taxonomy

name

taxonomy

description

Source of truth for event taxonomy generation, data auditing, and governance best practices in Amplitude. Use when an agent needs to create, validate, audit, score, or recommend improvements to event tracking plans, naming conventions, property standards, data quality, or deprecation workflows. Covers naming rules, property standards, scoring frameworks, safe metadata operations, deprecation procedures, and AI readiness guidance.

Taxonomy Generation & Data Auditing

When to Use

• User asks to create or review a tracking plan or event taxonomy
• User wants to validate event/property naming conventions
• User needs to audit data quality (duplicates, stale events, missing metadata)
• User asks about funnel design or event relationships
• Agent is generating event names or property names and needs to follow standards
• User wants to understand or improve their taxonomy governance
• User asks about reducing event volume or type counts
• User asks about deprecation, blocking, deleting, or hiding events
• Any agent needs a "source of truth" for taxonomy best practices before recommending events
• User asks about AI readiness, AI Controls, or improving AI feature accuracy

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Layer 1: Foundational Concepts

Core Philosophy

Six principles govern all taxonomy work:

1. Evidence-first. Never fabricate. Every finding must be grounded in tool-retrieved data. If something cannot be verified, say so explicitly.
2. Scan aggressively. Propose confidently. Confirm before writing. Paginate autonomously through the full taxonomy. Form a prioritized, opinionated view of what needs fixing — then present it. Never call a write tool without explicit user confirmation.
3. Be opinionated, not neutral. Generic requests ("audit my taxonomy") are an invitation to lead. Use the scoring framework, recommend the highest-impact action first, and explain why. Don't present a menu of equal options.
4. Surface critical issues proactively. If you find something important while working on an adjacent task, raise it. Don't silently ignore a PII violation because the user only asked about naming conventions.
5. Questions extract institutional knowledge. Ask about business intent and real-world meaning, not Amplitude mechanics. One focused question at a time. The goal is to surface knowledge that lives in people's heads.
6. Explain before acting. Before calling any write tool, present exact proposed changes — including before/after state — and wait for explicit confirmation.

Data Quality Lifecycle

All taxonomy governance follows a four-stage loop:

1. Detect — Scan systematically. Paginate through the full taxonomy. Score every finding. Surface issues with evidence before conclusions.
2. Clarify — Ask one focused question to capture semantic truth. Do not suggest actions yet. Seek understanding first.
3. Resolve — Apply metadata-only improvements. Guide humans through phased deprecation for structural changes. Never execute destructive actions unilaterally.
4. Prevent — Recommend conventions and governance habits that stop drift from recurring.

Event Volume vs. Taxonomy Type Counts

These are different problems requiring different solutions:

• Event volume = total event instances ingested per billing period (how many times events fire). Properties do not count toward volume.
• Taxonomy type counts = number of distinct names across all schema dimensions (event types, event property types, user property types, group types, group property types). Each has its own limit.

Billing models — know which applies before advising:

• Event volume billing: customer has a contracted allocation of events per period. Exceeding it triggers overage costs. Flag significant event volume changes to these customers.
• MTU billing: customer is billed based on distinct users who trigger any event in a month. Per-user event counts matter less; total unique user count matters more.

What customers usually mean:

• "I need to reduce my event volume" → worried about billing (volume-billed customers)
• "I need to reduce my event types / schema count" → worried about hitting type limits (new types won't be queryable)

What actually reduces each:

Goal	Action	Reduces Volume?	Reduces Type Count?
Reduce volume	Block event	Yes	No
Reduce volume	Delete event	Yes	Yes
Reduce type count	Delete event/property/group type	—	Yes
Reduce type count	Block event	No	No
Reduce type count	Hide event	No	No

Key rules:

• Blocking and hiding do NOT reduce type count. A quota-constrained customer must delete, not block.
• Never recommend sampling. Sampling breaks funnel charts, journey paths, cohorts, downstream destinations, and Guides.
• Custom events and merged events simplify analysis but do NOT reduce raw event volume.
• When ambiguous, ask: "Are you trying to reduce how many events are being sent, or the number of different event and property types in your taxonomy?"

Event States and Metadata Permissions

Status	Meaning	Can Edit Metadata?
Planned	In tracking plan; not yet instrumented	Yes
Live	Actively receiving data	Yes
Blocked	Stops new ingestion; historical data accessible	Yes
Unexpected	Receiving data but NOT in tracking plan	No — must add to tracking plan first
Deleted	Stops ingestion; removed from new-chart dropdowns	No — must restore first

Unexpected events have special restrictions. No metadata can be updated until the event is added to the tracking plan. When you encounter Unexpected events:

• If they appear legitimate (real product actions, consistent volume): recommend adding to the tracking plan first, then apply metadata.
• If they appear invalid (single-day spikes, test strings, security scan artifacts): treat as a deprecation candidate through the standard safe deprecation process. Always distinguish "legitimate but undocumented" from "truly invalid" before recommending any action.

Activity state is NOT a deprecation signal. An event marked Inactive is behaving as intended.

Actual deprecation signals:

Signal	Interpretation
No recent volume	Event has gone stale
No recent queries	Event is unused
Both together	Strong deprecation candidate

Planned events: Zero volume and queries are expected — evaluate by age, name collisions with Live events, and test-like names instead.

Custom Events, Labeled Events, and Merged Events

None reduce event volume. Each has distinct behavior:

• Custom events (ce: prefix, type = custom): Logical combinations of underlying events for analysis convenience. The underlying events still exist and fire independently. Always check whether an event is used as the basis for a custom event before recommending its deletion — deleting the underlying event may break the custom event silently. Allowed: consolidate duplicate custom events with the same definition; improve naming, descriptions, categories, tags. Never claim that removing a custom event reduces event volume.
• Labeled events (ce: prefix, type = labeled): Designed for use with Autocapture, distinguished from custom events by a separate metadata flag. Adding/deleting does not impact volume.
• Merged events (Transform/Merge): Source events are no longer individually available for analysis after a merge. If the user needs to analyze combined events AND retain independent analysis of source events, recommend a custom event instead of a merge. Allowed: merge truly duplicated events that share the same semantics and where independent analysis is not needed. Never claim that merging reduces event volume.

Protected Data Categories

How to identify category from naming convention: Events with bracket prefixes ([...]) follow a consistent pattern: if the text inside the brackets is a recognizable third-party product brand, it is an integration. If not, it is an Amplitude system event.

Amplitude system events ([Amplitude], [Guides-Surveys], [Experiment], etc.): Critical to platform functionality. Do not recommend blocking, deleting, hiding, or modifying in response to generic cleanup.

Integration-prefixed data ([Appboy], [Adjust], [Intercom], etc.): Can be cleaned up, but recommend turning off at the integration source first. Lower priority than native events.

Experiment data: Do not recommend TTLs or automatic deletion. Deleting breaks historical experiment interpretation.

Interpreting Usage Signals

Query count reflects usage across user-created objects (charts, dashboards, notebooks, cohorts, metrics) but does NOT include AI tools, Chat, Global Agent, MCP, or Alerts. Zero-query is a strong signal to review, not a definitive signal to act.

Three key patterns:

Pattern	Definition	Action
Stale event	Has ingested before, but volume stopped	Confirm with customer before deprecating
Test event	first seen = last seen, single day	Strong deprecation candidate; confirm first
Firing but unqueried	Has volume, zero queries	Flag for review, not immediate removal

Safe to act on: No volume for 6-12 months. Even if query activity exists, those queries return zero results.

AI Readiness

Frame metadata and cleanup work as AI readiness improvements. Every AI feature selects events by evaluating the visible taxonomy — taxonomy quality directly determines AI output quality.

Flag these as AI quality issues:

• Cryptic event names with no description — AI cannot interpret them
• Clusters of duplicate/near-duplicate names — AI will guess incorrectly between them
• Implementation-focused descriptions (e.g., "fires when POST /purchase returns 200") — users ask behavioral questions, not backend questions
• Large numbers of deprecated events still visible — noise that increases wrong AI selection

Event description structure (in order):

1. Non-technical behavior definition — what the user did, in plain language
2. Trigger conditions — exact conditions, UI vs API, success-only or also failure, page/URL pattern
3. Disambiguation — how this differs from similarly-named events
4. Key use cases — if it's a funnel step, success metric, or key analysis input
5. Frequently used properties — 2-3 most commonly queried properties with brief context
6. Technical details (optional) — implementation notes, source system, endpoint

Property descriptions: Start with a clear definition, then include example values. Example: "The category of the product the user viewed. Examples: 'electronics', 'apparel', 'home & garden'."

AI readiness at instrumentation time:

• Choose clear, descriptive event and property names that don't require a display name to be interpretable. Do not recommend adding display names during instrumentation — they are only needed later when the raw name is already established and ambiguous.
• Write descriptions following the structure above: non-technical behavior definition → trigger conditions → disambiguation → key use cases → frequently used properties → optional technical details.
• For properties with coded values (SKUs, IDs, status codes): recommend creating lookup tables mapping codes to human-readable labels (available to Growth and Enterprise customers).

AI Controls recommendations:

• Organization context (10,000 char): company-wide standards, KPI definitions, standard terminology, global filters, fiscal calendar
• Project context (10,000 char): product-specific events/funnels, project-specific metrics, segment definitions
• Use audit findings to populate these recommendations. Recurring jargon or acronyms across multiple events belong in org/project context, not just individual descriptions. Consistent structural patterns (naming conventions, event groupings) are useful project context that helps AI interpret the taxonomy as a whole.

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Layer 2: Rules by Action Type

When Reading and Analyzing (Always Safe)

Reading and analysis operations carry no risk — be autonomous and decisive. For tool usage strategy and step-by-step procedures, see the Data Quality Audit procedure in the governance skill.

When Writing or Updating Metadata

Before/after confirmation required for all writes. Never auto-apply. Only update confirmed items — do not extend to similar items based on pattern inference.

Per-field defaults:

• Descriptions: Do not remove existing content unless clearly erroneous. Append to or incorporate existing detail.
• Categories: Only set when empty. Suggest changing only if clearly incorrect or user requests it.
• Tags: Add only; never remove without explicit request.
• Display names: Follow the project's existing naming conventions.

Restrictions:

• Do not write to bracket-prefixed or vendor-prefixed events unless explicitly requested.
• Never write to Unexpected or Deleted events (must be added to plan / restored first).

When writes fail due to permissions:

• Explain that the user lacks write access.
• Provide read-only guidance on what could be done and why.
• Offer an "Ask an Admin to apply this" summary the user can share.

When Recommending Cleanup or Deprecation

Deprecation must always follow a phased process. For the step-by-step procedure, see the governance skill's Deprecation Workflow.

Never:

• Present delete/hide/block as immediate one-step solutions
• Recommend sampling, TTLs, automatic deletion rules, or moving events between projects
• Recommend reconfiguring upstream integrations for volume control
• Skip dependency checks before recommending deprecation

When Recommending New Instrumentation

Event Naming Standards

Format: [Object] [Past-Tense Verb] in Title Case

Good	Bad	Why
Song Played	Play Song	Past tense = completed action
Form Submitted	Submit Form	Noun-first = scannable, sortable
Product Added	product added, product_added, productAdded	Amplitude treats different casings as separate events — always use Title Case, not snake_case or camelCase

Consistency is the top priority. If an existing taxonomy uses a consistent convention that differs from the ideal, match the existing convention rather than introducing a new pattern.

User perspective, not system perspective:

• Message Sent (user sent) not Message Delivered (system delivered)
• Purchase Completed (user completed) not Payment Processed (system processed)

Specificity balance — one event + properties, not many events:

• Good: Order Completed with property payment_method
• Bad: Credit Card Order Completed, Apple Pay Order Completed

Cross-platform consistency: Same user action = same event name across Web, iOS, Android. Platform differences go in a platform property.

One action = one event name. No duplicates across the codebase.

Autocapture-first: Do not recommend custom events for anything already captured by Autocapture: Page Viewed, Element Clicked, Element Changed, Form Started, etc.

Property Naming Standards

• snake_case for all property names
• Descriptive and specific: payment_type not type, error_message not message
• Include units when ambiguous: video_duration_seconds, file_size_mb, price_usd
• Timestamp convention: [event_name]_at (e.g., product_added_at)
• Consistent across events: Same property name for the same concept everywhere. product_name must be product_name on every event — not name, prod_name, etc.
• Distinct names for distinct concepts: login_method and payment_method, not generic method for both
• Dot notation: Means a nested object was passed (Amplitude creates it automatically). Don't use dot notation in property names directly unless intending nested objects. During audits, a cluster of dot notation properties is a cleanup signal — check which are actually being queried. If a significant portion are unused, recommend trimming the object at the source to reduce taxonomy noise.

Property Type Standards

Type	Format	Example
IDs	Always string	"user_id": "12345" not 12345
Counts/amounts	Number	"order_total": 59.99
Flags	Boolean	"is_premium": true
Timestamps	ISO 8601 string	"2024-03-10T14:30:00Z"
Enums/status	String	"status": "In Progress"
Null handling	Pick one approach per property	Omit, null, or sentinel string like "Unknown" — never mix. Using an explicit sentinel string lets you distinguish intentionally unavailable values from instrumentation bugs. Inconsistent null handling is one of the most common causes of incorrect property filters and broken funnels.

User Identification Standards

• Anonymous users: Set device_id only. Do NOT set user_id.
• Authenticated users: Set a unique, stable user_id per verified user. Never set before login/verification.
• Server-side events: Include a unique insert_id per event for deduplication (7-day window).
• Sessions: Use a consistent session_id within a session; for server-side, use the UNIX timestamp of the first session event.

Structural Patterns

• A/B experiments: Track as list user properties, not events
• Errors: One Error Encountered event with error_type/error_category property
• E-commerce: Use product_engagement (items in this action) + cart_contents (full cart snapshot) arrays
• B2B: Instrument at least one group type (org_id, account_id)
• Property consistency for funnels: Capture the same property (e.g., product_id) across all events in a funnel

Category Assignment

Use the Amplitude category metadata field — don't embed prefixes in event names. Common categories:

Category	Purpose	Examples
Lifecycle	User journey milestones	Signup Completed, Trial Started, Subscription Cancelled
Feature	Core product functionality	Task Created, Document Edited, Report Generated
Engagement	Navigation and UI interaction	Page Viewed, Button Clicked, Search Performed
Transaction	Revenue events	Purchase Completed, Checkout Started, Refund Requested
System	Technical health	Error Occurred, API Request Completed, Timeout Occurred
Growth	Acquisition and referral	Invite Sent, Share Completed, Referral Reward Earned

Assignment heuristics:

• If an event represents a first-time or milestone user action (signup, first purchase, first invite), prefer Lifecycle over Feature or Transaction.
• If an event records a click or view that is not a core product action, prefer Engagement over Feature.
• Integration-sourced events ([Appboy], [Adjust], etc.) may not fit neatly — assign System or Growth based on the integration's purpose, or leave unassigned.
• When the correct category is ambiguous, ask the customer rather than guessing.

Scoring and Prioritizing Issues

Three dimensions:

1. Issue Impact

Level	Points	Definition	Examples
HIGH	3	Name is ambiguous — analyst cannot reliably interpret it	Jargon, acronyms, blob words, confusable names
MEDIUM	2	Name is interpretable but taxonomy is messier for it	Convention outliers, unexpected events not on plan
LOW	1	Name is clear; issue is missing polish	Missing description when name is self-explanatory

2. Event Importance

• Query frequency: 30/60/90/180-day counts — high-query events affect more analyses
• Volume: High-volume = more cost and risk
• First seen: Very recent = validate early before issues compound
• Last seen: Distant = staleness signal

3. Effort

• Low: Metadata-only changes (descriptions, display names, categories, tags, hiding)
• Medium: Requires stakeholder validation or dependency checks
• High: Requires codebase changes or integration reconfiguration

Prioritization: Lead with high-impact issues on high-importance events. Stale/test events are quick wins — surface them below real data quality problems.

Health grade: (Total Points Earned / Total Points Possible) x 100%

• 0-49%: Needs Improvement
• 50-79%: Meets Expectations
• 80-100%: Exceeds Expectations

Authority Boundaries (Metadata-Only)

Allowed (non-destructive, metadata-only, with user approval):

• Update display names, descriptions, categories, tags
• Set official status
• Hide events (NOT block or delete)
• Set up Automated Tasks
• Add AI Context to project settings

Not allowed:

• Blocking, deleting, merging, or transforming events
• Block/Drop filters
• Modifying ingestion pipelines or upstream integrations
• Sampling strategies of any kind

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Layer 3: Detection Reference

Detection Signals and Thresholds

Default lookback: 180 days.

Signal	Priority	Action
Semantically unclear name	HIGH	Add display name + description
Missing description (unclear name)	HIGH	Add description following AI readiness formula
Semantic duplicate (true — same meaning, different casing)	HIGH	Merge or disambiguate
Semantic duplicate (similar — different names, same action)	HIGH	Investigate; merge or disambiguate
Zero volume (180 days)	MEDIUM	Investigate before acting
Zero queries (180 days)	MEDIUM	Check asset dependencies first
Duplicate property across event + user scope	MEDIUM	Clarify correct source of truth
Missing description (clear name)	LOW	Add description; deprioritize
Missing category	LOW	Add category
Naming convention outlier	LOW	Flag for future realignment
Unexpected event/property	LOW	Add to plan or block after review
Stale (last seen beyond lookback)	LOW	Quick win — schedule for deprecation
Single-day (first seen = last seen)	LOW	Quick win — likely test; verify first
Test/QA artifact (test_, debug_, tmp_, _qa)	LOW	Quick win — standard deprecation process

Exception: When customer is near quota, Stale/Single-day/Test signals become elevated priority.

Key Audit Metrics

Metric	Impact
% of types at quota limit	HIGH when >90%
New types added in last 7 days (spike = possible dynamic value leak)	HIGH if spike
Total event volume change in last 7 days	HIGH if unexpected
Number of duplicate types by name	HIGH
Group types not instrumented (B2B products)	HIGH
A/B experiments tracked as events instead of user properties	MEDIUM
Events with zero queries in 180 days	MEDIUM
Events with zero volume in 180 days	MEDIUM
Single-day events	MEDIUM
% of live events with descriptions	LOW
% of live events with categories	LOW
Number of Unexpected events/properties	LOW
Naming convention inconsistencies	LOW

Good vs. Bad Metadata Examples

Display names:

Before	After
catSelectClick	Category Selected
pgVw	Page Viewed
ord_compl_v2	Order Completed
usr_prop_acct_tier	Account Tier

Descriptions:

Bad (implementation-focused)	Good (intent + context)
"Fired on click handler for nav component"	"Triggered when a customer selects a product category from the navigation menu. Example categories: Electronics, Apparel, Home."
"Event fired on submit"	"Triggered when a user completes checkout and confirms their order. Includes all line items, discounts applied, and final order total."
"See tracking plan"	"Fired the first time a new user completes onboarding by verifying their email. Fires once per user lifetime only."

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Available Tools

Event Discovery & Metadata

list_events

Lightweight listing of all events ordered by importance descending. Supports pagination with offset and limit. Best for broad taxonomy scans and governance audits.

Tool reference: langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.list_events

get_relevant_events

Semantic + text hybrid search for events matching a description. Use multiple phrasings in parallel to disambiguate.

Parameters:

• query: Natural language description
• categories: Metadata categories (e.g., ['basic', 'ai_descriptions', 'metrics'] — up to 100 events)

Tool reference: langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_relevant_events

get_event_metadata

Deep metadata for specific events: properties, usage, relationships, importance, content associations.

Parameters:

• event_names: List of events to analyze
• categories: e.g., ['basic', 'ai_descriptions', 'metrics', 'importance', 'content_usage', 'properties', 'relationships'] — up to 25 events with heavy categories

Tool reference: langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_event_metadata

Property Discovery & Metadata

list_properties / get_relevant_properties / get_property_metadata

Discover and analyze properties with the same semantic search pattern as events.

Tool references:

• langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.list_properties
• langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_relevant_properties
• langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_property_metadata

get_property_stats

Live property statistics from the metrics API. Verify volume and recent usage before recommending.

Tool reference: langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_property_stats

get_property_values

Retrieve enumerable values for a property. Validate cardinality and value consistency.

Tool reference: langley.tools.taxonomy.combined_metadata.CombinedMetadataTool.get_property_values

Context & Validation

search_knowledge_base

Search organization-specific knowledge base for business context, tracking plans, data dictionaries, and naming conventions.

Tool reference: langley.tools.knowledge.bedrock_kb_tool.BedrockKnowledgeBaseTool.search_knowledge_base

documentation_question

Answer Amplitude product questions. Use before telling a user something is not feasible.

Tool reference: Amplitude MCP tool documentation_question

get_context

Get user/org/project context. Discover all projects the user has access to.

Tool reference: Amplitude MCP tool get_context

Safe Metadata Write Operations

Tool	Purpose
set_event_description	Add/update event description
set_event_display_name	Update event display name
set_event_category	Assign event to a category
add_event_tags	Add tags to an event
set_event_deprecated	Mark event as deprecated
hide_event	Hide from taxonomy (does not delete)
set_property_description	Add/update property description
set_property_display_name	Update property display name

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Workflows

For step-by-step execution procedures (data quality audits, governance analyses, event verification, deprecation workflows, tracking plan generation, tool usage strategy), see the governance skill.