data-export

Name: data-export
Author: indranilbanerjee

92installs91forks

How to install data-export?

indranilbanerjee/digital-marketing-pro/data-export

$npx -y skills add indranilbanerjee/digital-marketing-pro --skill data-export

Installs into the current project.

Summary

Export marketing data. Use when: sending data to BigQuery, Google Sheets, or Supabase for analysis or reporting.

Files · 1

View on GitHub

SKILL.md

1	# /digital-marketing-pro:data-export
2
3	## Purpose
4
5	Export marketing data — metrics, contacts, campaign results, and performance snapshots — to an external data store for analysis, reporting, or integration with other tools. Supports BigQuery for data warehousing and advanced analytics, Google Sheets for sharing and collaboration with stakeholders, and Supabase for custom database use and application integration. Transforms raw marketing data into clean, structured, tabular formats ready for downstream consumption with full schema documentation. Handles PII redaction when exporting contact data to shared destinations, ensuring compliance with privacy regulations.
6
7	Use this command to move data out of the marketing system for external analysis, client reporting, or data warehouse integration. For exporting audience segments specifically, use `/digital-marketing-pro:segment-audience` to create the segment first, then this command to export the member data.
8
9	## Execution gate (MANDATORY — cannot be skipped)
10
11	1. Present the full preview — recipients / spend / changes / compliance — as an Execution Summary before touching any live system.
12	2. The user must type `yes` (or an equivalent explicit approval). ANY other input — ambiguous, implied, partial, or absent approval — cancels the run.
13	3. Never proceed on ambiguous input. Never auto-retry a failed execution; a failure needs human review before any re-run.
14	4. Record the approval with `python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action create-approval --data '{"risk_level":"<tier>","summary":"..."}'` before executing, then `python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action mark-executed --id {approval_id}` after the platform confirms success.
15
16	## Input Required
17
18	The user must provide (or will be prompted for):
19
20	- Data type: What to export — metrics (KPIs, channel performance, funnel metrics), contacts (CRM records, segments, lead lists), campaigns (structure, settings, targeting, creative), performance (daily/weekly snapshots, trend data, year-over-year comparisons), or custom query (specific fields and filters defined by the user)
21	- Destination: Where to export — BigQuery (project, dataset, and table name), Google Sheets (existing spreadsheet ID or create new with specified name), or Supabase (project reference, schema, and table name)
22	- Date range: Time period for the export — specific start and end dates, relative window (last 7/30/90/365 days), quarter-to-date, year-to-date, or all available historical data
23	- Filters (optional): Criteria to narrow the export — specific channels, campaigns, audience segments, geographic markets, device types, performance thresholds (e.g., only campaigns with ROAS above 2.0), or custom field values
24	- Format preferences: Column ordering priority (dimensions first or metrics first), naming conventions (snake_case, camelCase, Title Case), date format (ISO 8601, MM/DD/YYYY, YYYY-MM-DD), currency formatting (symbol, code, decimal places), timezone for timestamps, and whether to include calculated fields (percentages, ratios, period-over-period deltas)
25	- Append or replace: Whether to append new data to existing destination table/sheet or replace the entire contents — critical for recurring exports where append prevents data duplication while replace ensures a clean snapshot
26	- Schema preferences (optional): Custom column definitions, data types, or transformations — e.g., "split full name into first_name and last_name", "convert all currencies to USD", "aggregate daily data to weekly", or "pivot channels into columns"
27	- Scheduling (optional): Whether this is a one-time export or should be saved as a recurring export template — if recurring, specify frequency (daily, weekly, monthly) and any conditional triggers (e.g., only export when new data is available)
28	- Access permissions (optional): For Google Sheets, who should have access (specific emails, domain-wide, or public link). For BigQuery, which service accounts or users need query access. For Supabase, which API keys or roles need read access.
29	- PII handling (optional): Whether to redact, hash, or anonymize personally identifiable information — relevant when exporting contact data to shared destinations or less-secured environments
30	- Comparison baseline (optional): Whether to include prior period data alongside the current export for trend analysis — e.g., include both current month and previous month side-by-side, or add period-over-period change columns
31	- Summary row preferences (optional): Whether to include aggregation rows — totals, averages, or weighted averages at the bottom of the export for quick reference without additional calculation
32	- **N

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data-export

92installs91forks

View on GitHub

How to install data-export?

indranilbanerjee/digital-marketing-pro/data-export

$npx -y skills add indranilbanerjee/digital-marketing-pro --skill data-export

Installs into the current project.

Summary

Export marketing data. Use when: sending data to BigQuery, Google Sheets, or Supabase for analysis or reporting.

Files · 1

View on GitHub

SKILL.md

1	# /digital-marketing-pro:data-export
2
3	## Purpose
4
5	Export marketing data — metrics, contacts, campaign results, and performance snapshots — to an external data store for analysis, reporting, or integration with other tools. Supports BigQuery for data warehousing and advanced analytics, Google Sheets for sharing and collaboration with stakeholders, and Supabase for custom database use and application integration. Transforms raw marketing data into clean, structured, tabular formats ready for downstream consumption with full schema documentation. Handles PII redaction when exporting contact data to shared destinations, ensuring compliance with privacy regulations.
6
7	Use this command to move data out of the marketing system for external analysis, client reporting, or data warehouse integration. For exporting audience segments specifically, use `/digital-marketing-pro:segment-audience` to create the segment first, then this command to export the member data.
8
9	## Execution gate (MANDATORY — cannot be skipped)
10
11	1. Present the full preview — recipients / spend / changes / compliance — as an Execution Summary before touching any live system.
12	2. The user must type `yes` (or an equivalent explicit approval). ANY other input — ambiguous, implied, partial, or absent approval — cancels the run.
13	3. Never proceed on ambiguous input. Never auto-retry a failed execution; a failure needs human review before any re-run.
14	4. Record the approval with `python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action create-approval --data '{"risk_level":"<tier>","summary":"..."}'` before executing, then `python "${CLAUDE_PLUGIN_ROOT}/scripts/approval-manager.py" --brand {slug} --action mark-executed --id {approval_id}` after the platform confirms success.
15
16	## Input Required
17
18	The user must provide (or will be prompted for):
19
20	- Data type: What to export — metrics (KPIs, channel performance, funnel metrics), contacts (CRM records, segments, lead lists), campaigns (structure, settings, targeting, creative), performance (daily/weekly snapshots, trend data, year-over-year comparisons), or custom query (specific fields and filters defined by the user)
21	- Destination: Where to export — BigQuery (project, dataset, and table name), Google Sheets (existing spreadsheet ID or create new with specified name), or Supabase (project reference, schema, and table name)
22	- Date range: Time period for the export — specific start and end dates, relative window (last 7/30/90/365 days), quarter-to-date, year-to-date, or all available historical data
23	- Filters (optional): Criteria to narrow the export — specific channels, campaigns, audience segments, geographic markets, device types, performance thresholds (e.g., only campaigns with ROAS above 2.0), or custom field values
24	- Format preferences: Column ordering priority (dimensions first or metrics first), naming conventions (snake_case, camelCase, Title Case), date format (ISO 8601, MM/DD/YYYY, YYYY-MM-DD), currency formatting (symbol, code, decimal places), timezone for timestamps, and whether to include calculated fields (percentages, ratios, period-over-period deltas)
25	- Append or replace: Whether to append new data to existing destination table/sheet or replace the entire contents — critical for recurring exports where append prevents data duplication while replace ensures a clean snapshot
26	- Schema preferences (optional): Custom column definitions, data types, or transformations — e.g., "split full name into first_name and last_name", "convert all currencies to USD", "aggregate daily data to weekly", or "pivot channels into columns"
27	- Scheduling (optional): Whether this is a one-time export or should be saved as a recurring export template — if recurring, specify frequency (daily, weekly, monthly) and any conditional triggers (e.g., only export when new data is available)
28	- Access permissions (optional): For Google Sheets, who should have access (specific emails, domain-wide, or public link). For BigQuery, which service accounts or users need query access. For Supabase, which API keys or roles need read access.
29	- PII handling (optional): Whether to redact, hash, or anonymize personally identifiable information — relevant when exporting contact data to shared destinations or less-secured environments
30	- Comparison baseline (optional): Whether to include prior period data alongside the current export for trend analysis — e.g., include both current month and previous month side-by-side, or add period-over-period change columns
31	- Summary row preferences (optional): Whether to include aggregation rows — totals, averages, or weighted averages at the bottom of the export for quick reference without additional calculation
32	- **N

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6 picks

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