plannotator-compound

Name: plannotator-compound
Availability: InStock
Author: irootpro

Analyze Plannotator plan archives to generate an HTML report of denial patterns and feedback taxonomy.

Updated Jan 13, 2024

Search Tags:#planning #feedback #analysis #reporting #improvement

Ready to run

npx skills add https://github.com/iRootPro/dotfiles --skill plannotator-compound-irootpro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: plannotator-compound
Source: https://github.com/iRootPro/dotfiles/tree/main/.config/agents/skills/plannotator-compound
Command: npx skills add https://github.com/iRootPro/dotfiles --skill plannotator-compound-irootpro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, BeautifulSoup, requests, 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 to extract denial patterns, feedback taxonomy, evolution over time, and actionable prompt improvements, ultimately producing a polished HTML dashboard report.

Core Features & Use Cases

Denial Pattern Analysis: Extracts and categorizes denial patterns from user plans.
Feedback Taxonomy: Identifies and categorizes feedback types provided in annotations.
Evolution Over Time: Tracks changes in feedback patterns over time.
Actionable Instructions: Generates specific instructions for planning prompts to avoid common denial reasons.
Use Case: A user with a complex planning process can use this Skill to understand common pitfalls in their planning and receive tailored instructions to improve future plans.

Quick Start

Use the plannotator-compound skill to analyze your planning data by specifying the path to your plans directory.

Frequently Asked Questions about plannotator-compound

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

FAQPage Schema

How do I analyze planning archives to extract feedback taxonomy and denial patterns?▼

To analyze planning archives for denial patterns and feedback taxonomy, you process structured plan files to categorize feedback types and track evolution over time. This skill uses Python text processing to generate an HTML dashboard detailing actionable improvements.

What is the best way to generate actionable improvement reports from plan annotations?▼

Generating actionable improvement reports from plan annotations involves identifying common denial reasons in your planning data. This skill analyzes archives to produce specific instructions for planning prompts, outputting a polished HTML dashboard report.

Do I need Python libraries installed to process plan files and generate HTML reports?▼

Yes, you need Python libraries installed to process plan files and generate HTML reports. This skill specifically depends on pandas, numpy, BeautifulSoup, and requests to perform text processing and create the final dashboard output.

Can I track how feedback patterns evolve over time in my planning data?▼

You can track how feedback patterns evolve over time in your planning data by analyzing your Plannotator plan archives. The skill evaluates historical annotations to map feedback taxonomy changes and generates an HTML report visualizing this evolution.

How do I avoid common denial reasons when creating new planning prompts?▼

To avoid common denial reasons when creating planning prompts, you need actionable instructions derived from past planning failures. This skill analyzes your archives for denial patterns and generates tailored prompt improvements to guide future plans.