Export

Spotify Ads API — Campaign Data Export

Export campaign hierarchies to CSV for offline review, combining entity data with optional performance metrics.

Setup

Set the plugin root and define the request wrapper:

PLUGIN_ROOT="${CODEX_PLUGIN_ROOT:-${CLAUDE_PLUGIN_ROOT:-.}}"
api() { "$PLUGIN_ROOT/scripts/api-request.sh" export "$@"; }

Before the first Ads API v3 call, read and follow $PLUGIN_ROOT/skills/api-reference/references/live-openapi.md.

To retrieve settings values (TOKEN, AD_ACCOUNT_ID, AUTO_EXECUTE, BASE_URL, SDK_HEADER, SKILL_HEADER, PLUGIN_VERSION) for use outside API calls, run api --env. The output is eval-safe, so eval $(api --env) assigns them all.

Parsing Arguments

• No argument → Export all campaigns
• <campaign_id> (UUID) → Export a specific campaign
• --metrics → Include performance metrics (impressions, spend, reach, etc.)
• --date-range <start> <end> → Metric date range (ISO 8601). Default: last 30 days.
• If ambiguous, ask the user.

Step 1: Ask Export Preferences

Ask the user to confirm:

• Scope: All campaigns or a specific campaign?
• Include metrics? Entity data only, or include performance metrics?
• Output path: Default ./spotify-ads-export-YYYY-MM-DD.csv, or user-specified path.

Step 2: Fetch Entity Data

Fetch all entity data with full pagination. Unlike other skills that show the first page, export must retrieve every entity to produce a complete file.

Fetch campaigns

api GET "ad_accounts/{ad_account_id}/campaigns?limit=50&offset=0"

Check paging.total_results in the response. If total_results > 50, make additional requests incrementing offset by 50 until all campaigns are fetched. For a single-campaign export, use:

api GET "ad_accounts/{ad_account_id}/campaigns/$CAMPAIGN_ID"

Fetch ad sets

api GET "ad_accounts/{ad_account_id}/ad_sets?limit=50&offset=0"

For a single campaign: add &campaign_ids=$CAMPAIGN_ID. Paginate with offset until all ad sets are fetched.

Fetch ads

api GET "ad_accounts/{ad_account_id}/ads?limit=50&offset=0"

For a single campaign: add &campaign_ids=$CAMPAIGN_ID. Paginate with offset until all ads are fetched.

Step 3: Fetch Metrics (if requested)

When metrics are included, fetch aggregate reports at each entity level. For a single-campaign export, add entity_ids=$CAMPAIGN_ID&entity_ids_type=CAMPAIGN to every report request so the export does not rely on the first unfiltered page containing the requested campaign's metrics.

Campaign-level metrics

api GET "ad_accounts/{ad_account_id}/aggregate_reports?\
entity_type=CAMPAIGN&\
fields=IMPRESSIONS&fields=SPEND&fields=CLICKS&fields=REACH&fields=FREQUENCY&fields=CTR&fields=COMPLETES&\
granularity=LIFETIME&\
entity_status_type=CAMPAIGN&\
limit=50"

If a date range is specified, switch to granularity=DAY and add &report_start=<start>&report_end=<end>. Use UTC midnight timestamps such as 2026-05-01T00:00:00Z. Do not send report_start or report_end with granularity=LIFETIME. Paginate with continuation_token if present in the response.

Ad set-level metrics

api GET "ad_accounts/{ad_account_id}/aggregate_reports?\
entity_type=AD_SET&\
fields=IMPRESSIONS&fields=SPEND&fields=CLICKS&fields=REACH&fields=FREQUENCY&fields=COMPLETES&fields=COMPLETION_RATE&\
granularity=LIFETIME&\
entity_status_type=AD_SET&\
include_parent_entity=true&\
limit=50"

For a single-campaign export, add &entity_ids=$CAMPAIGN_ID&entity_ids_type=CAMPAIGN. Paginate with continuation_token if present in the response.

Ad-level metrics

api GET "ad_accounts/{ad_account_id}/aggregate_reports?\
entity_type=AD&\
fields=IMPRESSIONS&fields=SPEND&fields=CLICKS&fields=REACH&\
granularity=LIFETIME&\
entity_status_type=AD&\
include_parent_entity=true&\
limit=50"

For a single-campaign export, add &entity_ids=$CAMPAIGN_ID&entity_ids_type=CAMPAIGN. Paginate with continuation_token if present.

Step 4: Build and Write CSV

CSV columns

The CSV is denormalized — one row per ad, with campaign and ad set data repeated on each row. Ad sets with no ads get a row with blank ad columns.

Entity columns:

• campaign_id, campaign_name, campaign_status, campaign_objective
• ad_set_id, ad_set_name, ad_set_status, ad_set_format, ad_set_budget_type, ad_set_budget_amount, ad_set_bid_strategy, ad_set_bid_amount, ad_set_start_time, ad_set_end_time, ad_set_delivery
• ad_set_geo_country, ad_set_geo_regions, ad_set_age_min, ad_set_age_max, ad_set_platforms, ad_set_placements, ad_set_genders
• ad_id, ad_name, ad_status, ad_delivery, ad_tagline, ad_advertiser_name, ad_cta_key, ad_cta_url

Metric columns (when --metrics is used):

• impressions, spend, clicks, reach, frequency, ctr, completes, completion_rate

Data transformations

• Budget/bid amounts: Divide micro_amount by 1,000,000 to display in the billing currency (e.g., 50000000 → 50.00).
• Metric SPEND: Values from aggregate_reports are already in the billing currency — display directly.
• Geo targeting: Flatten to ad_set_geo_country = country code string, ad_set_geo_regions = comma-separated region/DMA/city names if available (IDs if names are not in the response). Note: DMA-level targeting (dma_ids) is no longer available for new ad sets, but older campaigns may still contain DMA data in their geo targets.
• Age ranges: Extract first range's min and max into ad_set_age_min and ad_set_age_max.
• Arrays (platforms, placements, genders): Join with commas (e.g., "ANDROID,DESKTOP,IOS").
• CSV quoting: Wrap values containing commas, quotes, or newlines in double quotes. Escape internal double quotes by doubling them ("").

Join logic

Match entities by ID:

• Each ad belongs to an ad set (via ad_set_id) which belongs to a campaign (via campaign_id).
• Metrics join on entity_id from the report rows to the entity's id.
• If an entity has no metrics (zero impressions, new campaign), include the row with blank metric columns.

Write the file

Write the CSV header and rows with a structured CSV writer. Prefer Python's standard csv module, or use jq @csv if all rows are already available as JSON. Do not build CSV rows with echo or string concatenation; that will corrupt values containing commas, quotes, or newlines.

Example Python shape:

import csv

columns = [
    "campaign_id", "campaign_name", "campaign_status", "campaign_objective",
    "ad_set_id", "ad_set_name", "ad_set_status", "ad_set_format",
    "ad_set_budget_type", "ad_set_budget_amount", "ad_set_start_time",
    "ad_set_end_time", "ad_id", "ad_name", "ad_status", "ad_delivery",
    "impressions", "spend", "clicks", "reach",
]

with open(output_path, "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore")
    writer.writeheader()
    writer.writerows(rows)

Build rows from parsed API JSON before writing. Let the CSV writer handle quoting and escaping.

Step 5: Display Summary

After writing the file, display:

Export complete: ./spotify-ads-export-2026-05-14.csv
  Campaigns: 3
  Ad Sets: 7
  Ads: 12
  Metrics included: Yes (lifetime, last 30 days)
  File size: 4.2 KB

Execution Behavior

• If auto_execute is true, execute all API calls directly and write the file.
• If auto_execute is false, present the curl commands for the first fetch and ask for confirmation before executing. After confirmation, execute all remaining fetches without additional prompts.
• Always check the HTTP_STATUS: line from curl output to determine success or failure before interpreting the response body.
• On error from any fetch, show the error and continue with available data. Note which entity types are missing from the export.
• For large accounts (50+ campaigns), note that pagination will require multiple API calls and may take 30-60 seconds.

Cross-references

• For server-generated async CSV reports with different column structure, use /spotify-ads-api:report async-create.
• For a quick visual overview instead of a file export, use /spotify-ads-api:dashboard.
• After reviewing exported data, use /spotify-ads-api:bulk for batch changes.