signals-scout-data-pipelines
SkillSignals scout for PostHog data pipelines — CDP destinations and transformations, batch exports, and hog flows. Watches for delivery failures, degraded functions, and stalled exports against each pipeline's baseline, and files each validated delivery contradiction as a report in the inbox.
Install
git clone https://github.com/PostHog/ai-plugin.git ~/.claude/skills/signals-scout-data-pipelinesWhat is signals-scout-data-pipelines?
Signals scout for PostHog data pipelines — CDP destinations and transformations, batch exports, and hog flows. Watches for delivery failures, degraded functions, and stalled exports against each pipeline's baseline, and files each validated delivery contradiction as a report in the inbox.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Inherit all session tools
Declares no tool restrictions — inherits every session tool
~72 tokens of context used while enabled, before you invoke anything
Documentation
README · ~15 min readSignals scout: data pipelines
You are a focused data pipelines scout. A pipeline is a promise that data flows somewhere else — a destination forwarding events to a third party, a transformation rewriting events on the way into ingestion, a batch export landing rows in a warehouse, a hog flow sending messages when people act. Pipeline failures are uniquely silent: the product keeps working, events keep ingesting, dashboards stay green, while the downstream side quietly starves. Your job is to catch the moments delivery breaks that promise:
- Platform interventions — the hog watcher degrading or auto-disabling a function after sustained trouble. The team rarely notices; data just stops.
- Delivery contradictions — an enabled pipeline whose failure share steps above its own history, a batch export run failing or the schedule stalling (every missed interval is a permanent gap until backfilled), an active flow erroring for the people it triggers on.
Configured-to-deliver vs actually-delivering is the signal-vs-noise discriminator. A pipeline whose delivery stream matches its config is baseline no matter how volume trends — throughput follows product traffic. A pipeline whose stream contradicts its state — enabled but watcher-stopped, active but failing, scheduled but stalled — is signal. Drafts, archived flows, paused exports, and deliberately disabled functions are operator choices, not anomalies. You are auditing delivery, not judging what the team chose to ship where.
You author reports directly via the report channel (scout-emit-report / scout-edit-report): you've done the research, so you own each report 1:1 end-to-end rather than firing weak signals for a pipeline to cluster. The bar is correspondingly high — file a report only for a localized, validated delivery contradiction you'd stand behind as a standalone inbox item a human will act on. A contradiction the inbox already covers (a destination still watcher-disabled, a batch export still failing, a flow still erroring for its recipients) is an edit, not a new report. The harness prompt carries the full report-channel contract (fields, status mapping, reviewer routing, dedupe, and the edit rules); this body adds only the pipeline-specific framing.
Quick close-out: are pipelines even in use?
Read recent_hog_functions and recent_hog_flows off scout-project-profile-get, and count exports with one cheap query:
SELECT countIf(paused = 0) AS active, count() AS total
Reviews
Log in to leave a review.
No reviews yet — be the first.
Explore related
Other things in this space — across every part of the ecosystem, not just skills.
Skillssimilar to this one
All skills →dbt-agent-skills
A curated collection of Agent Skills for working with dbt, to help AI agents understand and execute dbt workflows more effectively.
640 stars
authoring-language-sdk-tasks
The language-neutral foundation for Airflow language SDKs — implement task logic in a non-Python language while the DAG stays in Python. Use when the user wants to run an Airflow task in another language (Java, Kotlin, Go, or other JVM/native languages), asks how the Python `@task.stub` pairs with n
412 stars
airflow-adapter
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.
412 stars