Where is the final executive summary saved?

The summary is automatically saved as 'executive-summary.md' within the specific date-organized folder that was analyzed.

Can I customize the focus of the generated summary?

Yes, when invoking the skill, you can specify optional focus areas such as security, deployment status, or specific project components to guide the AI analysis.

How does it handle team member identification?

It uses the TEAM.md file in your static context folder to map technical usernames or GitHub handles to real names and roles for clearer reporting.

Does this skill require a specific folder structure?

Yes, it is designed to work with the SLIM Project-Aware Workspace, which organizes project metadata in a 'static/' folder and daily activity in a 'dynamic/' folder.

What files does this skill analyze?

The skill analyzes communication files stored in your workspace's date folders, including Markdown meeting notes, Slack JSON exports, GitHub data, and CSV activity logs.

Executive Summary Generator

Name: Executive Summary Generator
Author: NASA-AMMOS

byNASA-AMMOS

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Collaboration Tools

Generates comprehensive executive summaries from communication and project files using AI-driven context discovery.

About

The Executive Summary Generator automates the creation of high-level project reports by analyzing date-organized communication files—such as meeting notes, Slack exports, and GitHub activity—within a SLIM-compliant workspace. By integrating static project context like team roles and technical stacks, it transforms raw data into structured executive overviews, identifying key themes, critical issues, and prioritized action items while maintaining a consistent professional format using standardized templates.

Key Features

Context-aware name mapping and technical term recognition using team and project metadata
31 GitHub stars
Standardized template-based output for consistent professional reporting
Automatic workspace discovery and context loading from static project files
Intelligent pattern recognition for identifying cross-cutting themes and risks
Analysis of diverse communication formats including Markdown, JSON, TXT, and CSV

Use Cases

Summarizing cross-channel team communication into actionable items and milestones
Generating daily or weekly project status reports for stakeholders
Synthesizing complex technical discussions into scannable executive-level insights

About

Key Features

Context-aware name mapping and technical term recognition using team and project metadata
31 GitHub stars
Standardized template-based output for consistent professional reporting
Automatic workspace discovery and context loading from static project files
Intelligent pattern recognition for identifying cross-cutting themes and risks
Analysis of diverse communication formats including Markdown, JSON, TXT, and CSV

Use Cases

Summarizing cross-channel team communication into actionable items and milestones
Generating daily or weekly project status reports for stakeholders
Synthesizing complex technical discussions into scannable executive-level insights