plannotator-compound

Analyze Plannotator plan archives and generate an HTML dashboard of denial patterns.

Updated Mar 24, 2016
One-click install
npx skills add https://github.com/jonmast/dotfiles --skill plannotator-compound-jonmast
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: plannotator-compound
Source: https://github.com/jonmast/dotfiles/tree/main/agents/skills/plannotator-compound
Command: npx skills add https://github.com/jonmast/dotfiles --skill plannotator-compound-jonmast

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, pandas, numpy, matplotlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill analyzes a user's Plannotator plan archive, extracts denial patterns, and generates an HTML dashboard report to help improve planning and outcomes.

Core Features & Use Cases

  • Plan Analysis: Extract denial patterns, feedback taxonomy, and evolution over time from Plannotator plans.
  • Dashboard Generation: Produce a polished HTML dashboard report with insights.
  • Fallback Mode: Use Claude Code ExitPlanMode denial reasons when Plannotator data is unavailable.
  • Use Case: If you have a collection of Plannotator plan files and want to understand the common reasons for denials, this Skill can analyze them and generate a report to guide future planning.

Quick Start

Analyze the Plannotator plans from 'user-plans' directory and generate a report.

Frequently Asked Questions about plannotator-compound

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

FAQPage Schema
How do I analyze Plannotator plan archives to extract denial patterns?

You can analyze Plannotator plan archives by pointing this Skill at your plan directory. It parses markdown files to extract denial patterns and generates an HTML dashboard report visualizing feedback taxonomy and planning evolution.

What is denial pattern extraction from markdown planning files?

Denial pattern extraction is the process of parsing markdown plan files to identify recurring rejection reasons. This Skill applies this technique to Plannotator archives to reveal feedback taxonomy and improve future planning outcomes.

Do I need Python and pandas to generate the HTML dashboard report?

Yes, generating the HTML dashboard report requires Python, pandas, numpy, and matplotlib. These dependencies handle parsing markdown plan data, analyzing denial patterns, and rendering the visual dashboard output.

Can I analyze Claude Code ExitPlanMode denial reasons if Plannotator data is unavailable?

Yes, you can analyze Claude Code ExitPlanMode denial reasons if Plannotator data is unavailable. The Skill includes a fallback mode that parses exit plan feedback to ensure analysis proceeds without primary Plannotator archives.

What is the best way to visualize planning feedback taxonomy from markdown files?

The best way to visualize planning feedback taxonomy from markdown files is generating a polished HTML dashboard. This Skill leverages matplotlib and pandas to transform parsed denial patterns into an accessible visual report.