aps-explanation-editorial

Name: aps-explanation-editorial
Availability: InStock
Author: 100Thieves-team

Designs hint ladders, editorials, and reveal policies for algorithm learning platforms.

Updated Jul 4, 2026

Search Tags:#product requirements #learning science #editorial design #algorithm learning #hint ladder #spoiler control #reveal policy

Ready to run

npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-explanation-editorial-100thieves-team

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aps-explanation-editorial
Source: https://github.com/100Thieves-team/plady-expert-skills/tree/main/algo-experts/skills/aps-explanation-editorial
Command: npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-explanation-editorial-100thieves-team

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Product teams building algorithm-problem-solving platforms (Baekjoon/LeetCode-style judges) struggle to design hints, editorials, and explanations that aid learning without spoiling the discovery process or corrupting learning signals. ## Core Features & Use Cases - Editorial Design Specs: Produces structured requirements covering explanation sections, reveal policies per user state (before attempt, during, after AC), and learning safeguards. - Hint Ladder & Spoiler Control: Defines a 7-level hint ladder (meta nudge to full solution) with fading principles grounded in learning science (worked-example effect, ZPD, scaffolding). - Explanation Quality Review: Audits existing editorials against quality criteria like reasoning paths, correctness arguments, complexity analysis, and common pitfalls. - Use Case: When deciding whether reading an editorial should count as solving, or how to gate AI-generated explanations in assessment mode, this Skill produces the domain distinctions, metrics, and risk analysis. ## Quick Start Ask the Skill to structure a good editorial and define the reveal policy for hints versus full solutions in learning mode.

Frequently Asked Questions about aps-explanation-editorial

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

FAQPage Schema

How do I structure a good algorithm editorial?▼

A good editorial restates the problem formally, connects constraints to solution requirements, shows the brute-force baseline and why it fails, walks the insight path to the better approach, and includes correctness reasoning, complexity analysis, implementation notes, and common pitfalls.

What is the difference between a hint and a full editorial?▼

Hints sit on a 7-level ladder from meta nudges to key insights and are given during solving to preserve discovery, while editorials are complete explanations usually unlocked post-solve or opt-in. They carry different spoiler risks and learning impacts, so products should separate them.

When should editorials be unlocked in a learning platform?▼

Reveal policy should be a function of mode, solver level, and struggle time. Learning mode favors facilitative, delayed hints with struggle gates before higher levels unlock, while assessment mode strictly withholds explanations to certify results.

Does reading an editorial count as solving the problem?▼

Viewing an editorial before any attempt signals high assistance and potential passive learning, while viewing after independent AC supports review. The strongest signal is when the user reconstructs or explains the solution in their own words after reading.

How should AI-generated explanations be handled in-product?▼

AI explanations must be gated on having concrete diagnostic evidence rather than generic feedback, since confident but wrong or generic explanations damage trust and learning. This Skill coordinates with an AI Assistance & Integrity Expert for guardrails.

What are the limitations of this explanation design skill?▼

It does not solve algorithm problems, write competitive-programming code, or debug user submissions. It produces product requirements, domain distinctions, and metrics for planners and engineers rather than implementation details like schemas or APIs.