使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/posthog/ai-plugin --skill suggesting-data-imports開始前の確認
- 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
Suggesting data imports
This skill helps identify when data the user needs lives outside PostHog and guides them toward importing it via the data warehouse. The key insight is recognizing the gap — then connecting it to the right source type.
What PostHog collects natively
PostHog collects product analytics events, persons, sessions, and groups via its SDKs. Additional products are available but must be enabled: session replay, feature flags, experiments, surveys, web analytics, error tracking, AI observability, conversations, logs, revenue analytics, workflows, CDP destinations, and batch exports. PostHog does not collect external business data like payments, subscriptions, CRM records, support tickets from other systems, or production database tables — that data must be imported via the data warehouse.
When to use this skill
- A HogQL query fails because a table doesn't exist
- The user asks about data from an external system (Stripe, Hubspot, Salesforce, etc.)
- The user wants to correlate PostHog analytics with business data (revenue, support tickets, CRM records, etc)
- The user asks "how do I get my X data into PostHog?"
- Analysis requires joining PostHog events with external data
- The user asks about exporting PostHog data for comparison elsewhere (in a google sheet, external warehouse, etc)
Workflow
1. Understand what data is missing
Listen for signals that the user needs external data:
- They mention a specific tool or system (Stripe, Hubspot, Zendesk, their production database, etc.)
- A query references a table that doesn't exist in PostHog
- They want to analyze something PostHog doesn't track natively (revenue, support tickets, CRM deals, etc.)
If a query failed, check the error — if it's "table not found" or similar, the data likely needs to be imported.
2. Check what's already connected
Call posthog:external-data-sources-list to see existing sources. The data might already be imported but the user doesn't know the table name or prefix.
If a source exists for the system they're asking about, call posthog:external-data-schemas-list to show the available tables. The data might be there but under a different name or prefix.
Also query system.information_schema.tables with posthog:execute-sql to see all queryable tables — the data might already be available as a view or joined table.
3. Identify the right source type
If the data isn't imported yet, call posthog:external-data-sources-wizard to see available source types — when enumerating without source_type, pass fields: ['*.name', '*.caption'] to skip the large per-source config field definitions. Match the user's need to a source:
Common patterns:
| User wants | Source type | Key tables |
|---|---|---|
| Revenue / payment data | Stripe, Chargebee, Shopify | charges, subscriptions, invoices, customers |
| CRM / sales pipeline | Hubspot, Salesforce, Attio | contacts, deals, companies |
| Support tickets | Zendesk | tickets, users, organizations |
| Product data from their DB | Postgres, MySQL, BigQuery, Snowflake, Redshift | user's own tables |
| Marketing / ads | Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads | campaigns, ad_groups, ads |
| Email marketing | Mailchimp, Klaviyo | campaigns, lists, subscribers |
| Project management | Linear | issues, projects |
| Error tracking (external) | Sentry | issues, events |
4. Suggest the import
Present the recommendation concisely:
- What source type to connect
- What tables would become available
- How this enables the analysis they want
Example: "Your Stripe data isn't in PostHog yet. If you connect a Stripe source, you'll get tables like charges, subscriptions, and customers that you can join with PostHog events to analyze revenue by user behavior."
5. Offer to set up the source
If the user wants to proceed, the fastest path is the one-step data-warehouse-source-setup tool (validate creds → discover tables → sync defaults → create, in one call), with data-warehouse-source-connect-link to collect credentials securely in the browser rather than in chat. For anything beyond the happy path (hand-picking tables, non-default sync types, webhooks, CDC), hand off to the `setting-up-a-data-warehouse-source` skill, which covers the full flow, sync-type selection, webhook registration, and prefix guidance. Do not duplicate that workflow here.
6. Show what's possible after import
Once connected, help the user write their first query joining PostHog data with the imported data. Use posthog:execute-sql to demonstrate.
Common join patterns:
- Join Stripe customers with PostHog persons on email:
SELECT * FROM stripe_customers sc JOIN persons p ON sc.email = p.properties.$email - Join CRM deals with events: correlate product usage with sales outcomes
- Join support tickets with session recordings: find recordings for users who filed tickets
Important notes
- Don't guess table names. Always check
system.information_schema.tables(viaposthog:execute-sql) andposthog:external-data-schemas-listbefore saying data doesn't exist. - Check prefixes. Imported tables are often prefixed (e.g.
stripe_chargesnotcharges). The user might not know the prefix. - Collect credentials securely. Use
data-warehouse-source-connect-linkto hand the user a browser link — it opens a minimal connect page rendering the source's full connection form (OAuth or credentials, whichever the source offers) that stashes the details temporarily without creating the source. Afterwards pass{"credential_id": <id>}(discovered viadata-warehouse-stored-credentials-list) todata-warehouse-source-setup— stored credentials are single-use and expire after 24 hours. Don't collect passwords or OAuth tokens in chat. - Not all systems are supported. If the user's system isn't in the wizard list, suggest using Postgres/MySQL as a bridge if they can export to a database, or mention that custom sources can be requested.
- Connecting a source also documents it. After the first sync, PostHog automatically generates semantic descriptions for the imported tables and columns (from the source database's own column comments where present, plus an LLM pass using the table relationships and the team's business context). Those descriptions surface in
system.information_schema.columns(query it withposthog:execute-sql), so once a source is connected the agent can reason about what each column means and how tables join — not just their names and types. Mention this when recommending an import: connecting the source is what makes the data answerable.
Related tools
posthog:external-data-sources-list: Check existing source connectionsposthog:external-data-schemas-list: Check what tables are already importedposthog:execute-sqloversystem.information_schema.*: See all queryable tables including viewsposthog:external-data-sources-wizard: Get available source types (passfields: ['*.name', '*.caption']when enumerating)posthog:data-warehouse-source-connect-link: Get a secure browser/OAuth link to collect credentialsposthog:data-warehouse-source-setup: One-step create (validate, discover tables, apply sync defaults, create)posthog:execute-sql: Run queries to demonstrate what's possible
Related skills
- `setting-up-a-data-warehouse-source`: Full source creation workflow — hand off here once the user decides to connect a source
