agent-analytics
- Repo stars 2,964
- Author updated Live
- Author repo buildwithclaude
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @davepoon · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Required · Vendor-specific
- Operating systems
- Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-analytics
description: Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize acro…
category: ai
runtime: no special runtime
---
# agent-analytics output preview
## PART A: Task fit
- Use case: Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to Use This Skill / Philosophy / First-time setup” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.”.
- **02** When the source has headings, the agent prioritizes “When to Use This Skill / Philosophy / First-time setup” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “When to Use This Skill / Philosophy / First-time setup”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-analytics
description: Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize acro…
category: ai
source: davepoon/buildwithclaude
---
# agent-analytics
## When to use
- Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects v…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to Use This Skill / Philosophy / First-time setup” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-analytics" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to Use This Skill / Philosophy / First-time setup
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Agent Analytics — Analytics your agent can actually use
You are adding analytics tracking using Agent Analytics — the analytics platform your AI agent can actually use. Built for developers who ship lots of projects and want their AI agent to track, analyze, experiment, and optimize across all of them.
Website: agentanalytics.sh GitHub: Agent-Analytics/agent-analytics Docs: docs.agentanalytics.sh
When to Use This Skill
- User wants to add analytics tracking to a website or app
- User wants to check how their projects are doing (traffic, conversions, engagement)
- User wants to run A/B experiments on headlines, CTAs, or flows
- User wants funnel analysis, retention cohorts, or traffic breakdowns
- User asks "how's my site doing?" or "are people visiting?"
Philosophy
You are NOT Mixpanel. Don't track everything. Track only what answers: "Is this project alive and growing?"
For a typical site, that's 3-5 custom events max on top of automatic page views.
First-time setup
Get an API key: Sign up at agentanalytics.sh and generate a key from the dashboard. Alternatively, self-host the open-source version from GitHub.
If the project doesn't have tracking yet:
# 1. Login (one time — uses your API key)
npx @agent-analytics/cli login --token aak_YOUR_API_KEY
# 2. Create the project (returns a project write token)
npx @agent-analytics/cli create my-site --domain https://mysite.com
# 3. Add the snippet using the returned token
# 4. Deploy, click around, verify:
npx @agent-analytics/cli events my-site
The create command returns a project write token — use it as data-token in the snippet. This is separate from your API key (which is for reading/querying).
Step 1: Add the tracking snippet
The create command returns a tracking snippet with your project token — add it before </body>. It auto-tracks page_view events with path, referrer, browser, OS, device, screen size, and UTM params. You do NOT need to add custom page_view events.
Step 1b: Discover existing events (existing projects)
If tracking is already set up, check what events and property keys are already in use so you match the naming:
npx @agent-analytics/cli properties-received PROJECT_NAME
Step 2: Add custom events to important actions
Use onclick handlers on the elements that matter:
<a href="..." onclick="window.aa?.track('EVENT_NAME', {id: 'ELEMENT_ID'})">
Standard events for 80% of SaaS sites
Pick the ones that apply. Most sites need 2-4:
| Event | When to fire | Properties |
|---|---|---|
cta_click |
User clicks a call-to-action button | id (which button) |
signup |
User creates an account | method (github/google/email) |
login |
User returns and logs in | method |
feature_used |
User engages with a core feature | feature (which one) |
checkout |
User starts a payment flow | plan (free/pro/etc) |
error |
Something went wrong visibly | message, page |
What NOT to track
- Every link or button (too noisy)
- Scroll depth (not actionable)
- Form field interactions (too granular)
- Footer links (low signal)
Property naming rules
- Use
snake_case:hero_get_startednotheroGetStarted - The
idproperty identifies WHICH element: short, descriptive - Name IDs as
section_action:hero_signup,pricing_pro,nav_dashboard
Step 2b: Run A/B experiments
Experiments let you test which variant of a page element converts better. The full lifecycle is API-driven — no dashboard UI needed.
Creating an experiment
npx @agent-analytics/cli experiments create my-site \
--name signup_cta --variants control,new_cta --goal signup
Implementing variants
Declarative (recommended): Use data-aa-experiment and data-aa-variant-{key} HTML attributes. Original content is the control. The tracker swaps text for assigned variants automatically.
<h1 data-aa-experiment="signup_cta" data-aa-variant-new_cta="Start Free Trial">Sign Up</h1>
Programmatic (complex cases): Use window.aa?.experiment(name, variants) — deterministic, same user always gets same variant.
Checking results
npx @agent-analytics/cli experiments get exp_abc123
Returns Bayesian probability_best, lift, and a recommendation. The system needs ~100 exposures per variant before results are significant.
Step 3: Test immediately
After adding tracking, verify it works:
# Click around, then check:
npx @agent-analytics/cli events PROJECT_NAME
# Events appear within seconds.
CLI Reference
All commands use npx @agent-analytics/cli:
# Setup
login --token aak_YOUR_KEY # Save API key (one time)
projects # List all projects
create my-site --domain https://... # Create project
# Real-time
live # Live TUI dashboard across ALL projects
live my-site # Live view for one project
# Analytics
stats my-site --days 7 # Overview: events, users, daily trends
insights my-site --period 7d # Period-over-period comparison
breakdown my-site --property path --event page_view --limit 10 # Top pages/referrers/UTM
pages my-site --type entry # Landing page performance & bounce rates
sessions-dist my-site # Session engagement histogram
heatmap my-site # Peak hours & busiest days
events my-site --days 30 # Raw event log
sessions my-site # Individual session records
properties my-site # Discover event names & property keys
funnel my-site --steps "page_view,signup,purchase" # Funnel drop-off
retention my-site --period week --cohorts 8 # Cohort retention
# A/B experiments
experiments list my-site
experiments create my-site --name signup_cta --variants control,new_cta --goal signup
experiments get exp_abc123
experiments complete exp_abc123 --winner new_cta
Which endpoint for which question
| User asks | Call | Why |
|---|---|---|
| "How's my site doing?" | insights + breakdown + pages (parallel) |
Full weekly picture |
| "Is anyone visiting right now?" | live |
Real-time visitors across all projects |
| "What are my top pages?" | breakdown --property path --event page_view |
Ranked page list |
| "Where's my traffic coming from?" | breakdown --property referrer --event page_view |
Referrer sources |
| "Are people actually engaging?" | sessions-dist |
Bounce vs engaged split |
| "When should I deploy?" | heatmap |
Find low-traffic windows |
| "Where do users drop off?" | funnel --steps "page_view,signup,purchase" |
Step-by-step conversion |
| "Are users coming back?" | retention --period week --cohorts 8 |
Cohort retention |
| "Which CTA converts better?" | experiments create + experiments get |
A/B test lifecycle |
For any "how is X doing" question, always call insights first — it's the single most useful endpoint.
Examples
Track custom events via window.aa?.track():
window.aa?.track('cta_click', {id: 'hero_get_started'});
window.aa?.track('signup', {method: 'github'});
window.aa?.track('feature_used', {feature: 'create_project'});
window.aa?.track('checkout', {plan: 'pro'});
What this skill does NOT do
- No GUI dashboards — your agent IS the dashboard (or use
livefor a real-time TUI) - No user management or billing
- No PII stored — IP addresses are not logged or retained. Privacy-first by design
Decide Fit First
Design Intent
How To Use It
Boundaries And Review