Fix dataset viewer layout

README.md

@@ -1,181 +1,181 @@
----
-license: cc-by-4.0
-task_categories:
-  - tabular-classification
-  - tabular-regression
-  - time-series-forecasting
-language:
-  - en
-tags:
-  - synthetic
-  - saas
-  - business-intelligence
-  - analytics
-  - dashboards
-  - startup
-  - growth
-  - mrr
-  - cac
-  - ltv
-  - churn
-  - marketing
-  - tabular
-pretty_name: Solstice SaaS Growth Pack
-size_categories:
-  - 1K<n<10K
-configs:
-  - config_name: companies
-    data_files:
-      - split: train
-        path: companies.csv
-  - config_name: growth_metrics
-    data_files:
-      - split: train
-        path: growth_metrics.csv
-  - config_name: channel_performance
-    data_files:
-      - split: train
-        path: channel_performance.csv
-  - config_name: customer_segments
-    data_files:
-      - split: train
-        path: customer_segments.csv
-  - config_name: metric_definitions
-    data_files:
-      - split: train
-        path: metric_definitions.csv
-  - config_name: dashboard_suggestions
-    data_files:
-      - split: train
-        path: dashboard_suggestions.csv
----
-
-# Solstice SaaS Growth Pack (Sample)
-
-**A dashboard-ready synthetic SaaS metrics dataset.** Import the 6 CSVs straight into any BI tool and have a credible SaaS growth dashboard in under 10 minutes — no cleanup, no modeling.
-
-Built by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic — no real company, customer, or personal data.
-
-## What's in the box
-
-| File | Rows | Grain | Purpose |
-|---|---|---|---|
-| `companies.csv` | 6 | company | Master dimension — 6 synthetic startups spanning 6 distinct growth narratives |
-| `growth_metrics.csv` | 540 | date × company | Daily revenue, MRR, customer counts, CAC, LTV, churn |
-| `channel_performance.csv` | 3,780 | date × company × channel | Marketing channel impressions, clicks, conversions, cost, attribution |
-| `customer_segments.csv` | 18 | company × segment | SMB / Mid-Market / Enterprise unit economics |
-| `metric_definitions.csv` | 7 | metric | Self-documenting formulas |
-| `dashboard_suggestions.csv` | 8 | chart | 4 starter dashboards with suggested axes |
-
-**Period:** 90 days. **Currency:** USD. **Dates:** ISO-8601 (`YYYY-MM-DD`). **Join key:** `company_id`.
-
-## Growth narratives included
-
-Each company embodies a distinct SaaS growth profile — so dashboards show realistic variance instead of random noise:
-
-- **Steady PLG** — strong SEO/content/referral, efficient long-term growth
-- **Paid accelerator** — aggressive paid acquisition, higher CAC
-- **Enterprise lumpy** — quarter-end deal spikes, lower churn
-- **Seasonal B2C** — demand seasonality and periodic swings
-- **Churn recovery** — visible churn event followed by stabilization
-- **Capital infusion** — growth acceleration after mid-period expansion
-
-## Why this dataset
-
-**Clean joins, zero cleanup.** Stable IDs, one clear grain per table, no null-heavy columns, no ambiguous foreign keys. Import order: companies → growth_metrics → channel_performance → customer_segments.
-
-**Pre-calculated SaaS metrics.** MRR, CAC, LTV, churn rate, conversion rate, CTR — all included, formulas documented in `metric_definitions.csv`. Users get to insight on first import.
-
-**Cross-table consistency.** Daily channel `conversions` sum exactly to `new_customers`. Daily channel `cost` sums exactly to `marketing_spend`. Active customer counts respect `prev + new − churned = active` on every row.
-
-**Realistic magnitudes.** Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.
-
-## Use cases
-
-- Instant demo dashboards for BI / analytics tools
-- User onboarding & first-value experiences
-- SaaS metrics dashboard templates
-- Product showcase & sales enablement
-- Analytics workflow testing (imports, joins, filters)
-- Startup & growth analytics education
-- Customer success & retention analysis
-- Marketing performance & attribution analysis
-
-## Quick start
-
-```
-companies.csv           → dimension table
-growth_metrics.csv      → primary fact (time × company)
-channel_performance.csv → secondary fact (time × company × channel)
-customer_segments.csv   → segment roll-up
-```
-
-Join key is `company_id`. All dates are ISO-8601. All currency is USD.
-
-**Suggested first dashboard: SaaS Growth Overview**
-- Line chart: `date` × `revenue`, filter by `company_name`
-- Dual-axis line: `date` × (`mrr`, `active_customers`), filter by `company_name`
-
-Full dashboard recipes in `dashboard_suggestions.csv`.
-
-### Load with pandas
-
-```python
-import pandas as pd
-
-companies = pd.read_csv("companies.csv")
-growth = pd.read_csv("growth_metrics.csv", parse_dates=["date"])
-channels = pd.read_csv("channel_performance.csv", parse_dates=["date"])
-segments = pd.read_csv("customer_segments.csv")
-
-# Monthly MRR per company
-monthly_mrr = (
-    growth.assign(month=growth["date"].dt.to_period("M"))
-          .groupby(["company_name", "month"])["mrr"].mean()
-          .reset_index()
-)
-```
-
-## Data quality checklist
-
-- All foreign keys resolve (0 orphans)
-- No nulls in required columns
-- No negative revenue, spend, or counts
-- Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)
-- Continuity invariant holds: `prev_active + new − churned = active` on every row
-- `impressions ≥ clicks ≥ conversions` on every channel row
-
-## Schema
-
-See `SCHEMA.md` for full column definitions, join model, metric formulas, and synthetic profile documentation.
-
-## License
-
-Released under **CC BY 4.0** — use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.
-
-Synthetic data only — no real company, customer, or personal information.
-
-## Get the full pack
-
-This repo is a **6-company, 90-day sample**. The production pack scales to any company count (12 / 50 / 500+), any date range (1 quarter / 1 year / 3 years), any seed for reproducibility, custom growth-profile mixes, and custom industry / channel configurations.
-
-**Self-serve (Stripe checkout):**
-- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
-
-**Full pack + enterprise scope:**
-- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
-
-**Procurement catalog:**
-- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
-
-## Citation
-
-```bibtex
-@dataset{solstice_saas_growth_pack_2026,
-  title        = {Solstice SaaS Growth Pack (Sample)},
-  author       = {Solstice AI Studio},
-  year         = {2026},
-  publisher    = {Hugging Face},
-  url          = {https://huggingface.co/datasets/solsticestudioai/saas-growth-pack}
-}
-```
+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - saas
+  - business-intelligence
+  - analytics
+  - dashboards
+  - startup
+  - growth
+  - mrr
+  - cac
+  - ltv
+  - churn
+  - marketing
+  - tabular
+pretty_name: Solstice SaaS Growth Pack
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: companies
+    data_files:
+      - split: train
+        path: data/companies/train.csv
+  - config_name: growth_metrics
+    data_files:
+      - split: train
+        path: data/growth_metrics/train.csv
+  - config_name: channel_performance
+    data_files:
+      - split: train
+        path: data/channel_performance/train.csv
+  - config_name: customer_segments
+    data_files:
+      - split: train
+        path: data/customer_segments/train.csv
+  - config_name: metric_definitions
+    data_files:
+      - split: train
+        path: data/metric_definitions/train.csv
+  - config_name: dashboard_suggestions
+    data_files:
+      - split: train
+        path: data/dashboard_suggestions/train.csv
+---
+
+# Solstice SaaS Growth Pack (Sample)
+
+**A dashboard-ready synthetic SaaS metrics dataset.** Import the 6 CSVs straight into any BI tool and have a credible SaaS growth dashboard in under 10 minutes — no cleanup, no modeling.
+
+Built by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic — no real company, customer, or personal data.
+
+## What's in the box
+
+| File | Rows | Grain | Purpose |
+|---|---|---|---|
+| `companies.csv` | 6 | company | Master dimension — 6 synthetic startups spanning 6 distinct growth narratives |
+| `growth_metrics.csv` | 540 | date × company | Daily revenue, MRR, customer counts, CAC, LTV, churn |
+| `channel_performance.csv` | 3,780 | date × company × channel | Marketing channel impressions, clicks, conversions, cost, attribution |
+| `customer_segments.csv` | 18 | company × segment | SMB / Mid-Market / Enterprise unit economics |
+| `metric_definitions.csv` | 7 | metric | Self-documenting formulas |
+| `dashboard_suggestions.csv` | 8 | chart | 4 starter dashboards with suggested axes |
+
+**Period:** 90 days. **Currency:** USD. **Dates:** ISO-8601 (`YYYY-MM-DD`). **Join key:** `company_id`.
+
+## Growth narratives included
+
+Each company embodies a distinct SaaS growth profile — so dashboards show realistic variance instead of random noise:
+
+- **Steady PLG** — strong SEO/content/referral, efficient long-term growth
+- **Paid accelerator** — aggressive paid acquisition, higher CAC
+- **Enterprise lumpy** — quarter-end deal spikes, lower churn
+- **Seasonal B2C** — demand seasonality and periodic swings
+- **Churn recovery** — visible churn event followed by stabilization
+- **Capital infusion** — growth acceleration after mid-period expansion
+
+## Why this dataset
+
+**Clean joins, zero cleanup.** Stable IDs, one clear grain per table, no null-heavy columns, no ambiguous foreign keys. Import order: companies → growth_metrics → channel_performance → customer_segments.
+
+**Pre-calculated SaaS metrics.** MRR, CAC, LTV, churn rate, conversion rate, CTR — all included, formulas documented in `metric_definitions.csv`. Users get to insight on first import.
+
+**Cross-table consistency.** Daily channel `conversions` sum exactly to `new_customers`. Daily channel `cost` sums exactly to `marketing_spend`. Active customer counts respect `prev + new − churned = active` on every row.
+
+**Realistic magnitudes.** Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.
+
+## Use cases
+
+- Instant demo dashboards for BI / analytics tools
+- User onboarding & first-value experiences
+- SaaS metrics dashboard templates
+- Product showcase & sales enablement
+- Analytics workflow testing (imports, joins, filters)
+- Startup & growth analytics education
+- Customer success & retention analysis
+- Marketing performance & attribution analysis
+
+## Quick start
+
+```
+companies.csv           → dimension table
+growth_metrics.csv      → primary fact (time × company)
+channel_performance.csv → secondary fact (time × company × channel)
+customer_segments.csv   → segment roll-up
+```
+
+Join key is `company_id`. All dates are ISO-8601. All currency is USD.
+
+**Suggested first dashboard: SaaS Growth Overview**
+- Line chart: `date` × `revenue`, filter by `company_name`
+- Dual-axis line: `date` × (`mrr`, `active_customers`), filter by `company_name`
+
+Full dashboard recipes in `dashboard_suggestions.csv`.
+
+### Load with pandas
+
+```python
+import pandas as pd
+
+companies = pd.read_csv("companies.csv")
+growth = pd.read_csv("growth_metrics.csv", parse_dates=["date"])
+channels = pd.read_csv("channel_performance.csv", parse_dates=["date"])
+segments = pd.read_csv("customer_segments.csv")
+
+# Monthly MRR per company
+monthly_mrr = (
+    growth.assign(month=growth["date"].dt.to_period("M"))
+          .groupby(["company_name", "month"])["mrr"].mean()
+          .reset_index()
+)
+```
+
+## Data quality checklist
+
+- All foreign keys resolve (0 orphans)
+- No nulls in required columns
+- No negative revenue, spend, or counts
+- Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)
+- Continuity invariant holds: `prev_active + new − churned = active` on every row
+- `impressions ≥ clicks ≥ conversions` on every channel row
+
+## Schema
+
+See `SCHEMA.md` for full column definitions, join model, metric formulas, and synthetic profile documentation.
+
+## License
+
+Released under **CC BY 4.0** — use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.
+
+Synthetic data only — no real company, customer, or personal information.
+
+## Get the full pack
+
+This repo is a **6-company, 90-day sample**. The production pack scales to any company count (12 / 50 / 500+), any date range (1 quarter / 1 year / 3 years), any seed for reproducibility, custom growth-profile mixes, and custom industry / channel configurations.
+
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) - per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+
+## Citation
+
+```bibtex
+@dataset{solstice_saas_growth_pack_2026,
+  title        = {Solstice SaaS Growth Pack (Sample)},
+  author       = {Solstice AI Studio},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/saas-growth-pack}
+}
+```

dataset-metadata.json

@@ -1,20 +0,0 @@
-{
-  "title": "Solstice SaaS Growth Pack (Sample)",
-  "id": "justin_solstice/solstice-saas-growth-pack",
-  "licenses": [{"name": "CC-BY-4.0"}],
-  "keywords": [
-    "business",
-    "saas",
-    "synthetic",
-    "analytics",
-    "business intelligence",
-    "dashboards",
-    "startup",
-    "growth",
-    "marketing",
-    "churn",
-    "tabular"
-  ],
-  "collaborators": [],
-  "data": []
-}

metadata/dataset-metadata.json

@@ -0,0 +1,266 @@
+{
+  "title": "Solstice SaaS Growth Pack (Sample)",
+  "id": "justinsolstice/solstice-saas-growth-pack",
+  "subtitle": "Dashboard-ready synthetic SaaS metrics - MRR, CAC, LTV, churn, ready in 10 min",
+  "description": "# Solstice SaaS Growth Pack (Sample)\n\n**A dashboard-ready synthetic SaaS metrics dataset.** Import the 6 CSVs straight into any BI tool and have a credible SaaS growth dashboard in under 10 minutes - no cleanup, no modeling.\n\nBuilt by [Solstice AI Studio](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic - no real company, customer, or personal data.\n\n## What is in the box\n\n| File | Rows | Grain | Purpose |\n|---|---|---|---|\n| companies.csv | 6 | company | Master dimension - 6 synthetic startups spanning 6 distinct growth narratives |\n| growth_metrics.csv | 540 | date x company | Daily revenue, MRR, customer counts, CAC, LTV, churn |\n| channel_performance.csv | 3,780 | date x company x channel | Marketing channel impressions, clicks, conversions, cost, attribution |\n| customer_segments.csv | 18 | company x segment | SMB / Mid-Market / Enterprise unit economics |\n| metric_definitions.csv | 7 | metric | Self-documenting formulas |\n| dashboard_suggestions.csv | 8 | chart | 4 starter dashboards with suggested axes |\n\n**Period:** 90 days. **Currency:** USD. **Dates:** ISO-8601 (YYYY-MM-DD). **Join key:** company_id.\n\n## Growth narratives included\n\nEach company embodies a distinct SaaS growth profile - so dashboards show realistic variance instead of random noise:\n\n- **Steady PLG** - strong SEO/content/referral, efficient long-term growth\n- **Paid accelerator** - aggressive paid acquisition, higher CAC\n- **Enterprise lumpy** - quarter-end deal spikes, lower churn\n- **Seasonal B2C** - demand seasonality and periodic swings\n- **Churn recovery** - visible churn event followed by stabilization\n- **Capital infusion** - growth acceleration after mid-period expansion\n\n## Why this dataset\n\n**Clean joins, zero cleanup.** Stable IDs, one clear grain per table, no null-heavy columns, no ambiguous foreign keys. Import order: companies -> growth_metrics -> channel_performance -> customer_segments.\n\n**Pre-calculated SaaS metrics.** MRR, CAC, LTV, churn rate, conversion rate, CTR - all included, formulas documented in metric_definitions.csv. Users get to insight on first import.\n\n**Cross-table consistency.** Daily channel conversions sum exactly to new_customers. Daily channel cost sums exactly to marketing_spend. Active customer counts respect prev + new - churned = active on every row.\n\n**Realistic magnitudes.** Daily revenue reconciles to MRR over a month. ARR, LTV:CAC, and payback periods sit in credible SaaS ranges.\n\n## Use cases\n\n- Instant demo dashboards for BI / analytics tools\n- User onboarding and first-value experiences\n- SaaS metrics dashboard templates\n- Product showcase and sales enablement\n- Analytics workflow testing (imports, joins, filters)\n- Startup and growth analytics education\n- Customer success and retention analysis\n- Marketing performance and attribution analysis\n\n## Quick start\n\n```\ncompanies.csv           -> dimension table\ngrowth_metrics.csv      -> primary fact (time x company)\nchannel_performance.csv -> secondary fact (time x company x channel)\ncustomer_segments.csv   -> segment roll-up\n```\n\nJoin key is company_id. All dates are ISO-8601. All currency is USD.\n\n**Suggested first dashboard: SaaS Growth Overview**\n- Line chart: date x revenue, filter by company_name\n- Dual-axis line: date x (mrr, active_customers), filter by company_name\n\nFull dashboard recipes in dashboard_suggestions.csv.\n\n### Load with pandas\n\n```python\nimport pandas as pd\n\ncompanies = pd.read_csv(\"companies.csv\")\ngrowth = pd.read_csv(\"growth_metrics.csv\", parse_dates=[\"date\"])\nchannels = pd.read_csv(\"channel_performance.csv\", parse_dates=[\"date\"])\nsegments = pd.read_csv(\"customer_segments.csv\")\n\n# Monthly MRR per company\nmonthly_mrr = (\n    growth.assign(month=growth[\"date\"].dt.to_period(\"M\"))\n          .groupby([\"company_name\", \"month\"])[\"mrr\"].mean()\n          .reset_index()\n)\n```\n\n## Data quality checklist\n\n- All foreign keys resolve (0 orphans)\n- No nulls in required columns\n- No negative revenue, spend, or counts\n- Derived metrics reproduce from inputs (mrr, cac, ltv, churn_rate, conversion_rate, click_through_rate)\n- Continuity invariant holds: prev_active + new - churned = active on every row\n- impressions >= clicks >= conversions on every channel row\n\n## License\n\nReleased under **CC BY 4.0** - use freely for demos, research, internal tooling, education, and commercial templates. Attribution appreciated.\n\nSynthetic data only - no real company, customer, or personal information.\n\n## Get the full pack\n\nThis is a **6-company, 90-day sample**. The production pack scales to any company count (12 / 50 / 500+), any date range (1 quarter / 1 year / 3 years), any seed for reproducibility, custom growth profile mixes, and custom industry / channel configurations.\n\n**Self-serve (Stripe checkout):**\n- [**Sample Scale tier - $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) - ~25K records, one subject, 72-hour delivery.\n\n**Full pack + enterprise scope:**\n- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) - per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.\n\n**Procurement catalog:**\n- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) - available via Datarade / Monda.\n\n## Citation\n\n```\n@dataset{solstice_saas_growth_pack_2026,\n  title        = {Solstice SaaS Growth Pack (Sample)},\n  author       = {Solstice AI Studio},\n  year         = {2026},\n  publisher    = {Kaggle},\n  url          = {https://www.kaggle.com/datasets/justinsolstice/solstice-saas-growth-pack}\n}\n```\n",
+  "licenses": [
+    {
+      "name": "CC BY 4.0"
+    }
+  ],
+  "keywords": [
+    "business",
+    "marketing",
+    "tabular",
+    "finance"
+  ],
+  "collaborators": [],
+  "isPrivate": false,
+  "expectedUpdateFrequency": "never",
+  "userSpecifiedSources": "Generated synthetically by Solstice AI Studio (solsticestudio.ai) using a proprietary SaaS growth simulation engine. 100% synthetic - no real company, customer, or personal information. Released as a free sample of a larger commercial pack.",
+  "image": "cover.png",
+  "data": [
+    {
+      "name": "companies.csv",
+      "description": "Master company dimension. 6 synthetic startups each embodying a distinct growth narrative (steady PLG, paid accelerator, enterprise lumpy, seasonal B2C, churn recovery, capital infusion). Foreign key: company_id.",
+      "totalBytes": 549,
+      "columns": [
+        {
+          "name": "company_id",
+          "description": "Stable primary key for the company (e.g. CO-0001). Foreign key target for all other tables."
+        },
+        {
+          "name": "company_name",
+          "description": "Human-readable company name (synthetic)."
+        },
+        {
+          "name": "industry",
+          "description": "Industry label for the synthetic company (e.g. Collaboration SaaS, FinTech SaaS)."
+        },
+        {
+          "name": "growth_style",
+          "description": "One of 6 growth narratives: steady_plg, paid_accelerator, enterprise_lumpy, seasonal_b2c, churn_recovery, capital_infusion."
+        },
+        {
+          "name": "founded_date",
+          "description": "Founding date (ISO-8601, YYYY-MM-DD)."
+        },
+        {
+          "name": "avg_revenue_per_customer",
+          "description": "Average monthly revenue per active customer in USD."
+        },
+        {
+          "name": "gross_margin_pct",
+          "description": "Gross margin as a percentage (e.g. 0.78 = 78%)."
+        },
+        {
+          "name": "initial_active_customers",
+          "description": "Active customer count at the start of the simulation window."
+        }
+      ]
+    },
+    {
+      "name": "growth_metrics.csv",
+      "description": "Primary fact table. Daily revenue, MRR, customer counts, CAC, LTV, and churn per company across 90 days. Grain: date x company. 540 rows (6 companies x 90 days).",
+      "totalBytes": 45436,
+      "columns": [
+        {
+          "name": "date",
+          "description": "Daily timestamp (ISO-8601, YYYY-MM-DD)."
+        },
+        {
+          "name": "company_id",
+          "description": "Foreign key to companies.company_id."
+        },
+        {
+          "name": "company_name",
+          "description": "Denormalized company name for quick grouping in BI tools."
+        },
+        {
+          "name": "revenue",
+          "description": "Daily revenue in USD."
+        },
+        {
+          "name": "mrr",
+          "description": "Monthly Recurring Revenue in USD (rolling). Reconciles with daily revenue over a month."
+        },
+        {
+          "name": "new_customers",
+          "description": "Count of customers acquired this day. Sum across channels equals channel_performance.conversions for the same date+company."
+        },
+        {
+          "name": "churned_customers",
+          "description": "Count of customers lost this day."
+        },
+        {
+          "name": "active_customers",
+          "description": "Customers active at end of day. Respects prev_active + new - churned = active."
+        },
+        {
+          "name": "cac",
+          "description": "Customer Acquisition Cost in USD (marketing_spend / new_customers)."
+        },
+        {
+          "name": "ltv",
+          "description": "Customer Lifetime Value in USD (avg_revenue_per_customer * gross_margin / churn_rate)."
+        },
+        {
+          "name": "marketing_spend",
+          "description": "Total marketing spend in USD. Equals sum of channel_performance.cost for the same date+company."
+        },
+        {
+          "name": "churn_rate",
+          "description": "Daily churn rate (churned_customers / prev_active)."
+        }
+      ]
+    },
+    {
+      "name": "channel_performance.csv",
+      "description": "Marketing channel performance at daily grain. Impressions, clicks, conversions, cost, and attributed revenue per channel per company per day. Channel totals reconcile exactly to growth_metrics (conversions = new_customers, cost = marketing_spend). Grain: date x company x channel. 3,780 rows.",
+      "totalBytes": 278512,
+      "columns": [
+        {
+          "name": "date",
+          "description": "Daily timestamp (ISO-8601, YYYY-MM-DD)."
+        },
+        {
+          "name": "company_id",
+          "description": "Foreign key to companies.company_id."
+        },
+        {
+          "name": "company_name",
+          "description": "Denormalized company name for quick grouping."
+        },
+        {
+          "name": "channel",
+          "description": "Marketing channel: paid_search, social, content, email, referral, direct, affiliate."
+        },
+        {
+          "name": "impressions",
+          "description": "Ad or content impressions delivered (impressions >= clicks invariant)."
+        },
+        {
+          "name": "clicks",
+          "description": "Clicks generated (clicks >= conversions invariant)."
+        },
+        {
+          "name": "conversions",
+          "description": "New customers attributed to this channel. Sums to growth_metrics.new_customers per date+company."
+        },
+        {
+          "name": "cost",
+          "description": "Marketing spend in USD for this channel. Sums to growth_metrics.marketing_spend per date+company."
+        },
+        {
+          "name": "revenue_generated",
+          "description": "Revenue attributed to this channel in USD."
+        },
+        {
+          "name": "conversion_rate",
+          "description": "Conversions / clicks."
+        },
+        {
+          "name": "click_through_rate",
+          "description": "Clicks / impressions."
+        }
+      ]
+    },
+    {
+      "name": "customer_segments.csv",
+      "description": "Segment-level unit economics. Average LTV, CAC, churn rate, and revenue per segment (SMB, Mid-Market, Enterprise) per company. 18 rows (6 companies x 3 segments).",
+      "totalBytes": 1078,
+      "columns": [
+        {
+          "name": "company_id",
+          "description": "Foreign key to companies.company_id."
+        },
+        {
+          "name": "company_name",
+          "description": "Denormalized company name."
+        },
+        {
+          "name": "segment",
+          "description": "Customer segment: SMB, Mid-Market, or Enterprise."
+        },
+        {
+          "name": "avg_ltv",
+          "description": "Average customer lifetime value for this segment (USD)."
+        },
+        {
+          "name": "avg_cac",
+          "description": "Average customer acquisition cost for this segment (USD)."
+        },
+        {
+          "name": "churn_rate",
+          "description": "Segment-level monthly churn rate."
+        },
+        {
+          "name": "avg_revenue",
+          "description": "Average monthly revenue per customer in this segment (USD)."
+        }
+      ]
+    },
+    {
+      "name": "metric_definitions.csv",
+      "description": "Self-documenting metric catalog. Each row is one SaaS metric with definition, formula, source table, and grain. 7 metrics.",
+      "totalBytes": 1269,
+      "columns": [
+        {
+          "name": "metric_name",
+          "description": "Canonical metric name (e.g. mrr, cac, ltv, churn_rate)."
+        },
+        {
+          "name": "definition",
+          "description": "Plain-English definition of the metric."
+        },
+        {
+          "name": "formula",
+          "description": "Mathematical formula or computation used to derive the metric."
+        },
+        {
+          "name": "table_name",
+          "description": "Which CSV file the metric appears in."
+        },
+        {
+          "name": "grain",
+          "description": "Row grain of the metric (e.g. date x company, company x segment)."
+        }
+      ]
+    },
+    {
+      "name": "dashboard_suggestions.csv",
+      "description": "Starter dashboard recipes. 8 charts spanning 4 dashboard themes with suggested chart type, x/y axes, and filters. Paste straight into any BI tool.",
+      "totalBytes": 921,
+      "columns": [
+        {
+          "name": "dashboard_name",
+          "description": "Named dashboard recipe (e.g. SaaS Growth Overview, Marketing Attribution)."
+        },
+        {
+          "name": "chart_name",
+          "description": "Short chart title."
+        },
+        {
+          "name": "chart_type",
+          "description": "Chart visualization type (line, bar, dual_axis_line, stacked_bar, etc)."
+        },
+        {
+          "name": "primary_table",
+          "description": "Main CSV this chart pulls from."
+        },
+        {
+          "name": "x_axis",
+          "description": "Suggested x-axis column."
+        },
+        {
+          "name": "y_axis",
+          "description": "Suggested y-axis column(s) - comma-separated when dual-axis."
+        },
+        {
+          "name": "filter_suggestion",
+          "description": "Recommended filter (usually company_name for per-company dashboards)."
+        }
+      ]
+    }
+  ]
+}