Agent Analytics Skills

Public skill repo for installing Agent Analytics skills into agent environments with:

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

List the available skills without installing:

npx skills add Agent-Analytics/agent-analytics-skill --list

Install one skill explicitly:

npx skills add Agent-Analytics/agent-analytics-skill --skill agent-analytics
npx skills add Agent-Analytics/agent-analytics-skill --skill agent-analytics-autoresearch

This repo intentionally contains only public skill definitions. It does not include the private MCP server implementation or deployment configuration.

Skills

agent-analytics

The regular Agent Analytics skill teaches an agent how to use the official Agent Analytics CLI to:

• create projects
• install tracking
• inspect traffic and events
• connect entry pages, exit pages, and goals with bounded session paths
• analyze funnels and retention
• store compact project goals, activation events, and event-name glossary context
• keep that context as a per-project, self-improving memory after scans, instrumentation, analysis, and human corrections
• scan additional public websites the user owns so the agent can find useful data that is not being collected yet
• read existing context before analysis, merge before writes, and save only durable product truth instead of noisy metric findings
• run experiments

Use this when you want the agent to operate Agent Analytics end-to-end.

agent-analytics-autoresearch

The Autoresearch skill teaches an agent how to run a structured growth loop for landing pages, onboarding copy, pricing pages, and experiment candidates.

It can use any analytics data: Agent Analytics, PostHog, GA4, Mixpanel, SQL, CSV exports, product logs, or a written data brief. It works best with Agent Analytics because the agent can pull fresh snapshots through the CLI/API, inspect funnels and events, and use experiment results in the next run.

When using Agent Analytics data, Autoresearch should read Project Context before a snapshot and feed back only durable product truth after human correction or measured evidence. It should not store noisy round notes, weekly numbers, or temporary spikes as project memory.

Use this when you want the agent to:

• collect or read an analytics snapshot
• preserve product truth and drift constraints
• generate, critique, synthesize, and blind-rank variants
• output two review-ready variants for an A/B test
• rerun after live experiment data arrives

The workflow is based on the public template repo:

• Autoresearch Growth: https://github.com/Agent-Analytics/autoresearch-growth

CLI Pinning

The skill is intentionally pinned to the official CLI invocation:

npx --yes @agent-analytics/cli@0.5.21 <command>

Agent environments should prefer that exact npx form over raw API calls, repo-local scripts, or an already-installed binary unless the user explicitly asks for a different interface.

The published skill now teaches the authenticated CLI setup flow:

Set up Agent Analytics for this project. If browser approval is needed, open it and wait for me. I will sign in with Google or GitHub and approve it. Then create or identify the matching Agent Analytics project, run website analysis for this site so you know what my agent should track first, install only the high-priority minimum viable instrumentation, and verify the first useful recommended event.

For deeper product analysis, the skill should also scan additional public websites the user owns, such as docs, pricing, support, signup, changelog, launch, or demo pages. The scanner is not only a way to feed the agent existing analytics data; it gives the agent product eyes on data that may not be collected yet.

Anonymous website-analysis preview is available on the public web scanner:

https://agentanalytics.sh/analysis/

In CLI and agent runtimes, the setup flow signs in first, then creates or identifies the matching project before running website analysis. Authenticated scans must name a project whose configured domain matches the scanned hostname:

npx --yes @agent-analytics/cli@0.5.21 login --detached
npx --yes @agent-analytics/cli@0.5.21 create <project> --domain <url>
npx --yes @agent-analytics/cli@0.5.21 scan <url> --project <project> --json

The skill uses the analysis output as analytics judgment: install only high-priority minimum_viable_instrumentation, explain what each event enables, and avoid generic tracking. When multiple owned surfaces are scanned, compare current_blindspots and minimum_viable_instrumentation before choosing what to instrument first.

Recommendations include practical implementation_hint guidance. Agents should map those hints to tracker.js capabilities instead of inventing generic instrumentation: use data-aa-event for named click intent, data-aa-impression for meaningful section exposure, window.aa.track(...) for computed client state, and server-side tracking for durable outcomes such as completed signup. Do not add custom duplicates for automatic tracker signals like page_view, path, referrer, UTMs, device/browser fields, country, session IDs, session count, days since first visit, or first-touch attribution.

For OpenClaw and similar managed runtimes, store CLI auth in a persistent workspace path instead of the default home config path:

export AGENT_ANALYTICS_CONFIG_DIR="$PWD/.openclaw/agent-analytics"
npx --yes @agent-analytics/cli@0.5.21 auth status

Normal setup, paid upgrade, and resumed agent work do not require an API key.

When a free account reaches a Pro-only analytics command, the skill should run the blocked command first, then use:

npx --yes @agent-analytics/cli@0.5.21 upgrade-link --detached --reason "<why Pro is needed>" --command "<blocked command>"

The CLI prints an app-domain payment handoff for the human. The dashboard page may ask the human to sign in, confirms the same account as the CLI, shows the blocked command and reason, and then opens Lemon Squeezy. The agent should run whoami after payment, then rerun the blocked command once Pro is active.

For direct MCP setup, use the install guides on the docs site.

Related

• Docs: https://docs.agentanalytics.sh/
• Session paths guide: https://docs.agentanalytics.sh/guides/session-paths/
• API reference: https://docs.agentanalytics.sh/api/
• OpenAPI spec: https://docs.agentanalytics.sh/openapi.yaml
• CLI package: https://github.com/Agent-Analytics/agent-analytics-cli
• Regular Agent Analytics skill: https://github.com/Agent-Analytics/agent-analytics-skill/tree/main/skills/agent-analytics
• Autoresearch Growth template: https://github.com/Agent-Analytics/autoresearch-growth
• MCP server docs: https://docs.agentanalytics.sh/installation/