# Agent Analytics > Simple website analytics your AI agent controls end-to-end. Track page views, events, funnels, retention, and A/B experiments across all your projects. Use when: adding website tracking, checking site traffic, setting up conversion funnels, running A/B experiments, or replacing Mixpanel / Plausible / PostHog with something lightweight and agent-operated. No dashboard needed. - Skill: `johnalbertini14-glitch/agent-analytics` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add johnalbertini14-glitch/agent-analytics` - Raw SKILL.md: https://api.skillmd.com/api/skills/johnalbertini14-glitch/agent-analytics/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: johnalbertini14-glitch (https://skillmd.com/u/johnalbertini14-glitch) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/johnalbertini14-glitch/agent-analytics --- # Agent Analytics — Website analytics your AI agent fully operates Simple, privacy-first website analytics and growth toolkit that your AI agent controls end-to-end. Track page views, custom events, conversion funnels, user retention, and A/B experiments across all your projects — then talk to your analytics in natural language. No dashboards. Your agent creates projects, adds tracking code, queries traffic data, builds funnels, runs experiments, and tells you what to optimize next. A lightweight Plausible/Mixpanel/PostHog alternative built for the AI agent era. ## Security & trust - **Open source**: Full source at [github.com/Agent-Analytics/agent-analytics-cli](https://github.com/Agent-Analytics/agent-analytics-cli) — inspect every command before running - **Read-only by default**: The CLI only reads analytics data. Write operations (creating projects, experiments) require explicit user-provided API keys - **No arbitrary code execution**: All CLI commands use structured flags (`--days`, `--property`, `--steps`). No eval, no shell interpolation, no dynamic code generation - **Scoped permissions**: The API key controls access. The CLI never requests filesystem, network, or system-level permissions beyond HTTP calls to `api.agentanalytics.sh` - **Published on npm**: [@agent-analytics/cli](https://www.npmjs.com/package/@agent-analytics/cli) — versioned, auditable, standard npm supply chain ## 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](https://agentanalytics.sh) and generate a key from the dashboard. Alternatively, self-host the open-source version from [GitHub](https://github.com/Agent-Analytics/agent-analytics). If the project doesn't have tracking yet: ```bash # 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 (Step 1 below) 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 below. 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 ``. 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: ```bash npx @agent-analytics/cli properties-received PROJECT_NAME ``` This shows which property keys each event type uses (e.g. `cta_click → id`, `signup → method`). Match existing naming before adding new events. ## Step 2: Add custom events to important actions Use `onclick` handlers on the elements that matter: ```html ``` The `?.` operator ensures no error if the tracker hasn't loaded yet. ### 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 to track as `cta_click` Only buttons that indicate conversion intent: - "Get Started" / "Sign Up" / "Try Free" buttons - "Upgrade" / "Buy" / pricing CTAs - Primary navigation to signup/dashboard - "View on GitHub" / "Star" (for open source projects) ### 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_started` not `heroGetStarted` - The `id` property identifies WHICH element: short, descriptive - Name IDs as `section_action`: `hero_signup`, `pricing_pro`, `nav_dashboard` - Don't encode data the page_view already captures (path, referrer, browser) ## Step 2b: Run A/B experiments (Pro) 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 ```bash 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. ```html

Sign Up

` — comparison period: `1d`, `7d`, `14d`, `30d`, `90d` (`insights`) or cohort grouping: `day`, `week`, `month` (`retention`) - `--property ` — property key to group by (`breakdown`, required) - `--event ` — filter by event name (`breakdown`) or first-seen event filter (`retention`) - `--returning-event ` — what counts as "returned" (`retention`, defaults to same as `--event`) - `--cohorts ` — number of cohort periods, 1-30 (`retention`, default: 8) - `--type ` — page type: `entry`, `exit`, `both` (`pages` only, default: entry) - `--steps ` — comma-separated event names, 2-8 steps max (`funnel`, required) - `--window ` — conversion window in hours (`funnel`, default: 168) or live time window in seconds (`live`, default: 60) - `--count-by ` — `user_id` or `session_id` (`funnel` only) - `--breakdown ` — segment funnel by a property (e.g. `country`, `variant`) — extracted from step 1 events (`funnel` only) - `--breakdown-limit ` — max breakdown groups, 1-50 (`funnel`, default: 10) - `--interval ` — live refresh in seconds (default: 5) ### The `live` command `npx @agent-analytics/cli live` opens a real-time TUI dashboard that refreshes every 5 seconds. It shows active visitors, sessions, and events/min across all your projects, plus top pages and recent events. Note: this is an interactive terminal UI — it clears the screen on each refresh, so it works best when run directly in a terminal rather than captured as output. ## Which endpoint for which question Match the user's question to the right call(s): | User asks | Call | Why | |-----------|------|-----| | "How's my site doing?" | `insights` + `breakdown` + `pages` (parallel) | Full weekly picture in one turn | | "Is anyone visiting right now?" | `live` | Real-time visitors, sessions, events across all projects | | "Is anyone visiting?" | `insights --period 7d` | Quick alive-or-dead check | | "What are my top pages?" | `breakdown --property path --event page_view` | Ranked page list with unique users | | "Where's my traffic coming from?" | `breakdown --property referrer --event page_view` | Referrer sources | | "Which landing page is best?" | `pages --type entry` | Bounce rate + session depth per page | | "Are people actually engaging?" | `sessions-dist` | Bounce vs engaged split | | "When should I deploy/post?" | `heatmap` | Find low-traffic windows or peak hours | | "Give me a summary of all projects" | `live` or loop: `projects` then `insights` per project | Multi-project overview | | "Which CTA converts better?" | `experiments create` + implement + `experiments get ` | Full A/B test lifecycle | | "Where do users drop off?" | `funnel --steps "page_view,signup,purchase"` | Step-by-step conversion with drop-off rates | | "Which variant converts better through the funnel?" | `funnel --steps "page_view,signup" --breakdown variant` | Funnel segmented by experiment variant | | "Are users coming back?" | `retention --period week --cohorts 8` | Cohort retention: % returning per period | For any "how is X doing" question, **always call `insights` first** — it's the single most useful endpoint. For real-time "who's on the site right now", use `live`. ## Analyze, don't just query Don't return raw numbers. Interpret them. Here's how to turn each endpoint's response into something useful. ### `/insights` → The headline API returns metrics with `current`, `previous`, `change`, `change_pct`, and a `trend` field. **How to interpret:** - `change_pct > 10` → "Growing" — call it out positively - `change_pct` between -10 and 10 → "Stable" — mention it's steady - `change_pct < -10` → "Declining" — flag it, suggest investigating - `bounce_rate` current vs previous → say "improved" (went down) or "worsened" (went up) - `avg_duration` → convert ms to seconds: `Math.round(value / 1000)` - Previous period is all zeros → say "new project, no prior data to compare" **Example output:** ``` This week vs last: 173 events (+22%), 98 users (+18%). Bounce rate: 87% (up from 82% — getting worse). Average session: 24s. Trend: growing. ``` ### `/breakdown` → The ranking API returns `values: [{ value, count, unique_users }]` sorted by count DESC. **How to interpret:** - Top 3-5 values is enough — don't dump the full list - Show the `unique_users` too — 100 events from 2 users is very different from 100 events from 80 users - Use `total_with_property / total_events` to note coverage: "155 of 155 page views have a path" - For referrers: group "(direct)" / empty as direct traffic **Example output:** ``` Top pages: / (98 views, 75 users), /pricing (33 views, 25 users), /docs (19 views, 4 users). The /docs page has high repeat visits (19 views, 4 users) — power users. ``` ### `/pages` → Landing page quality API returns `entry_pages: [{ page, sessions, bounces, bounce_rate, avg_duration, avg_events }]`. **How to interpret:** - `bounce_rate` > 0.7 → "high bounce, needs work above the fold" - `bounce_rate` < 0.3 → "strong landing page" - `avg_duration` → convert ms to seconds; < 10s is concerning, > 60s is great - `avg_events` → pages/session; 1.0 means everyone bounces, 3+ means good engagement - Compare pages: "Your /pricing page converts 3× better than your homepage" **Example output:** ``` Best landing page: /pricing — 14% bounce, 62s avg session, 4.1 pages/visit. Worst: /blog/launch — 52% bounce, 18s avg. Consider a stronger CTA above the fold. ``` ### `/sessions/distribution` → Engagement shape API returns `distribution: [{ bucket, sessions, pct }]`, `engaged_pct`, `median_bucket`. **How to interpret:** - `engaged_pct` is the key number — sessions ≥30s as a percentage of total - `engaged_pct` < 10% → "Most visitors leave immediately — focus on first impressions" - `engaged_pct` 10-30% → "Moderate engagement, room to improve" - `engaged_pct` > 30% → "Good engagement" - If 80%+ is in the "0s" bucket, the site has a bounce problem - If there's a healthy spread across buckets, engagement is genuine **Example output:** ``` 88% of sessions bounce instantly (0s). Only 6% stay longer than 30s. The few who do engage stay 3-10 minutes — the content works, but first impressions don't. ``` ### `/heatmap` → Timing API returns `heatmap: [{ day, day_name, hour, events, users }]`, `peak`, `busiest_day`, `busiest_hour`. **How to interpret:** - `peak` is the single busiest slot — mention day + hour + timezone caveat (times are UTC) - `busiest_day` → "Schedule blog posts/launches on this day" - `busiest_hour` → "This is when your audience is online" - Low-traffic windows → "Deploy during Sunday 3 AM UTC to minimize user impact" - Weekend vs weekday split → tells you if audience is B2B (weekdays) or B2C (weekends) **Example output:** ``` Peak: Friday at 11 PM UTC (35 events, 33 users). Busiest day overall: Sunday. Traffic is heaviest on weekends — your audience browses on personal time. Deploy on weekday mornings for minimal disruption. ``` ### `/funnel` → Where users drop off CLI: `funnel my-site --steps "page_view,signup,purchase"`. API: `POST /funnel` with JSON body. API returns `steps: [{ step, event, users, conversion_rate, drop_off_rate, avg_time_to_next_ms }]` and `overall_conversion_rate`. **How to interpret:** - Each step shows how many users progressed from the previous step - `conversion_rate` is step-to-step (step 2 users / step 1 users) - `drop_off_rate` is 1 - conversion_rate at each step - The biggest `drop_off_rate` is the bottleneck — focus optimization there - `avg_time_to_next_ms` shows how long users take between steps (convert to hours/minutes) - `overall_conversion_rate` is end-to-end (last step users / first step users) **Options:** - `--steps "event1,event2,event3"` — 2-8 step events (required) - `--window ` — max time from step 1 to last step (default: 168 = 7 days) - `--since ` — lookback period, e.g. `30d` (default: 30d) - `--count-by ` — `user_id` (default) or `session_id` - `--breakdown ` — segment funnel by a property (e.g. `country`, `variant`). Property is extracted from step 1 events. Returns overall + per-group results. - `--breakdown-limit ` — max groups returned (default: 10, max: 50). Groups ordered by step 1 users descending. **Breakdown use case — A/B experiments:** `funnel my-site --steps "page_view,signup" --breakdown variant` shows which experiment variant converts better through the funnel. **API-only: per-step filters** — each step can have a `filters` array with `{ property, op, value }` (ops: `eq`, `neq`, `contains`). Example: filter step 1 to `path=/pricing` to see conversions from the pricing page specifically. **Example output:** ``` page_view → signup → purchase 500 users → 80 (16%) → 12 (15%) — 2.4% overall Biggest drop-off: page_view → signup (84%). Focus on signup CTA visibility. Avg time to signup: 4.2 hours. Avg time to purchase: 2.1 days. ``` ### `/retention` → Are users coming back? CLI: `retention my-site --period week --cohorts 8`. API: `GET /retention?project=X&period=week&cohorts=8`. By default uses session-based retention — a user is "retained" if they have any return visit (session) in a subsequent period. Pass `--event` to switch to event-based retention. API returns `cohorts: [{ date, users, retained: [...], rates: [...] }]`, `average_rates: [...]`, and `users_analyzed`. **How to interpret:** - Each cohort row = users who first appeared in that period - `rates[0]` is always 1.0 (100% — the cohort itself) - `rates[1]` = % who came back the next period — this is the critical number - Declining rates across offsets is normal; the slope matters more than absolutes - `average_rates` is weighted by cohort size — larger cohorts count more - Compare recent cohorts vs older ones: improving rates = product is getting stickier **Options:** - `--period

` — `day`, `week`, `month` (default: week) - `--cohorts ` — number of cohort periods, 1-30 (default: 8) - `--event ` — first-seen event filter (e.g. `signup`). Switches to event-based retention - `--returning-event ` — what counts as "returned" (defaults to same as `--event`) **Event-based retention:** Set `--event signup --returning-event purchase` to answer "of users who signed up, what % made a purchase in subsequent weeks?" **Example output:** ``` Cohort W0 (2026-01-27): 142 users → W1: 45% → W2: 39% → W3: 32% Cohort W0 (2026-02-03): 128 users → W1: 42% → W2: 36% Weighted avg: W1 = 44%, W2 = 37%, W3 = 32% Week-1 retention of 44% is strong — nearly half of new users return. Slight decline in recent cohorts — investigate onboarding changes. ``` ### Weekly summary recipe (3 parallel calls) Call `insights`, `breakdown --property path --event page_view`, and `pages --type entry` in parallel, then synthesize into one response: ``` Weekly Report — my-site (Feb 8–15 vs Feb 1–8) Events: 1,200 (+22% ↑) Users: 450 (+18% ↑) Bounce: 42% (improved from 48%) Top pages: /home (523), /pricing (187), /docs (94) Best landing: /pricing — 14% bounce, 62s avg. Worst: /blog — 52% bounce. Trend: Growing. ``` ### Multi-project overview For a quick real-time check, use `live` — it shows all projects in one view with active visitors, sessions, and events/min. For a historical summary, call `projects` to list all projects, then call `insights --period 7d` for each. Present one line per project: ``` my-site 142 views (+23% ↑) 12 signups healthy side-project 38 views (-8% ↓) 0 signups quiet api-docs 0 views (—) — ⚠ inactive since Feb 1 ``` Use arrows: `↑` up, `↓` down, `—` flat. Flag anything that needs attention. ### Anomaly detection Proactively flag — don't wait to be asked: - **Spike**: any metric >2× its previous period → "unusual surge, check referrers" - **Drop**: any metric <50% of previous → "significant decline, worth investigating" - **Dead project**: zero `page_view` events → "⚠ no traffic detected" - **Errors**: any `error` events in the window → surface the `message` property ### Visualizing results When reporting to messaging platforms (Slack, Discord, Telegram), raw text tables break. Use companion skills: - **`table-image-generator`** — render stats as clean table images - **`chart-image`** — generate line, bar, area, or pie charts from analytics data ## Growth Playbook — How to grow, not just track Tracking is step one. Growth comes from a **repeatable system**: clear messaging → focused distribution → obsessive tracking → rapid experimentation → learning. Here's how to apply each principle using Agent Analytics. ### Principle 1: Promise clarity The #1 conversion lever is messaging. If someone lands and has to think hard to understand the value, they're gone. **What your agent should do:** - Set up an A/B experiment on the hero headline immediately: `experiments create PROJECT --name hero_headline --variants control,b,c --goal cta_click` - Test 2-3 headline variations that frame the same value differently - Use declarative HTML: `data-aa-experiment="hero_headline" data-aa-variant-b="New headline"` - Check results after ~500 visitors per variant: `experiments get EXP_ID` - Ship the winner, start testing the subtitle or CTA next **Rule:** Spend more time testing messaging than adding features. Even the best product won't convert if the value isn't obvious in seconds. ### Principle 2: Track what drives decisions, not everything Don't be Mixpanel. Track only what answers: **"Is this project alive and growing, and what should I do next?"** **The essential events (pick 3-5):** | Event | What it tells you | |-------|-------------------| | `cta_click` (with `id`) | Which buttons drive action — your conversion signal | | `signup` | Are people converting? At what rate? | | `feature_used` (with `feature`) | Are they finding value after signup? | | `checkout` | Revenue signal | **Agent workflow for tracking setup:** 1. Look at the site — identify the 2-3 most important user actions 2. Add tracking on those specific actions (not everything) 3. Verify with `events PROJECT` that data flows 4. Set up a weekly check: `insights PROJECT --period 7d` **Anti-pattern:** Don't track scroll depth, mouse hovers, every link click, or form field interactions. Noise kills signal. ### Principle 3: Find the activation moment Conversion doesn't happen at checkout. It happens when the user realizes the product solves their problem — the "aha moment." **How to find it:** 1. Track key feature interactions: `feature_used` with specific feature names 2. Use `breakdown --property feature --event feature_used` to see which features correlate with retention 3. Check `sessions-dist` — if most sessions are 0s bounces, the landing page is the problem. If sessions are long but signups are low, the activation path is the problem 4. Use `pages --type entry` — compare bounce rates across landing pages to find which first impression works **What to optimize:** - Time to first value — how fast does the user get a result? - Onboarding friction — where do users drop off? - Feature discovery — are users finding the thing that makes them stay? ### Principle 4: One channel, iterate relentlessly Don't try to be everywhere. Pick one acquisition channel and go deep. **How Agent Analytics supports this:** - `breakdown --property referrer --event page_view` → see where traffic actually comes from - `breakdown --property utm_source` → track campaign sources - `insights --period 7d` → week-over-week: is the channel growing? - Create landing page variants per channel (e.g., `/reddit/`, `/hn/`) and compare with `pages --type entry` **Agent workflow for channel optimization:** 1. Check referrer breakdown weekly 2. Identify the top-performing channel (highest traffic + lowest bounce) 3. Double down: create content, run experiments on that channel's landing page 4. Ignore channels that aren't working — focus beats breadth ### Principle 5: The autonomous growth loop This is what makes Agent Analytics different from traditional analytics. Your agent can run the full cycle: ``` Track → Analyze → Experiment → Ship winner → Repeat ``` **The loop in practice:** 1. **Track**: Agent sets up tracking on CTAs and key actions 2. **Analyze**: Weekly `insights` + `breakdown` + `pages` calls → synthesize into a report 3. **Hypothesize**: "Hero headline has 87% bounce — test a clearer value prop" 4. **Experiment**: `experiments create PROJECT --name hero_v2 --variants control,b --goal cta_click` 5. **Monitor**: Check `experiments get EXP_ID` after sufficient traffic 6. **Ship**: `experiments complete EXP_ID --winner b` → deploy the winner 7. **Repeat**: Start the next experiment on the next weakest element **What to test (in order of impact):** 1. Hero headline — biggest impact on bounce rate 2. CTA button text — directly affects conversion 3. Social proof / trust signals — affects signup confidence 4. Pricing presentation — affects revenue 5. Onboarding flow — affects activation **Cadence:** One experiment at a time. ~1-2 weeks per test depending on traffic. Don't stack experiments unless traffic is very high (>1000 visitors/day). ### Proactive growth monitoring Don't wait for the user to ask. If your agent has scheduled checks, proactively flag: - **Dead project**: 0 events in 7 days → "⚠ PROJECT has no traffic — is it still deployed?" - **Conversion drop**: `cta_click` rate dropped >20% week-over-week → "Conversion declined — worth investigating" - **Experiment ready**: An experiment has >100 exposures per variant → "Experiment X has enough data — check results" - **Experiment winner**: Significance >95% → "Experiment X: Variant B wins with 3.8% vs 2.1%. Ship it?" - **Traffic spike**: >2× normal → "Unusual traffic surge on PROJECT — check referrers for the source" ## What this skill does NOT do - No GUI dashboards — your agent IS the dashboard (or use `live` for a real-time TUI) - No user management or billing - Funnels and retention are ad-hoc queries (no saved/scheduled reports) - No PII stored — IP addresses are not logged or retained. Privacy-first by design ## Examples Track custom events via `window.aa?.track()` (the `?.` ensures no error if tracker hasn't loaded): ```js 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'}); ```