predict

Generate prediction exams for implementation milestones and grade answers against PR diffs.

Updated Aug 23, 2026

Search Tags:#github #prediction #grading #exam-generation #pr-diff #developer-feedback #prediction-log

Ready to run

npx skills add https://github.com/vitalwarley/hyprdots --skill predict-vitalwarley

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: predict
Source: https://github.com/vitalwarley/hyprdots/tree/main/claude-global/skills/predict
Command: npx skills add https://github.com/vitalwarley/hyprdots --skill predict-vitalwarley

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill generates prediction exams for implementation milestones so teams can capture developer expectations before code is merged and later measure those predictions against actual PR diffs to identify learning gaps and improve forecasting.

Core Features & Use Cases

Exam generation: Create 3-5 targeted questions (multiple-choice, numeric, open-ended) scoped to a milestone, issue, or stream with context scaffolding and plausible distractors.
Automated grading: Retrieve PR diffs or merged commit ranges, compare recorded answers to the implementation, mark correctness, and write concise explanations with gap categorization.
Trend analysis & feed-forward: Append exam records to a persistent prediction log, compute accuracy trends by category and question type, and flag persistent gaps for targeted concept briefs.
Use case: Generate an exam at the start of an autonomous implementation, collect answers while the system implements, and grade the exam automatically when the PR arrives.

Quick Start

Ask the assistant to generate a prediction exam for milestone A1 and record the participant answers for later grading.

Frequently Asked Questions about predict

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

FAQPage Schema

How do I test developer predictions against actual PR diffs?▼

Generate prediction exams for implementation milestones, collect participant answers during development, and automatically grade them by retrieving PR diffs to compare answers against the actual merged implementation. This identifies learning gaps and improves forecasting accuracy for the team.

What is a prediction exam for autonomous implementation milestones?▼

A prediction exam for autonomous implementation milestones is a set of 3-5 targeted questions including multiple-choice, numeric, and open-ended formats scoped to a milestone or issue. It captures developer expectations before code is merged to measure forecasting accuracy against actual PR diffs later.

Do I need GitHub CLI access to generate and grade prediction exams?▼

Yes, you need GitHub CLI access. Generating and grading prediction exams requires access to project plans and issue or PR data via gh and git commands, plus a writable prediction log file to record entries and later grade them against the actual implementation diffs.

How do I analyze developer prediction accuracy trends over time?▼

You analyze developer prediction accuracy trends by appending exam records to a persistent prediction log, computing accuracy trends by category and question type, and flagging persistent gaps for targeted concept briefs. This feed-forward mechanism continuously improves future milestone forecasting.

Can I scope prediction exam questions to specific GitHub issue numbers?▼

Yes, you can scope prediction exam questions to specific GitHub issue numbers. Apply the exam generation to repository workflows using issue numbers, milestone streams, or plan documents to target the implementation context before or during autonomous development.

What are the limitations of automated prediction grading for PR diffs?▼

Automated prediction grading for PR diffs requires a writable prediction log file to record and later grade entries, and depends on retrieving merged commit ranges or PR diffs via gh and git. It is limited by the availability of project plans and accurate issue or PR data access.