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semantic-view-time-tracking

Track and report execution time for workflow steps and agent tasks.

Updated Jun 9, 2025
Search Tags:#workflow#python#benchmarking#reporting#performance analysis#time tracking#agent tasks
One-click install
npx skills add https://github.com/RajaPoseidon/snippets_repo --skill semantic-view-time-tracking-rajaposeidon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: semantic-view-time-tracking
Source: https://github.com/RajaPoseidon/snippets_repo/tree/main/snowflake/skills/time_tracking
Command: npx skills add https://github.com/RajaPoseidon/snippets_repo --skill semantic-view-time-tracking-rajaposeidon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve? Measuring how long workflow steps and agent tasks take is difficult when work spans multiple tools, directories, and processes. This Skill provides centralized timing with persistent state files so you can identify bottlenecks in setup, audit, debug, and validation workflows. ## Core Features & Use Cases - Agent Task Tracking: Capture complete wall-clock time for agent tasks including reasoning, tool execution, and response generation via the track_agent_task.py wrapper. - Hierarchical Step Timing: Track parent tasks and sub-tasks (e.g., setup containing download and extraction steps) with structured breakdowns. - Report Generation: Print console summaries and export timing data to CSV or JSON for performance analysis and historical comparison. - Use Case: During a semantic view optimization session, track each VQR evaluation and debug step, then export a timing report to identify which workflow phase consumes the most time. ## Quick Start Track my next analysis task in session my_session and show me a timing summary when it finishes.

Frequently Asked Questions about semantic-view-time-tracking

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

FAQPage Schema
How do I track execution time of agent tasks in Python?▼

Use the track_agent_task.py script with a session ID: call start before the task begins and end after it completes. It captures total wall-clock time including reasoning, tool execution, and response generation, then reports via the TimeTracker summary.

How to export workflow timing data to CSV or JSON?▼

Call the TimeTracker export_csv or export_json methods with your session ID and output filename. The exports include step names, start and end timestamps, durations, and parent-step hierarchy for further analysis.

Why are some tracked steps missing from the timing summary?▼

Missing steps usually mean a start call had no matching end call, often due to an exception skipping the end. Verify state files with ls /tmp/.time_tracker_* and ensure every started task is properly ended.

Where does the time tracker store its state files?▼

State files are stored in /tmp as .time_tracker_SESSION_ID.json by default. This ensures persistence across working directories and lets multiple processes share the same session without creating duplicate state files.

What are the limitations of wall-clock task tracking?▼

Wall-clock timing cannot break down thinking time into sub-steps or separate pure reasoning from response generation. It also includes network latency and API delays, so treat it as total task duration rather than precise compute time.

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