All repositories or multi-select specific repos             | Search and isolate metrics for individual services or monorepo subprojects. |
| **Authors**       | All authors or specific contributors                        | Track review volume and resolution metrics per developer.                   |
| **Timeframe**     | Last 24h, Last 7 days, Last 30 days, Last 90 days, All time | Dynamically adjusts all summary cards, time series charts, and tables.      |

<Tip>
  **Export Data:** Click the **Export** button in the filter bar to download the scoped analytics dataset as a timestamped `.json` file (`blame-analytics-{range}-{date}.json`) for external reporting or BI pipelines.
</Tip>

***

## KPI Summary Cards

The top of the dashboard highlights core efficiency, velocity, and code quality indicators:

***

## Trend Charts & Findings Breakdown

The interactive **Review Findings** chart tracks issue velocity and category distribution across your chosen timeframe:

```mermaid theme={null}
pie title Review Findings by Category
    "Security & Auth" : 25
    "Logic & Bugs" : 55
    "Performance" : 20
```

### Finding Categories

* **Security & Auth:** Injection vulnerabilities (SQLi, XSS), exposed secrets/API keys, authentication flaws, OWASP Top 10 vulnerabilities, and unsafe dependencies.
* **Logic & Bugs:** Runtime exceptions, unhandled `null`/`undefined` states, race conditions, broken control flow, and code quality antipatterns.
* **Performance:** N+1 database queries, unindexed lookups, memory leaks, unoptimized React re-renders, and inefficient algorithmic complexity.

***

## Pull Request Inspection & Actions

Below the charts, the **Pull Requests Feed** lists all scoped PRs with rich status indicators:

* **Tab Filters:** Filter between `All`, `Open`, `Draft`, and `Closed / Merged` PRs.
* **Review Badges:** Real-time evaluation status (`PASSED`, `WARNING`, `FAILED`).
* **Issue Counts:** Number of critical bugs and warnings detected per pull request.
* **Inline Actions:** Trigger manual reviews on-demand, convert open PRs to draft mode, or mark drafts as ready directly from the table.

***

## Using Analytics to Improve Code Quality

If authors are not resolving review comments, your repository guidelines may be too noisy or pedantic. Consider adjusting custom repository rules under **Repository Settings → Rules** or lowering nitpick strictness.

A sudden spike in security findings usually indicates newly introduced libraries or auth changes. Inspect the affected repository from the leaderboard and review recent dependency updates.

If Blame reviews complete in `< 1m` but PRs take days to merge, the bottleneck is human review turnaround or CI pipeline queue times. Use this data to streamline approval policies.