feedback-digest

Analyze manuscript review feedback to recommend writing framework improvements.

Updated Aug 27, 2026

Search Tags:#writing workflow #quality improvement #manuscript review #feedback analysis #review logs #detector tuning

Ready to run

npx skills add https://github.com/Werewolf-DynamiX/_New_Book_Starter_Kit --skill feedback-digest

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: feedback-digest
Source: https://github.com/Werewolf-DynamiX/_New_Book_Starter_Kit/tree/main/.claude/skills/feedback-digest
Command: npx skills add https://github.com/Werewolf-DynamiX/_New_Book_Starter_Kit --skill feedback-digest

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill transforms scattered chapter review feedback into actionable insights, helping writers identify recurring issues, improve detectors, and evolve their writing framework.

Core Features & Use Cases

Feedback Pattern Analysis: Aggregates review dispositions to identify noisy checks, recurring craft weaknesses, escalation needs, and workflow lessons.
Framework Improvement Recommendations: Suggests detector tuning, WRITER_VOICE updates, reviewer complaint entries, and process documentation changes based on evidence.
Use Case: Analyze completed manuscript review logs to discover which prose checks are frequently dismissed, consistently fixed, or require broader workflow changes.

Quick Start

Use the feedback-digest skill to analyze all processed review files and recommend improvements to the writing framework.

Frequently Asked Questions about feedback-digest

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

FAQPage Schema

How do I analyze manuscript review feedback to identify recurring writing issues?▼

You analyze manuscript review feedback by parsing structured review dispositions to identify patterns in recurring craft weaknesses, noisy checks, and escalation needs. Aggregating these logs reveals frequently dismissed prose checks and highlights necessary workflow lessons.

What is the best way to turn chapter review logs into framework improvements?▼

The best way to turn chapter review logs into framework improvements is to aggregate review dispositions to generate actionable recommendations. This process suggests specific detector tuning, WRITER_VOICE updates, reviewer complaint entries, and process documentation changes based on evidence.

How do I tune writing detectors based on processed review dispositions?▼

To tune writing detectors based on processed review dispositions, you analyze feedback patterns to identify noisy checks and consistently fixed issues. This evidence-based approach recommends specific detector tuning and prose rule adjustments to improve iterative quality systems.

Can I use feedback analysis to update my writing workflow and escalation records?▼

Yes, you can use feedback analysis to update writing workflows by identifying escalation needs and generating process lessons. It evaluates chapter reviews to recommend broader workflow changes, reviewer complaint entries, and process documentation updates.

Does analyzing feedback patterns require structured review logs?▼

Analyzing feedback patterns requires structured review logs because the process parses structured review dispositions. Without structured dispositions from chapter reviews and detector evaluations, the system cannot accurately generate recommendations for detector tuning or prose rules.