Validate data and provide two-tier feedback on written work.
npx skills add https://github.com/Black-JL/writing-mentor-framework --skill writing-mentor
Please help me install this Agent Skill. Skill: writing-mentor Source: https://github.com/Black-JL/writing-mentor-framework/tree/main/skills/feedback Command: npx skills add https://github.com/Black-JL/writing-mentor-framework --skill writing-mentor
This Skill provides structured, rigorous feedback on written work, going beyond surface-level review to validate data, check consistency, and deliver pedagogical guidance.
Use the writing-mentor skill to review the attached document and spreadsheet.
High-intent search queries and answers about installing and using this skill.
Structured academic writing feedback validates data pipelines, audits statistical assumptions, and checks internal consistency to deliver pedagogical guidance. This Skill thoroughly reviews documents and spreadsheets to identify inconsistencies and provide actionable teaching notes for writers and technical notes for reviewers.
Yes, writing feedback can check calculations against claims and external data sources through rigorous data validation. The Skill verifies charts against formulas and validates data sources against external APIs to ensure intellectual rigor and internal consistency across your analytical review.
The best way to audit economic and statistical assumptions is through systematic evaluation checklists that verify them against course principles. This Skill performs assumption auditing to check whether your analytical review aligns with general practice and pedagogical requirements.
Yes, this feedback tool provides separate guidance for students and instructors through two-tier feedback. It delivers detailed technical notes for reviewers and teaching-focused pedagogical guidance for writers, ensuring both audiences receive relevant analytical review insights.
To review an analytical paper with spreadsheets and charts, use this Skill to thoroughly audit the data pipeline. It provides structured feedback by validating data, checking consistency between charts and formulas, and identifying inconsistencies across the submitted documents.
Consistency checking for pedagogical writing feedback relies on the provided documents and data sources. While it validates data against external APIs and course principles, complex analytical review assumptions may require manual verification if the underlying data pipeline is incomplete or inaccessible.
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