>- Guides adding, changing, and reviewing telemetry through the `@n8n/telemetry` event registry. Use when working on telemetry, analytics, tracking, product events, `track()` calls, or RudderStack/PostHog product events, in frontend or backend code — and whenever you need to find which registered telemetry events exist, what an event means, or what properties it carries.
npx skills add https://github.com/n8n-io/n8n --skill n8n:telemetry
Events migrated to the registry live in packages/@n8n/telemetry as one entry per event — its exact emitted name, a description, and a zod schema typing its properties — organized per product domain in src/events/ and composed into TELEMETRY_EVENT.<DOMAIN>.<EVENT>. The package defines registered events and never depends on transport SDKs.
To find which events are registered, what they mean, or what properties they carry, run the catalog first:
pnpm --filter @n8n/telemetry catalog # human-readable, grouped by domain
pnpm --filter @n8n/telemetry catalog --json # structured, for programmatic use
The registry is being adopted incrementally. Events not yet registered do not appear in the catalog, so search track() call sites when the catalog has no match.
Pass the entry itself to track() — it resolves the emitted name internally:
import { TELEMETRY_EVENT } from '@n8n/telemetry';
telemetry.track(TELEMETRY_EVENT.PLATFORM.USER_IS_PART_OF_EXPERIMENT, {
name: experimentName,
variant,
});
Both track() implementations accept registry entries and plain strings. Plain strings remain supported for events that have not yet migrated:
packages/frontend/editor-ui/src/app/plugins/telemetry/index.tspackages/cli/src/telemetry/index.tsEntries get property autocomplete and compile-time checks — typo'd, missing, or wrongly typed properties fail typecheck. When the telemetry transport is initialized, track() additionally validates registered-event payloads via getEventValidationError (shared from @n8n/telemetry) and logs a warning on mismatch, including unrecognized properties that slipped past structural typing. A validation warning does not stop the event from being emitted.
pnpm --filter @n8n/telemetry catalog). If an existing event covers the same user action from another surface, augment it with a property instead of adding a near-duplicate event.User opened Credential modal is CREDENTIALS whether opened from the NDV, template setup, or chat. The trigger context goes into a source property.User pinned node data). No template interpolation in names — variability goes into properties. The name must snake_case cleanly into a BigQuery table name: no punctuation beyond spaces, no casing that collides after snake_casing.description stating what the event means and when it fires — a registry test rejects blank descriptions. Document individual properties with .describe() where the key alone is not obvious.import { z } from 'zod/v4'): snake_case keys, explicit .optional() where a call site may omit a value, z.looseObject()/.catchall() for genuinely dynamic remainders. Schemas must stay JSON-Schema-representable — no transforms, refinements, or z.date() (a registry test enforces this via z.toJSONSchema()).useTelemetry().track(...); backend either through a RelayEventMap handler in packages/cli/src/events/relays/telemetry.event-relay.ts (event-bus-driven) or a direct Telemetry.track(...) call — both reference the same registry entry..meta({ deprecated: true })) instead of removing.Do not retype event-name literals in tests:
useTelemetry().track or the backend Telemetry.track service and expect the registry entry itself with the payload.window.rudderanalytics.track to receive entry.name and the augmented payload.event field to equal entry.name and its properties to include the event payload.Experiment exposure and metric events follow n8n:experiments (.agents/skills/experiments/SKILL.md).
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I''m stuck", "Im stuck", "I''m confused", "Im confused", "I don''t understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn''t this work", "why does not this work", "I''m a beginner", "Im a beginner", "I''m learning", "Im learning", "I''m new to this", "Im new to this", "walk me through", "how does this work", "what''s wrong with my code", "what''s wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
Take n8n-io/n8n:telemetry from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.