Agent Analytics
VerifiedRun analytics end-to-end from your agent without opening a dashboard. English-first workflow, with Chinese docs and content available. Create projects, ship...
$ Add to .claude/skills/ 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:
- Run `agent-analytics create my-site --domain https://mysite.com`
- Copy the returned snippet into the page before `</body>`
- Deploy
- 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?
What platforms support Agent Analytics?
What are the use cases for Agent Analytics?
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