Where does the skill find my session data?

The skill automatically scans the .claude-trace directory in your project for JSONL files generated by Claude Code during your sessions.

How does this differ from the built-in /transcripts command?

The /transcripts command focuses on conversation history and content restoration. Session Replay focuses on API-level telemetry, including token counts, latencies, and specific tool execution metrics found in JSONL trace files.

Can I compare performance across different days?

Yes, the skill includes a 'compare' action that aggregates data from multiple trace files to show trends in token efficiency, duration, and error rates over time.

Does this skill help reduce my API costs?

By identifying high-consumption patterns and redundant tool calls, the skill provides actionable insights to help you optimize how Claude uses context and tools, ultimately lowering token usage.

Session Replay & Trace Analytics

Name: Session Replay & Trace Analytics
Author: rysweet

byrysweet

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Analíticas y Monitorización

Analyzes Claude Code session traces to optimize token consumption and diagnose technical performance issues.

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The Session Replay skill provides deep technical visibility into your Claude Code sessions by parsing API-level JSONL trace files. It empowers developers to audit token usage, calculate efficiency, identify recurring tool failures, and benchmark performance across multiple interactions. While standard transcripts show what was said, this skill reveals how Claude is performing under the hood, making it an essential tool for debugging agentic workflows and managing API costs.

Características Principales

16 GitHub stars
Tool effectiveness audits to identify latency bottlenecks
Comprehensive session health and efficiency reporting
Multi-session performance comparisons and trend analysis
Automated error pattern identification and categorization
Granular token usage and consumption tracking

Casos de Uso

Identifying recurring API errors or rate-limiting patterns across different projects
Debugging why an agentic session was slow or failed to complete a task
Optimizing prompt and tool strategies to reduce token costs

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Características Principales

16 GitHub stars
Tool effectiveness audits to identify latency bottlenecks
Comprehensive session health and efficiency reporting
Multi-session performance comparisons and trend analysis
Automated error pattern identification and categorization
Granular token usage and consumption tracking

Casos de Uso

Identifying recurring API errors or rate-limiting patterns across different projects
Debugging why an agentic session was slow or failed to complete a task
Optimizing prompt and tool strategies to reduce token costs