Skip to content
Agent Analytics

Agent Analytics Skill

Use the regular Agent Analytics skill when you want your AI agent to operate Agent Analytics end-to-end from the project it is already editing.

It is the right first skill for setup, tracking, reporting, funnels, session paths, and normal experiment work. If you want the agent to generate and judge growth variants before an A/B test, use the Autoresearch Growth Skill after the basic analytics loop works.

Install

Section titled “Install”

Install from the public skill repo:

Terminal window
npx skills add Agent-Analytics/agent-analytics-skill

If the installer asks which skill to install, choose agent-analytics.

You can also install it explicitly:

Terminal window
npx skills add Agent-Analytics/agent-analytics-skill --skill agent-analytics

The source is public:

What To Ask Your Agent

Section titled “What To Ask Your Agent”

Start in the codebase or site you want to measure, then ask:

Set up Agent Analytics for this project. Run the website analysis first so you know what my agent should track first. Install it here if needed. Open the browser for me or give me a login link, then wait. I will sign in with Google or GitHub, approve it, and paste back any finish code if you need it. Then create the project, install only the high-priority recommended events, explain what each event enables, and verify the first useful event.

After setup, ask normal analytics questions in plain English:

How did this site perform in the last 7 days?
Show the funnel from page_view to signup_cta_click to signup.
Create an experiment for the signup CTA with control and a clearer variant, then show me how to QA both versions before it gets traffic.

What The Skill Does

Section titled “What The Skill Does”

The skill teaches the agent to use the official Agent Analytics CLI and API patterns without making you hand-write requests.

Typical jobs:

  • analyze the public site before installing custom events
  • create or find the right project
  • install tracker.js
  • add only the first useful declarative events such as CTA clicks
  • verify the first useful recommended event
  • inspect pages, events, funnels, retention, and bot traffic
  • create, QA, measure, and complete experiments
  • explain gaps in the data before recommending action

Browser approval is the normal login path. You do not need to create an API key first unless you are building a custom direct HTTP integration.

For OpenClaw and similar managed runtimes, tell the agent to keep Agent Analytics CLI auth in a persistent workspace path instead of the default home config path:

Terminal window
export AGENT_ANALYTICS_CONFIG_DIR="$PWD/.openclaw/agent-analytics"
npx @agent-analytics/[email protected] auth status

If the runtime may not preserve exported variables between commands, prefix each Agent Analytics CLI command with that same AGENT_ANALYTICS_CONFIG_DIR=... value or pass --config-dir "$PWD/.openclaw/agent-analytics". Do not commit .openclaw/agent-analytics/config.json.

When To Use The Autoresearch Skill Instead

Section titled “When To Use The Autoresearch Skill Instead”

Use Autoresearch Growth Skill when the task is not just “read analytics” or “create an experiment,” but:

  • generate landing-page, onboarding, pricing, or CTA variants
  • critique generic copy and product-truth drift
  • blind-rank multiple candidates
  • output two review-ready experiment variants
  • rerun the loop after experiment data comes back

In practice, the regular skill gets the analytics foundation working. The autoresearch skill uses that foundation to run a structured growth loop.

Section titled “Related”