Imported from openai/plugins (plugins/mixpanel-headless/skills/dashboard-expert/SKILL.md). Install upstream with npx skills add openai/plugins --skill dashboard-expert. Copyright stays with the author.

Dashboard Expert

Analyze, build, modify, and explain Mixpanel dashboards. Four modes — pick the one matching the user's intent.

Mode Selection

Quick Start: Analyze an Existing Dashboard

import json, re
import mixpanel_headless as mp

ws = mp.Workspace()
dash = ws.get_dashboard(DASHBOARD_ID)
layout, contents = dash.layout, dash.contents

# Extract structure: rows → cells → content items
for row_id in layout["order"]:
    row = layout["rows"][row_id]
    for cell in row["cells"]:
        cid, ctype = str(cell["content_id"]), cell["content_type"]
        if ctype in ("report", "report-link"):
            info = contents["report"][cid]
            print(f"  [{cell['width']}w] {info['name']} ({info['type']}) {'[linked]' if ctype == 'report-link' else ''}")
        elif ctype == "text":
            md = contents["text"][cid].get("markdown", "")
            is_header = bool(re.search(r'<h2[\s>]', md, re.I))
            print(f"  [{cell['width']}w] TEXT {'[SECTION]' if is_header else ''}: {md[:60]}...")

# Execute each report → DataFrame
for cid, info in contents.get("report", {}).items():
    btype, bid = info["type"], info["id"]
    if btype == "flows":
        result = ws.query_saved_flows(bid)
    else:
        result = ws.query_saved_report(bid, bookmark_type=btype)
    df = result.df
    print(f"{info['name']}: {len(df)} rows, columns={list(df.columns)}")

Quick Start: Build a New Dashboard

from mixpanel_headless.types import CreateDashboardParams, DashboardRow, DashboardRowContent
import json

ws = mp.Workspace()
dau = ws.query("Login", math="dau", last=90)

def text(html):
    return DashboardRowContent(content_type="text", content_params={"markdown": html})

def report(name, btype, result):
    return DashboardRowContent(content_type="report", content_params={
        "bookmark": {"name": name, "type": btype, "params": json.dumps(result.params)}})

dashboard = ws.create_dashboard(CreateDashboardParams(
    title="Product Health", description="Core metrics.",
    rows=[
        DashboardRow(contents=[text("<h2>Product Health</h2><p>Core metrics.</p>")]),
        DashboardRow(contents=[report("DAU (90d)", "insights", dau)]),
    ],
))
ws.pin_dashboard(dashboard.id)  # Make visible to team

Mode: Analyze

Read existing dashboards, execute their reports, and synthesize understanding.

Phase A1: Read Dashboard Structure

dash = ws.get_dashboard(dashboard_id)
layout, contents = dash.layout, dash.contents

Parse the response into a structured representation:

• layout["order"] — ordered list of row IDs
• layout["rows"][row_id]["cells"] — cells with content_id, content_type, width
• contents["report"][str(content_id)] — report metadata: id (bookmark_id), name, type, params, description
• contents["text"][str(content_id)] — text card: markdown

Classify each cell:

• content_type == "report" → owned, editable
• content_type == "report-link" → linked from another dashboard, read-only
• content_type == "text" → text card; detect section headers via re.search(r'<h2[\s>]', md, re.I)

Build a mental model: Group reports by section (text cards with <h2> tags delimit sections). Note each report's chart type, width, and position.

Phase A2: Extract Report Details

For deeper understanding, fetch full bookmark params:

bookmark = ws.get_bookmark(bookmark_id)
params = bookmark.params  # Full query definition dict

Key fields in params (Insights format):

• params["sections"]["show"] — metrics with event names and math type
• params["sections"]["group"] — breakdown properties
• params["sections"]["filter"] — active filters
• params["sections"]["time"] — date range
• params["displayOptions"]["chartType"] — visualization type

Note: params in contents["report"][id] may be a JSON string — parse with json.loads() if needed.

Phase A3: Execute and Summarize

Execute each report to get live data:

for cid, info in contents.get("report", {}).items():
    bid, btype = info["id"], info["type"]
    if btype == "flows":
        result = ws.query_saved_flows(bid)
    else:
        result = ws.query_saved_report(bid, bookmark_type=btype)
    df = result.df

Summarize by report type:

Cross-correlate across reports: Look for relationships — DAU trends vs. retention, funnel drop-off vs. feature adoption.

Phase A4: Present Analysis

Structure findings as:

1. Dashboard overview — title, purpose, section count, report count
2. Section-by-section breakdown — what each section measures, key findings
3. Cross-metric insights — correlations, anomalies, patterns
4. Suggestions — missing metrics, better chart types, layout improvements

Multi-Dashboard Analysis

When analyzing multiple dashboards, build a unified picture:

dashboard_ids = [1001, 1002, 1003]
all_data = {}
for did in dashboard_ids:
    dash = ws.get_dashboard(did)
    for cid, info in dash.contents.get("report", {}).items():
        result = ws.query_saved_report(info["id"], bookmark_type=info["type"])
        all_data[f"{dash.title}/{info['name']}"] = result.df
# Cross-dashboard: join DataFrames on date index, compute correlations

Mode: Build

Create new dashboards from scratch. Five phases.

Phase B1: Investigate

Before building, discover the data. Never build reports for events with zero volume.

ws = mp.Workspace()
top = ws.top_events(limit=15)
for t in top:
    print(f"{t.event}: {t.count:,} ({t.percent_change:+.1%})")

# Validate candidate events
for event in candidate_events:
    result = ws.query(event, from_date="2025-01-01", to_date="2025-03-31")
    print(f"{event}: {result.df['count'].sum():,.0f} total")

# Explore properties for breakdowns
props = ws.properties(event="key_event")
values = ws.property_values(event="key_event", property="platform", limit=20)

Phase B2: Plan Structure

Present a proposed structure before building. Choose a template from references/dashboard-templates.md.

A plan includes: title + description, sections with text card headers, reports per section with chart type, grid layout.

Text cards use HTML (not markdown). Every dashboard must have an intro text card and section headers.

Allowed HTML tags: <h1>, <h2>, <h3>, <p>, <strong>, <em>, <u>, <s>, <mark>, <code>, <blockquote>, <hr>, <br>, <ul>, <ol>, <li>, <a href="...">

Forbidden (stripped): <div>, <span>, <b> (use <strong>), <i> (use <em>), <img>, <table>

Critical: Strip \n and collapse whitespace from HTML before sending. Each element renders as its own line.

Text card patterns:

Intro:     <h2>Dashboard Title</h2><p>What and why. Time period: last 90 days.</p>
Section:   <h2>Acquisition</h2><p>How users discover and sign up.</p>
Explainer: <p>^ Signup conversion is <strong>23.4%</strong>, up 2.1pp.</p>

Phase B3: Query and Build

Query each metric, verify data, then create with layout in one call.

def text(html):
    return DashboardRowContent(content_type="text", content_params={"markdown": html})

def report(name, btype, result, description=None):
    params = {"bookmark": {"name": name, "type": btype, "params": json.dumps(result.params)}}
    if description:
        params["bookmark"]["description"] = description
    return DashboardRowContent(content_type="report", content_params=params)

dashboard = ws.create_dashboard(CreateDashboardParams(
    title="Product Health Dashboard",
    description="Key metrics for product health monitoring.",
    rows=[
        DashboardRow(contents=[text("<h2>Product Health</h2><p>Updated daily.</p>")]),
        DashboardRow(contents=[
            report("DAU (90d)", "insights", dau),
            report("Signups (90d)", "insights", signups),
            report("Revenue (90d)", "insights", revenue),
        ]),
        DashboardRow(contents=[text("<h2>Conversion</h2><p>Key funnels.</p>")]),
        DashboardRow(contents=[report("Signup Funnel", "funnels", funnel)]),
    ],
))

On report failure, substitute a fallback text card:

try:
    result = ws.query(event, math="total", last=90)
    row_items.append(report(f"{event} Trend", "insights", result))
except Exception as e:
    row_items.append(text(f"<p><strong>Failed:</strong> {event} — {e}</p>"))

Phase B4: Enhance

• Pin for team visibility: ws.pin_dashboard(dashboard.id) — dashboards are invisible by default
• Favorite for personal use: ws.favorite_dashboard(dashboard.id)
• Add explainer cards: see Mode: Explain
• Adjust heights: see references/dashboard-reference.md Section 3.4

Phase B5: Verify

Open the dashboard and confirm all reports render with data, text cards display correctly, and layout matches the plan.

Mode: Modify

Update existing dashboards. Read first, then apply changes in the correct order.

Phase M1: Read Current State

Use Analyze Phase A1-A2 to understand the dashboard's structure. Present to user before making changes.

Phase M2: Plan Changes

Classify each change and plan execution order. Operations must follow this sequence:

1. Metadata (title/description) — standalone PATCH
2. Cell creates — add new content first
3. Row reorder (rows_order) — after creates so temp IDs resolve
4. Cell updates — modify existing content
5. Cell deletes — remove content
6. Row deletes — remove entire rows last

Phase M3: Execute Changes

Adding content to a specific existing row — send content AND layout together:

import copy
dash = ws.get_dashboard(dashboard_id)
layout = copy.deepcopy(dash.layout)
target_row = layout["rows"][target_row_id]

# Redistribute widths
new_count = len(target_row["cells"]) + 1
cell_width = 12 // new_count
for cell in target_row["cells"]:
    cell["width"] = cell_width
target_row["cells"].append({"temp_id": "-1", "width": cell_width})

ws.update_dashboard(dashboard_id, UpdateDashboardParams(
    content={"action": "create", "content_type": "report",
             "content_params": {"bookmark": {"name": "New Report", "type": "insights",
                                              "params": json.dumps(result.params)}}},
    layout={"rows_order": layout["order"], "rows": layout["rows"]},
))

Adding content as a new row — content action alone (appends to bottom):

ws.update_dashboard(dashboard_id, UpdateDashboardParams(
    content={"action": "create", "content_type": "text",
             "content_params": {"markdown": "<p>^ Explainer card.</p>"}},
))

Deleting content:

ws.update_dashboard(dashboard_id, UpdateDashboardParams(
    content={"action": "delete", "content_type": "report", "content_id": content_id},
))

Cross-type updates (e.g., text → report): API rejects changing content_type on update. Delete the old cell, then create the new one.

See references/dashboard-reference.md Section 8 for temp ID resolution, operation ordering details, and report-link semantics.

Mode: Explain

Combine analysis with targeted text card insertion.

1. Analyze — run Mode: Analyze to extract structure and execute reports
2. Generate insights — for each report, compute key metrics from the DataFrame:

   latest = df.iloc[-1]["count"]
   prev = df.iloc[-8]["count"]
   trend = ((latest - prev) / prev) * 100
   html = (f"<p>^ DAU is <strong>{latest:,.0f}</strong>, "
           f"{'up' if trend > 0 else 'down'} <strong>{abs(trend):.1f}%</strong> "
           f"vs. last week.</p>").replace("\n", "")

3. Insert cards — add as new rows below each report section:

   ws.update_dashboard(dashboard_id, UpdateDashboardParams(
       content={"action": "create", "content_type": "text",
                "content_params": {"markdown": html}},
   ))

Critical Gotchas

1. Combined content+layout PATCH — send both content and layout in the same UpdateDashboardParams to add cells to specific existing rows. Without layout, new content appends as a full-width row at the bottom.

2. Width auto-redistribution — when adding to an existing row with N cells, set all cells (including new) to 12 // (N+1) width.

3. Update operation ordering — metadata → cell creates → rows_order → cell updates → cell deletes → row deletes. Wrong order causes failures.

4. per_user requires math_property — using per-user aggregation without a numeric property raises BookmarkValidationError.

5. CreateBookmarkParams(dashboard_id=X) does NOT add to layout — use add_report_to_dashboard() or inline content action.

6. add_report_to_dashboard() CLONES — creates "Duplicate of..." copy. Use rows in CreateDashboardParams or inline content action instead.

7. GET order vs PATCH rows_order — layout from GET uses order; PATCH expects rows_order.

8. Never include version in layout PATCH — the API rejects it.

9. Strip \n and collapse whitespace — call .replace("\n", "").strip() on text card HTML. Newlines cause TipTap to mangle content.

10. Limits — title 255 chars, description 400 chars, text cards 2,000 chars, max 4 items/row, max 30 rows.

11. Cross-type cell updates require delete+create — API rejects changing content_type on an update action.

12. Report-link cells are read-only — content_type: "report-link" references a report owned by another dashboard. You can view but not edit its params.

13. Auto-pin after creation — dashboards are invisible to the team by default. Call ws.pin_dashboard(dashboard.id).

14. The markdown field accepts only HTML — despite the name. Markdown syntax renders as literal text.

See Also

• references/dashboard-reference.md — Complete API reference, layout system, content actions, text card formatting, update operations, analysis patterns
• references/dashboard-templates.md — 9 purpose-built dashboard templates with section layouts and report specs
• references/bookmark-pipeline.md — End-to-end pipeline from typed query to dashboard report for all 4 engines
• references/chart-types.md — Chart type selection guide with slugs, use cases, and width recommendations