AI Coding Data Reference

Complete reference of all data points collected from AI coding assistant integrations, including Claude Code, Gemini CLI, and Cursor IDE.

This page documents every data point that Revenium collects from AI coding assistant integrations. Use this reference to understand exactly what telemetry is captured, how it's used, and what privacy guarantees apply.

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How Data Is Collected

All AI coding assistant data is collected via OpenTelemetry (OTLP) log records. Each coding tool has a dedicated integration that exports usage telemetry to Revenium's OTLP endpoint. No proprietary agents or background processes are involved — data flows through the standard OpenTelemetry protocol.

Tool	Integration Method	Data Flow
Claude Code	@revenium/cli npm package	Claude Code hooks → OTLP logs → Revenium
Gemini CLI SDK	@revenium/cli npm package	Gemini CLI → OTLP logs → Revenium
Cursor IDE	Admin API sync	Cursor Admin API → Revenium (periodic)

Agent Identifiers

Each tool is identified by an agent value in the telemetry:

Tool	Agent Identifier
Claude Code	claude-code
Gemini CLI	gemini-cli
Cursor IDE	cursor-ide

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Data Privacy

Revenium never collects your code, prompts, or conversation content. Only usage metadata is transmitted — token counts, model names, timestamps, and session identifiers. This applies to all integrations by default.

Specifically, the following are never sent in the default configuration:

• Source code or file contents

• Prompt text or system prompts

• AI response content

• API keys, credentials, or secrets

• Repository names or git history (diffs, commits, file contents)

• Screen content or clipboard data

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GitHub Integration Data (Optional)

The following section applies only when the optional GitHub integration is connected. Without it, the AI Coding Dashboard operates entirely from OTLP telemetry data and does not interact with GitHub in any way.

What We Read from GitHub

When the integration is active, Revenium makes the following read-only API calls to GitHub:

Data Read	Purpose
Organization member list (usernames, public emails)	Auto-map developers to their corporate email
Public user profiles and email search	Resolve GitHub logins to email addresses for attribution
Repository names in the organization	Determine which repos to scan for merged PRs
Merged PR metadata (author, merge date)	Count PRs merged per developer in the selected period
Commit messages and commit author emails	Detect AI co-author patterns (e.g. Co-Authored-By trailers)

Revenium reads commit messages to detect AI co-authorship patterns, but does not read or store the message content itself — only the boolean result (AI-assisted or not) is retained.

What We Store

Stored Data	Description
Daily PR counts per developer	Number of PRs merged and number of AI-assisted PRs, per day
GitHub-to-email mappings	Links each developer's GitHub username to their corporate email for attribution

What We Do NOT Access

Even though the GitHub token may have broad permissions (repo scope), our implementation only makes the specific API calls listed above. The following are never accessed:

• File contents, diffs, or patches

• Pull request descriptions or comments

• Repository source code

• Issues, reviews, or branch data

• GitHub Actions, webhooks, or deployment data

• Private user profile data beyond public email

Token Permissions

The GitHub integration requires a personal access token with repo and read:org scopes. The repo scope is broader than strictly necessary, but GitHub does not offer a narrower scope that grants access to merged PR metadata across private repositories. Our code only exercises the minimum API calls needed for PR attribution.

For details on setting up and configuring the GitHub integration, see GitHub Integration.

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Common Data Points

The following data points are collected by all AI coding assistant integrations. These form the core telemetry schema that powers the AI Coding Dashboard.

Token Metrics

Data Point	Type	Description
inputTokenCount	Integer	Number of input tokens consumed in the request
outputTokenCount	Integer	Number of output tokens generated by the model
cacheReadTokenCount	Integer	Tokens served from the model's prompt cache (reduces cost)
cacheCreationTokenCount	Integer	Tokens written to the model's prompt cache
reasoningTokenCount	Integer	Extended thinking / chain-of-thought tokens (model-dependent)
totalTokenCount	Integer	Sum of all token types for the request

Cost Metrics

Data Point	Type	Description
totalCost	Decimal	Calculated cost in USD for this request, based on model pricing
cost_source	String	Always coding_assistant for AI coding tool traffic
costType	String	Always AI for AI coding assistant requests

Model & Provider Identity

Data Point	Type	Description
model	String	AI model name (e.g., claude-opus-4-5-20251101, gemini-2.5-pro)
provider	String	AI provider identifier. Set by backend mappers: ClaudeCode, GeminiCli, CursorIde.
agent	String	Coding assistant identifier (claude-code, gemini-cli, cursor-ide)

Timing

Data Point	Type	Description
requestTime	Timestamp	When the request was initiated (ISO 8601 / epoch nanoseconds)
requestDuration	Integer	Total request duration in milliseconds

Attribution

Data Point	OTLP Attribute	Type	Description
subscriber	user.email	String	Developer email address for usage attribution (optional, user-configured)
organizationName	organization.id or organization.name	String	Organization or company name/ID for cost rollup (optional). The backend prefers organization.name; falls back to organization.id.
productName	product.id or product.name	String	Product or project name/ID for cost rollup (optional). The backend prefers product.name; falls back to product.id.
traceId	session.id	String	Session identifier — groups requests within a single coding session
transactionId	transaction_id	String	Unique identifier for each individual request (used for deduplication)

The Data Point column shows the name as stored in the analytics database. The OTLP Attribute column shows the key name in the raw telemetry payload. The backend mapper translates between these formats during ingestion.

Operational Classification

Data Point	Type	Description
operationType	String	Request classification (e.g., CHAT)
stopReason	String	Why the model stopped generating. Revenium enum values: END, TOKEN_LIMIT, ERROR, CANCELLED. See Gemini Stop Reason Mapping for tool-specific normalization.
errorReason	String	Error description if the request failed (empty on success)

Coding Assistant Account Linkage

Data Point	Type	Description
coding_assistant_account_uuid	String	Links telemetry to a specific coding assistant account for cross-session tracking
subscription_tier	String	Subscription plan identifier (see Subscription Tiers below)

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Claude Code Data Points

In addition to the Common Data Points above, Claude Code captures the following:

Subscription Tiers

Claude Code subscriptions tiers are optionally tracked when using the Revenium SDKs:

Tier	Description
pro	Anthropic Pro plan
max_5x	Anthropic Max 5x plan
max_20x	Anthropic Max 20x plan
team_premium	Anthropic Team Premium plan
enterprise	Anthropic Enterprise plan
api	Direct API usage (full API pricing, no subscription discount)

Data Collection Modes

Claude Code supports two data collection modes:

Mode	Description
Real-time	Telemetry is exported automatically during each Claude Code session via OTLP hooks. Captures core token, cost, and timing metrics.
Backfill	The revenium-metering backfill command scans local Claude Code session logs (~/.claude/projects/) and sends historical usage data.

Backfill is idempotent — deterministic transaction IDs (SHA-256 hash of session ID, timestamp, model, and token counts) prevent duplicate records.

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Gemini Data Points

Gemini CLI data flows into Revenium via the @revenium/cli npm package, which configures Gemini CLI's native OTLP export to send telemetry to Revenium's endpoint.

Gemini CLI SDK Data Points

The CLI SDK captures the Common Data Points listed above — token metrics, cost, model identity, timing, and attribution.

Gemini CLI operates in real-time only — there is no backfill capability. Telemetry is captured and exported as each Gemini CLI request completes.

Stop Reason Mapping

Gemini CLI normalizes Google's finish reasons to Revenium's supported StopReason value:

Gemini Finish Reason	Revenium StopReason	Description
STOP	END	Normal completion
MAX_TOKENS	TOKEN_LIMIT	Token limit reached
SAFETY, BLOCKLIST, PROHIBITED_CONTENT, SPII, MODEL_ARMOR	ERROR	Content safety filter triggered
RECITATION, IMAGE_SAFETY, IMAGE_PROHIBITED_CONTENT, IMAGE_RECITATION	ERROR	Recitation or image safety filter
MALFORMED_FUNCTION_CALL, UNEXPECTED_TOOL_CALL, NO_IMAGE	ERROR	Tool call or image error
CANCELLED / CANCELED	CANCELLED	Request canceled
FINISH_REASON_UNSPECIFIED, OTHER, IMAGE_OTHER	(caller-supplied default)	Returns the default stop reason provided by the calling context

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Cursor IDE Data Points

In addition to the Common Data Points above, Cursor IDE captures the following through its Admin API sync:

Billing Classification

Data Point	Type	Description
billing.kind	String	Cursor billing classification (Included, Premium, etc.) — determines whether usage counts against quota
operation_type	String	Operation type from Cursor (e.g., request classification)
stop_reason / finish_reason	String	Finish reason from Cursor

When billing.kind is Included, Revenium sets billingSkipped = true, skipReason = FREE_TIER, and forces totalCost to null — indicating the request was covered by the subscription and incurred no additional cost.

Data Collection Mode

Cursor IDE usage data is collected periodically from Cursor's Admin API and exported to Revenium via OTLP. Unlike Claude Code and Gemini CLI, data is not captured in real-time during each request — it is synced at regular intervals from Cursor's administrative interface.

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Derived Fields

The following fields are not sent by the SDKs but are calculated by the Revenium backend during ingestion:

Field	Derivation	Description
inputTokenCost	inputTokenCount × model_input_cost_per_token	Cost attributed to input tokens
outputTokenCost	outputTokenCount × model_output_cost_per_token	Cost attributed to output tokens
cacheCreationTokenCost	cacheCreationTokenCount × model_cache_creation_cost	Cost attributed to cache creation
cacheReadTokenCost	cacheReadTokenCount × model_cache_read_cost	Cost attributed to cache reads
totalCost (when not provided)	Sum of all token costs	Calculated when SDK sends zero or null cost
apiKey	Extracted from x-api-key HTTP header	Authentication key for tenant identification
credentialId	Extracted from subscriber JSON	Credential identifier for access control

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OTLP Transport Details

For teams implementing custom integrations or verifying data flow, here are the OTLP transport details:

Endpoint

POST {base_url}/v1/logs

Where base_url is typically https://api.revenium.ai/v2/otlp.

Authentication

x-api-key: rev_mk_your_key_here

This is a metering key (rev_mk_*) — sufficient for OTLP telemetry ingest, which is what every AI coding-assistant integration on this page does. For workflows that also report business outcomes or manage Revenium resources, use a write-scope key (rev_sk_*) — see API Key Permissions.

Payload Format

All integrations use the OTLP/HTTP JSON format (application/json):

{
  "resourceLogs": [{
    "resource": {
      "attributes": [
        { "key": "service.name", "value": { "stringValue": "claude-code" } }
      ]
    },
    "scopeLogs": [{
      "scope": { "name": "claude-code", "version": "1.0.0" },
      "logRecords": [{
        "timeUnixNano": "1711324800000000000",
        "body": { "stringValue": "claude_code.api_request" },
        "attributes": [
          { "key": "session.id", "value": { "stringValue": "sess-abc123" } },
          { "key": "model", "value": { "stringValue": "claude-opus-4-5-20251101" } },
          { "key": "input_tokens", "value": { "intValue": 1500 } },
          { "key": "output_tokens", "value": { "intValue": 2000 } },
          { "key": "cache_read_tokens", "value": { "intValue": 500 } },
          { "key": "cache_creation_tokens", "value": { "intValue": 0 } },
          { "key": "total_input_tokens", "value": { "intValue": 2000 } }
        ]
      }]
    }]
  }]
}

The example above shows a Claude Code backfill payload with the core token attributes. The real-time test/connectivity payload (via revenium-metering test in each relevant SDK if used) uses stringValue for token fields and additionally sends cost_usd and duration_ms. Gemini CLI payloads follow the same OTLP structure with service.name set to gemini-cli and scope name set to gemini_cli.

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Related Documentation

• AI Coding Dashboard — Dashboard views and analysis features

• Integration Options for AI Metering — Setup instructions for all integrations

• OpenTelemetry Integration — General OTLP integration guide

• Set Budgets & Alerts — Alerting on coding assistant metrics