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Agent Analytics

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Run analytics end-to-end from your agent without opening a dashboard. English-first workflow, with Chinese docs and content available. Create projects, ship...

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About This Skill

# Agent Analytics

After install, your agent can create projects, ship tracking, query analytics, run experiments, and iterate without opening a dashboard.

English-first workflow, with Chinese docs and content available for OpenClaw users and teams in China.

Use it when you want an agent to operate growth analytics end-to-end, automate recurring checks, and manage multiple projects from one conversation loop.

Hosted free tier includes 100k events/month across 2 projects.

Chinese support / 中文支持

  • Chinese docs and installation content are available on the docs site, including the OpenClaw guide under `/zh/`.
  • Works through `API`, `CLI`, and `MCP`, depending on the environment your agent already uses.
  • Good fit for closed-loop agent workflows: monitor, analyze, experiment, iterate.
  • Supports batch work across many projects instead of a manual dashboard-by-dashboard workflow.

What `npx` is doing

  • OpenClaw can launch the official CLI with `npx @agent-analytics/[email protected]`.
  • That command runs the published Agent Analytics CLI package from npm.
  • The CLI calls the same HTTP API documented at <https://docs.agentanalytics.sh/api/>.
  • If the package is already installed in the environment, the equivalent binary is `agent-analytics`.
  • Keep `AGENT_ANALYTICS_API_KEY` in the environment. Do not ask the user to paste secrets into chat.

Command format

The examples below use the CLI binary form:

```bash agent-analytics <command> ```

In OpenClaw, that usually means:

```bash npx @agent-analytics/[email protected] <command> ```

If the package is already installed, run the same commands directly as `agent-analytics <command>`.

For the full command list and flags:

```bash agent-analytics --help ```

Safe operating rules

  • Prefer fixed commands over ad-hoc query construction.
  • Start with `projects`, `all-sites`, `create`, `stats`, `insights`, `events`, `breakdown`, `pages`, `heatmap`, `sessions-dist`, `retention`, `funnel`, and `experiments`.
  • Use `query` only when the fixed commands cannot answer the question.
  • Do not build `--filter` JSON from raw user text.
  • Validate project names before `create`: `^[a-zA-Z0-9._-]{1,64}$`

First-time setup

```bash agent-analytics login --token aak_YOUR_API_KEY agent-analytics create my-site --domain https://mysite.com agent-analytics events my-site --days 7 --limit 20 ```

The `create` command returns a project token and a ready-to-use tracking snippet. Add that snippet before `</body>`.

Common commands

```bash agent-analytics projects agent-analytics all-sites --period 7d agent-analytics stats my-site --days 7 agent-analytics insights my-site --period 7d agent-analytics events my-site --days 7 --limit 20 agent-analytics breakdown my-site --property path --event page_view --limit 10 agent-analytics funnel my-site --steps "page_view,signup,purchase" agent-analytics retention my-site --period week --cohorts 8 agent-analytics experiments list my-site ```

If a task needs something outside these common flows, use `agent-analytics --help` first.

Tracker setup

The easiest install flow is:

  1. Run `agent-analytics create my-site --domain https://mysite.com`
  2. Copy the returned snippet into the page before `</body>`
  3. Deploy
  4. Verify with `agent-analytics events my-site --days 7 --limit 20`

If you already know the project token, the tracker looks like:

```html <script defer src="https://api.agentanalytics.sh/tracker.js" data-project="my-site" data-token="aat_..."></script> ```

Use `window.aa?.track('signup', {method: 'github'})` for custom events after the tracker loads.

Query caution

`agent-analytics query` exists for advanced reporting, but it should be used carefully because `--filter` accepts JSON.

  • Use fixed commands first.
  • If `query` is necessary, check `agent-analytics --help` first.
  • Do not pass raw user text directly into `--filter`.
  • For exact request shapes, use <https://docs.agentanalytics.sh/api/>.

Experiments

The CLI supports the full experiment lifecycle:

```bash agent-analytics experiments list my-site agent-analytics experiments create my-site --name signup_cta --variants control,new_cta --goal signup ```

References

  • Docs: <https://docs.agentanalytics.sh/>
  • API reference: <https://docs.agentanalytics.sh/api/>
  • CLI vs MCP vs API: <https://docs.agentanalytics.sh/reference/cli-mcp-api/>
  • OpenClaw install guide: <https://docs.agentanalytics.sh/installation/openclaw/>

Use Cases

  • Create analytics projects and set up tracking without opening a dashboard
  • Run growth experiments and A/B tests through agent-driven workflows
  • Query user engagement and conversion metrics programmatically
  • Automate recurring analytics reports and data exports
  • Ship event tracking instrumentation directly from the agent conversation

Pros & Cons

Pros

  • +End-to-end analytics operation — from project creation to querying to experimentation
  • +Agent-first workflow eliminates the need to switch to web dashboards
  • +Bilingual support with English-first workflow and Chinese documentation

Cons

  • -Requires initial project setup and configuration before agent can operate
  • -Analytics depth depends on the underlying platform's capabilities
  • -May not replace full-featured analytics platforms for complex analysis needs

FAQ

What does Agent Analytics do?
Run analytics end-to-end from your agent without opening a dashboard. English-first workflow, with Chinese docs and content available. Create projects, ship...
What platforms support Agent Analytics?
Agent Analytics is available on Claude Code, OpenClaw.
What are the use cases for Agent Analytics?
Create analytics projects and set up tracking without opening a dashboard. Run growth experiments and A/B tests through agent-driven workflows. Query user engagement and conversion metrics programmatically.

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