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types

Document Splitrail core data types for conversations, stats, and aggregation results.

216|23|Updated Jul 12, 2025
Search Tags:#aggregation#conversation#types#stats#datamodel#splitrail#daily-stats
One-click install
npx skills add https://github.com/Piebald-AI/splitrail --skill types-piebald-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: types
Source: https://github.com/Piebald-AI/splitrail/tree/main/.claude/skills/types
Command: npx skills add https://github.com/Piebald-AI/splitrail --skill types-piebald-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reference for Splitrail's core data types used to model conversations, stats, and aggregation results, helping developers implement, extend, or reason about analytics and usage data.

Core Features & Use Cases

  • ConversationMessage - Normalized message format across analyzers with source, timestamp, hashes, model info, token/cost stats, and role.
  • Stats - Usage metrics for a single message including token counts, costs, file operations, and composition stats by file type.
  • DailyStats - Pre-aggregated stats per date with message counts, conversation counts, and embedded Stats.
  • Types & Aggregation - Documentation and examples for how data types feed into Splitrail analytics pipelines.
  • Usage Scenarios: Integrate these types into dashboards, data pipelines, and validation logic.

Quick Start

Review src/types.rs to understand the type definitions and how to integrate Splitrail’s data models in your analytics pipeline.

Frequently Asked Questions about types

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What data types are used to model conversation messages and usage stats in Splitrail?▼

ConversationMessage is the normalized message format across analyzers, capturing source, timestamp, hashes, model info, token/cost stats, and role. It standardizes message data from multiple analyzers into a single consistent structure.

How do I work with pre-aggregated daily usage stats for analytics pipelines?▼

DailyStats provides pre-aggregated usage metrics per date, embedding message counts, conversation counts, and a Stats object. You integrate this structure directly into dashboards, data pipelines, and validation logic to analyze usage trends.

How do I extend or compose Splitrail data types for custom analytics tooling?▼

You review src/types.rs to understand the type definitions and compose them within your tooling. The types document their fields and show how to feed data structures into Splitrail analytics pipelines for extended integration.

What usage metrics are included in a single message's Stats data structure?▼

The Stats data structure includes usage metrics for a single message such as token counts, costs, file operations, and composition stats broken down by file type. It provides granular tracking for individual message processing.

Can I use these data type definitions to build validation logic for usage data?▼

Yes, the data types are designed for integration into validation logic. By reviewing the type definitions in src/types.rs, you can ensure your usage data conforms to the expected structures for ConversationMessage, Stats, and DailyStats.

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