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Post-Session Productivity Reports

At the end of a focus block, Coral AI can analyze what was accomplished using its vision and monitoring tools.

Core Architecture

How It Works Under The Hood

The Post-Session Productivity Reports module is built on a highly optimized C++ and Python bridge. By bypassing standard Windows UI restrictions, Coral AI directly interfaces with system memory, native Win32 APIs, and DOM structures to achieve near-zero latency execution.

Active Window Audits

Checks how long VS Code or Word was the active window vs the browser.

Screen Analysis

Briefly reads the screen to summarize code written or paragraphs completed.

Summary Generation

Generates a neat Markdown report of your 45-minute output.

Rest Reminders

Automatically suggests a 5-minute break before you start the next sprint.

Diagnostics

Execution Trace

~ > coral execute --module post-session-productivity-reports --verbose
0.00ms [INFO] Initializing C++ memory hooks... OK
2.14ms [INFO] Bypassing UI thread restrictions... OK
5.89ms [INFO] Allocating vector buffer for LLM context...
8.22ms [WARN] Elevating privileges to Admin ring...
14.01ms >>> Execution payload delivered successfully.

Technical Specs

  • Latency< 15ms
  • RuntimeC++ / Py 3.11
  • PrivilegeRing 3 / Admin
  • Offline ModeRequires Internet

Agentic Integration

This module does not operate in isolation. It is dynamically invoked by the Coral PlannerAgent via JSON-RPC, allowing it to be chained endlessly with vision and memory modules.