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Cja Funnel Health Check

Organization
adobe
cja-funnel-health-check

Analyzes a multi-step conversion funnel to find where users drop off and which steps have the worst leakage. Use this skill when someone describes a journey or funnel and asks about conversion rates, drop-off, fallout, or step completion. Trigger for phrases like "analyze our onboarding funnel," "where are users dropping off," "what's our checkout conversion rate," "funnel analysis," "show me fallout between these steps," or "which step loses the most users."

Overview

Publisheradobe
Repositoryskills
Skill namecja-funnel-health-check
Stars
298
Forks
92
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by adobe on GitHub. Read the source before you install it.

Installation

Install the Cja Funnel Health Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/adobe/skills/tree/main/plugins/adobe-cja/skills/cja-funnel-health-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/adobe/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/adobe-cja/skills/cja-funnel-health-check .claude/skills/cja-funnel-health-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cja Funnel Health Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cja Funnel Health Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cja Funnel Health Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Funnel Health Check (Customer Journey Analytics)

Turn a plain-English funnel description into a quantified step-by-step conversion analysis. The output identifies the biggest leakage point in the funnel and provides dimension-based breakdowns to show which audience or channel has the worst drop-off.

Funnel health checks are most valuable when a team suspects a specific step is broken but hasn't quantified it. This skill does the quantification in a single conversation turn.

CJA MCP Tools Used

  • findDimensions — resolve page name, event, or other dimensions for step filtering
  • findMetrics — get the base metric to measure (sessions, visitors, events)
  • searchDimensionItems — find exact dimension values for step names
  • runReport (with adhocSegments) — measure visitor counts at each funnel step
  • findSegments — use existing segments as funnel step filters if applicable
  • describeSegment — understand segment logic before applying as a funnel step

Phase 0 — Setup

  1. Call findDataViews to list available data views.
  2. If the user hasn't specified a data view, present the list and ask which to use.
  3. Call setDefaultSessionDataViewId with the chosen ID.
  4. Ask the user to define the funnel stages if not already specified (e.g., "What are the steps in the funnel you want to analyze?").

Phase 1 — Define Funnel Steps

1.1 Parse the user's funnel description

Extract the step sequence from the user's plain-English description:

  • "Homepage → Product Page → Cart → Purchase"
  • "Registration → Onboarding Step 1 → Onboarding Step 2 → Activated"
  • "Landing Page → Lead Form → Thank You Page"

Each step must resolve to a measurable condition in CJA. A step can be:

  • Page view: user viewed a specific page (resolved via page name dimension)
  • Event/metric: user triggered a specific action (e.g., "Added to Cart")
  • Segment membership: user meets a pre-built segment condition

1.2 Clarify ambiguous steps

If any step is vague (e.g., "checkout" without a page name), ask one question:

"For the 'Checkout' step — should I look at people who visited a page containing 'checkout' in the URL, or those who triggered a specific event like 'Cart Add'?"

Do not ask more than one clarifying question at a time.

1.3 Resolve page names to dimension values

For page-based steps, call searchDimensionItems to find the exact dimension values that match the step name. First call findDimensions with a semantic search like "page name url" to confirm the correct dimension ID — in most CJA data views this is variables/web.webPageDetails.name, not variables/page:

searchDimensionItems(
  dimensionId: "variables/web.webPageDetails.name",
  searchAnd: "<step page name>",
  startDate: "<period start>",
  endDate: "<period end>",
  page: 0,
  limit: 10
)

Present matches to the user if there are multiple candidates:

"I found these pages matching 'checkout': /checkout/start, /checkout/payment, /checkout/review. Should I use '/checkout/start' as the entry to the checkout step?"

Phase 2 — Measure Each Step

For each funnel step, construct an ad hoc segment that filters to visitors/sessions that reached that step. Then run a report measuring the base metric (usually Unique Visitors or Sessions) with that segment applied.

2.1 Construct ad hoc segments for each step

A "reached step N" ad hoc segment is:

  • Container: Visit or Person (use Visit for session-level funnels, Person for cross-visit journeys)
  • Condition: Page Name equals "" OR Event occurred

2.2 Run reports for each step

Run one runReport per step with the ad hoc segment applied. The adhocSegments parameter takes fully-formed CJA segment definition objects — NOT a simplified shorthand. The correct structure is:

runReport(
  dimensionIds: "variables/web.webPageDetails.name",
  metricIds: "metrics/visitors",
  startDate: "<period start>",
  endDate: "<period end>",
  page: 0,
  limit: 1,
  adhocSegments: [{
    "func": "segment",
    "version": [1, 0, 0],
    "container": {
      "func": "container",
      "context": "visitors",
      "pred": {
        "func": "streq",
        "val": { "func": "attr", "name": "variables/web.webPageDetails.name" },
        "str": "<step page value>"
      }
    }
  }]
)

Use context: "visitors" (person-level) for cross-visit funnels. Use context: "visits" (session-level) for within-session funnels. The metric value comes from summaryData.filteredTotals[0] in the response.

The dimension and value used in the predicate should match what was found via searchDimensionItems in Phase 1 — use the exact value string returned.

Important: Each step uses a cumulative filter — measure "visitors who EVER reached this step in the period," not "visitors who ONLY visited this page." This produces the classic funnel waterfall.

Capture stepCount[i] for each step i from 1 to N.

Phase 3 — Compute Funnel Metrics

For each step-to-step transition:

  • stepConversionRate[i→i+1] = stepCount[i+1] / stepCount[i] × 100
  • dropOff[i→i+1] = stepCount[i] − stepCount[i+1]
  • dropOffRate[i→i+1] = 100 − stepConversionRate[i→i+1]

Overall funnel:

  • overallConversionRate = stepCount[N] / stepCount[1] × 100
  • biggestDropOffStep = argmax(dropOff[i→i+1]) — the step with the most visitors lost

Phase 4 — Optional: Segment the Funnel

If the user wants to compare funnel performance across audiences or channels, run the same step reports filtered by a dimension or segment:

runReport(
  dimensionIds: "variables/device_type",
  metricIds: "metrics/visitors",
  startDate: "<range start>",
  endDate: "<range end>",
  page: 0,
  limit: 10,
  adhocSegments: [{ /* step N filter — same structure as Phase 2 */ }]
)

Note: use findDimensions with searchQuery: "device type mobile desktop" to confirm the device dimension ID for the data view (often variables/device_type).

This shows step-N visitor counts broken down by device type (or channel, or country). If one segment has a dramatically lower conversion through the worst step, that's the target for optimization.

Common comparisons to suggest:

  • Device type (mobile vs desktop conversion often differs significantly)
  • Marketing channel (paid vs organic users may convert differently)
  • New vs returning visitors

Phase 5 — Generate HTML Funnel Report

Generate the funnel report inline and write to /tmp/cja_funnel_health_check_report_<YYYY-MM-DD_HHMMSS>.html.

HTML Template

html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Funnel Health Check &mdash; {ORG_NAME} &mdash; {FUNNEL_NAME}</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Playfair+Display:wght@700;900&family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
<style>
  * { box-sizing: border-box; margin: 0; padding: 0; }
  :root {
    --bg: #f5f4f1;
    --surface: #ffffff;
    --ink: #1a1a1a;
    --ink-muted: #6b6b6b;
    --border: #e5e2dc;
    --header-bg: #0e0e10;
    --header-warm: #3a1010;
    --accent-red: #c8312f;
    --accent-red-bright: #ff6b68;
    --accent-red-soft: #fdecea;
    --accent-green: #1f7a4d;
    --accent-yellow: #d4a017;
  }
  body { font-family: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
         background: var(--bg); color: var(--ink); line-height: 1.5;
         -webkit-font-smoothing: antialiased; }

  /* === Header === */
  header { background: linear-gradient(120deg, var(--header-bg) 0%, #1a0d0d 55%, var(--header-warm) 100%);
           color: #fff; padding: 56px 56px 44px; position: relative; overflow: hidden; }
  header::after { content: ""; position: absolute; right: -140px; top: -140px;
                  width: 460px; height: 460px;
                  background: radial-gradient(circle, rgba(200,49,47,.35) 0%, transparent 70%);
                  pointer-events: none; }
  .header-inner { max-width: 1080px; margin: 0 auto; position: relative; z-index: 1; }
  .eyebrow { display: inline-flex; align-items: center; gap: 8px;
             padding: 6px 14px; border: 1px solid rgba(255,107,104,.55);
             border-radius: 999px; color: var(--accent-red-bright);
             font-size: 11px; font-weight: 600; letter-spacing: 1.2px;
             text-transform: uppercase; margin-bottom: 24px;
             background: rgba(200,49,47,.10); }
  .eyebrow::before { content: ""; width: 6px; height: 6px;
                     background: var(--accent-red-bright); border-radius: 50%; }
  header h1 { font-family: "Playfair Display", Georgia, serif;
              font-size: 56px; font-weight: 700; letter-spacing: -1.5px;
              line-height: 1.05; margin-bottom: 14px; color: #fff; }
  header .lede { font-size: 16px; max-width: 560px;
                 color: rgba(255,255,255,.80); margin-bottom: 24px;
                 line-height: 1.55; }
  header .meta { display: flex; flex-wrap: wrap; gap: 22px;
                 font-size: 13px; color: rgba(255,255,255,.60); }
  header .meta span { display: inline-flex; align-items: center; gap: 6px; }
  header .meta .icon { opacity: .8; }

  /* === Tabs === */
  nav { background: var(--surface); border-bottom: 1px solid var(--border);
        padding: 0 56px; display: flex; gap: 28px;
        position: sticky; top: 0; z-index: 50; }
  nav a { display: block; padding: 16px 0; font-size: 14px;
          color: var(--ink); text-decoration: none;
          border-bottom: 2px solid transparent;
          transition: border-color .15s ease; }
  nav a:hover { border-bottom-color: var(--accent-red); }

  /* === Container === */
  .container { max-width: 1080px; margin: 0 auto; padding: 36px 56px 60px; }

  /* === Section label === */
  .section-label { font-size: 11px; font-weight: 700;
                   text-transform: uppercase; letter-spacing: 1.4px;
                   color: var(--ink-muted); margin-bottom: 14px;
                   padding-bottom: 10px; border-bottom: 1px solid var(--border); }

  /* === KPI grid === */
  .kpi-row { display: grid; grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
             gap: 14px; margin-bottom: 36px; }
  .kpi-tile { background: var(--surface); border-radius: 8px;
              padding: 22px 22px 20px;
              border-top: 3px solid #b9b6ae;
              box-shadow: 0 1px 3px rgba(0,0,0,.05); }
  .kpi-tile.down { border-top-color: var(--accent-red); }
  .kpi-tile.up   { border-top-color: var(--accent-green); }
  .kpi-tile.flat { border-top-color: #b9b6ae; }
  .kpi-head { display: flex; justify-content: space-between;
              align-items: center; margin-bottom: 10px; }
  .kpi-label { font-size: 11px; font-weight: 700;
               text-transform: uppercase;
               color: var(--ink-muted); letter-spacing: 1px; }
  .kpi-value { font-family: "Playfair Display", Georgia, serif;
               font-weight: 700; font-size: 38px;
               line-height: 1; color: var(--ink);
               margin-bottom: 12px; }
  .pill { display: inline-flex; align-items: center; gap: 4px;
          padding: 3px 9px; border-radius: 4px;
          font-size: 12px; font-weight: 600; line-height: 1.4; }
  .pill.down { background: var(--accent-red-soft); color: var(--accent-red); }
  .pill.up   { background: #ebf5ef; color: var(--accent-green); }
  .pill.flat { background: #f1efea; color: var(--ink-muted); }
  .prior { display: block; margin-top: 10px;
           font-size: 12px; color: var(--ink-muted); }

  /* === Funnel chart wrap === */
  .chart-wrap { background: var(--surface); border-radius: 8px;
                padding: 28px 32px;
                box-shadow: 0 1px 3px rgba(0,0,0,.04);
                margin-bottom: 22px; }
  .chart-wrap h2 { font-family: "Playfair Display", Georgia, serif;
                   font-size: 18px; font-weight: 700;
                   margin-bottom: 18px;
                   padding-bottom: 12px;
                   border-bottom: 1px solid var(--border); }

  /* === Sections (collapsible tables) === */
  .section { background: var(--surface); border-radius: 8px;
             box-shadow: 0 1px 3px rgba(0,0,0,.04);
             margin-bottom: 22px; overflow: hidden; }
  .section-header { padding: 18px 28px; border-bottom: 1px solid var(--border);
                    display: flex; justify-content: space-between;
                    align-items: center; cursor: pointer; }
  .section-header h2 { font-family: "Playfair Display", Georgia, serif;
                       font-size: 18px; font-weight: 700; }
  table { width: 100%; border-collapse: collapse; font-size: 13px; }
  thead th { background: #faf8f4; padding: 12px 22px;
             text-align: left; font-weight: 600;
             text-transform: uppercase; letter-spacing: .6px;
             font-size: 11px; color: var(--ink-muted);
             border-bottom: 1px solid var(--border); }
  tbody td { padding: 12px 22px; border-bottom: 1px solid #f4f1eb; }
  tbody tr:last-child td { border-bottom: none; }

  .badge { display: inline-block; padding: 3px 9px; border-radius: 4px;
           font-size: 11px; font-weight: 600; }
  .badge.green  { background: #ebf5ef; color: var(--accent-green); }
  .badge.red    { background: var(--accent-red-soft); color: var(--accent-red); }
  .badge.yellow { background: #fef6e3; color: #b67a08; }
  .badge.grey   { background: #f1efea; color: var(--ink-muted); }

  .highlight-row td { background: var(--accent-red-soft) !important;
                      font-weight: 700; }
  .progress-bar-wrap { background: #f1efea; border-radius: 4px;
                       height: 8px; overflow: hidden;
                       width: 100%; min-width: 80px; }
  .progress-bar { height: 100%; border-radius: 4px;
                  background: linear-gradient(90deg, var(--accent-red), #8a1d1c); }

  .rec-item { display: flex; gap: 12px; align-items: flex-start;
              padding: 14px 28px; border-bottom: 1px solid #f1eeea;
              font-size: 14px; line-height: 1.55; }
  .rec-item:last-child { border-bottom: none; }

  .back-top { position: fixed; bottom: 24px; right: 24px;
              background: var(--accent-red); color: #fff;
              width: 44px; height: 44px; border-radius: 50%;
              border: none; font-size: 20px; cursor: pointer;
              box-shadow: 0 4px 12px rgba(200,49,47,0.30); }
  footer { text-align: center; padding: 32px 24px;
           font-size: 12px; color: var(--ink-muted); }

  /* === Print === */
  @media print {
    nav { display: none; position: static; }
    header { padding: 36px 32px 28px; }
    header h1 { font-size: 42px; }
    .section-header { cursor: default; }
    .kpi-row { page-break-inside: avoid; }
    .kpi-tile, .chart-wrap, .section {
      box-shadow: none; border: 1px solid var(--border);
    }
    .back-top { display: none; }
  }
</style>
</head>
<body>

<header>
  <div class="header-inner">
    <div class="eyebrow">Funnel Health Report</div>
    <h1>{ORG_NAME} Funnel Health</h1>
    <p class="lede">Step-by-step conversion for the {FUNNEL_NAME} journey across {DATE_RANGE}, with the biggest leakage point surfaced.</p>
    <div class="meta">
      <span><span class="icon">&#128197;</span> {DATE_RANGE}</span>
      <span><span class="icon">&#128202;</span> {DATA_VIEW}</span>
      <span><span class="icon">&#128340;</span> Prepared {GENERATED_DATE}</span>
    </div>
  </div>
</header>

<nav>
  <a href="#overview">Overview</a>
  <a href="#chart">Funnel Chart</a>
  <a href="#steps">Step Detail</a>
  <a href="#segments">Segment Breakdown</a>
  <a href="#recs">Recommendations</a>
</nav>

<div class="container">

  <!-- Summary KPI Tiles -->
  <div class="section-label">Funnel Summary</div>
  <div id="overview" class="kpi-row">
    <div class="kpi-tile flat">
      <div class="kpi-head"><div class="kpi-label">Entered Funnel</div></div>
      <div class="kpi-value">{STEP_1_COUNT}</div>
      <span class="prior">Step 1 visitors</span>
    </div>
    <div class="kpi-tile flat">
      <div class="kpi-head"><div class="kpi-label">Completed Funnel</div></div>
      <div class="kpi-value">{STEP_N_COUNT}</div>
      <span class="prior">Reached final step</span>
    </div>
    <div class="kpi-tile flat">
      <div class="kpi-head"><div class="kpi-label">Overall Conversion</div></div>
      <div class="kpi-value">{OVERALL_CVR}%</div>
      <span class="prior">End-to-end rate</span>
    </div>
    <div class="kpi-tile down">
      <div class="kpi-head"><div class="kpi-label">Biggest Drop-Off Step</div></div>
      <div class="kpi-value" style="font-size:22px;">{WORST_STEP_NAME}</div>
      <span class="pill down">&#9660; Worst leakage</span>
    </div>
    <div class="kpi-tile down">
      <div class="kpi-head"><div class="kpi-label">Worst Step Drop-Off</div></div>
      <div class="kpi-value">{WORST_STEP_DROPOFF}%</div>
      <span class="prior">Of visitors lost at this step</span>
    </div>
  </div>

  <!-- Funnel Bar Chart -->
  <div id="chart" class="chart-wrap">
    <h2>Funnel Visualization</h2>
    <canvas id="funnelChart" height="100"></canvas>
  </div>

  <!-- Step-by-Step Table -->
  <div id="steps" class="section">
    <div class="section-header" onclick="toggle('steps-body')">
      <h2>Step-by-Step Analysis</h2>
      <span id="steps-body-icon">&#9662;</span>
    </div>
    <div id="steps-body">
      <table>
        <thead><tr>
          <th>Step</th>
          <th>Visitors</th>
          <th>% of Step 1</th>
          <th>Step Conversion</th>
          <th>Drop-Off</th>
          <th>Drop-Off Rate</th>
          <th>Visual</th>
        </tr></thead>
        <tbody>
          <!-- For each step i:
          <tr class="{highlight-row if worst step}">
            <td>{STEP_NAME}</td>
            <td>{STEP_COUNT}</td>
            <td>{PCT_OF_STEP_1}%</td>
            <td>{STEP_CVR}% {badge}</td>
            <td>{DROPOFF}</td>
            <td>{DROPOFF_RATE}%</td>
            <td>
              <div class="progress-bar-wrap">
                <div class="progress-bar" style="width:{PCT_OF_STEP_1}%"></div>
              </div>
            </td>
          </tr>
          -->
        </tbody>
      </table>
    </div>
  </div>

  <!-- Segment Breakdown (optional) -->
  <div id="segments" class="section">
    <div class="section-header" onclick="toggle('seg-body')">
      <h2>Segment Breakdown at Worst Step</h2>
      <span id="seg-body-icon">&#9662;</span>
    </div>
    <div id="seg-body">
      <table>
        <thead><tr>
          <th>Segment / Dimension</th>
          <th>Entered Worst Step</th>
          <th>Passed Worst Step</th>
          <th>Conversion</th>
          <th>vs. Average</th>
        </tr></thead>
        <tbody>
          <!-- Fill with dimension breakdown at the worst step -->
        </tbody>
      </table>
    </div>
  </div>

  <!-- Recommendations -->
  <div id="recs" class="section">
    <div class="section-header" onclick="toggle('rec-body')">
      <h2>Optimization Recommendations</h2>
      <span id="rec-body-icon">&#9662;</span>
    </div>
    <div id="rec-body">
      <!-- 2-3 recommendation items as .rec-item -->
    </div>
  </div>

</div>

<button class="back-top" onclick="window.scrollTo({top:0,behavior:'smooth'})">&uarr;</button>
<footer>Funnel Health Check &mdash; {ORG_NAME} &mdash; Generated {GENERATED_DATE}</footer>

<script>
// Funnel bar chart — recolor via design tokens.
// Stage colors fade from accent-red (worst leakage upstream) to a deeper red downstream.
const ctx = document.getElementById('funnelChart').getContext('2d');
new Chart(ctx, {
  type: 'bar',
  data: {
    labels: {STEP_NAMES_JSON},
    datasets: [{
      label: 'Visitors',
      data: {STEP_COUNTS_JSON},
      backgroundColor: [
        'rgba(200,49,47,0.90)',
        'rgba(200,49,47,0.72)',
        'rgba(200,49,47,0.54)',
        'rgba(200,49,47,0.38)',
        'rgba(200,49,47,0.22)'
      ],
      borderRadius: 6
    }]
  },
  options: {
    responsive: true,
    plugins: { legend: { display: false } },
    scales: {
      y: { beginAtZero: true, grid: { color: '#eee9df' },
           ticks: { color: '#6b6b6b' } },
      x: { grid: { display: false },
           ticks: { color: '#1a1a1a', font: { weight: '600' } } }
    }
  }
});

function toggle(id) {
  var el = document.getElementById(id);
  var ic = document.getElementById(id + '-icon');
  if (el.style.display === 'none') { el.style.display=''; ic.textContent='\u25be'; }
  else { el.style.display='none'; ic.textContent='\u25b8'; }
}
</script>
</body></html>

Workflow Summary

  1. Parse funnel steps from user description.
  2. Clarify any ambiguous steps (one question at a time).
  3. Resolve page names with searchDimensionItems.
  4. Run one runReport per step with ad hoc segment filter; capture visitor counts.
  5. Compute step conversion rates, drop-off counts, and rates.
  6. Identify the biggest drop-off step.
  7. Optionally run dimension breakdown at the worst step.
  8. Generate HTML report with Chart.js funnel visualization.
  9. Write to /tmp/cja_funnel_health_check_report_<YYYY-MM-DD_HHMMSS>.html.
  10. Open with open /tmp/cja_funnel_health_check_report_<YYYY-MM-DD_HHMMSS>.html.
  11. Summarize inline: "Overall conversion: X%. Biggest drop-off at [Step N]: Y% of users abandon. Mobile users drop off at a 2× higher rate than desktop."

Important Guardrails

  • Read-only analysis. Never modify segments, calculated metrics, or project definitions automatically.
  • Confirm funnel stages before running. Ambiguous stage definitions produce misleading results — clarify with the user first.
  • Note attribution model. Funnel conversion rates depend on the attribution model in the data view; mention it in the report.
  • Flag incomplete data. If any stage returns zero or suspiciously low counts, note possible tracking gaps before drawing conclusions.
  • Cap date range. Funnel analysis over very long date ranges (>90 days) can be slow; suggest 30-day windows as default.
  • Never assume stage order. Confirm with the user that the stages are sequential and mutually exclusive before calculating drop-off rates.

Example Interaction

"Check the health of our checkout funnel — I want to see where people are dropping off."

  1. Setup: Call findDataViews, user selects their e-commerce data view. Call setDefaultSessionDataViewId.
  2. Define funnel: Ask "What are the checkout stages?" User replies: "Product View → Add to Cart → Checkout Start → Purchase."
  3. Analysis: Run runReport for each stage transition over the last 30 days. Calculate drop-off rates: Product View→Cart 22%, Cart→Checkout 58%, Checkout→Purchase 71%.
  4. Findings: Identify the Cart→Checkout step as the highest drop-off (78% fall off). Segment by device type to find mobile conversion is 40% lower than desktop.
  5. Report: Present a funnel visualization with drop-off rates per stage, top exit segments, and 3 prioritized recommendations.

Recommendations Logic

  • Worst step drop-off > 60%: "Critical leakage — this step is broken or the user expectation is misaligned. Prioritize UX investigation."
  • Mobile drop-off > 2× desktop: "Mobile UX at this step needs attention — consider a dedicated mobile flow or simplified form."
  • Specific channel drop-off > 40% worse than average: "Users from this channel may have mismatched intent — review landing page alignment."
  • Step 1 count < 1,000: "Funnel entry volume is too low for statistical confidence. Check the step definition or expand the date range."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cja Funnel Health Check AI skill do?

Analyzes a multi-step conversion funnel to find where users drop off and which steps have the worst leakage. Use this skill when someone describes a journey or funnel and asks about conversion rates, drop-off, fallout, or step completion. Trigger for phrases like "analyze our onboarding funnel," "where are users dropping off," "what's our checkout conversion rate," "funnel analysis," "show me fallout between these steps," or "which step loses the most users."

Why use Cja Funnel Health Check on TypingMind?

Because you install it once and use it with any model. Cja Funnel Health Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cja Funnel Health Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/adobe/skills/tree/main/plugins/adobe-cja/skills/cja-funnel-health-check. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cja Funnel Health Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cja Funnel Health Check?

As many as you like. As long as a model supports skills, you can use Cja Funnel Health Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cja Funnel Health Check AI skill free?

Yes. It is published on GitHub by adobe under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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