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aps-learning-retention

Defines learning-state models, mastery criteria, and spaced-repetition review loops for algorithm practice products.

Updated Jul 4, 2026
Search Tags:#spaced repetition#product requirements#retention#review loop#learning science#algorithm practice#mastery criteria
One-click install
npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-learning-retention-100thieves-team
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aps-learning-retention
Source: https://github.com/100Thieves-team/plady-expert-skills/tree/main/algo-experts/skills/aps-learning-retention
Command: npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-learning-retention-100thieves-team

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Product teams building algorithm-practice platforms (Baekjoon/LeetCode-style) often equate an Accepted verdict with real learning, leading to shallow progress metrics like solved counts and streaks. This Skill helps teams distinguish AC from understanding, mastery, retention, and transfer, and design review, revisit, and weak-concept loops grounded in learning science. ## Core Features & Use Cases - Learning State Ladder & Signals: Classifies progress from Exposed through Transferable, and ranks evidence signals (hint level used, mistake diagnosed, re-solve after delay, variation solved) by strength. - Mastery Criteria & Review Design: Defines goal-specific mastery rules (interview prep, competitive programming, fundamentals) and lightweight post-AC review prompts with adaptive depth. - Learning Science Reference: Covers SM-2, FSRS, Leitner schedulers, spacing and interleaving effects, Bloom's taxonomy, worked-example fading, and calibration countermeasures like predict-then-check. - Use Case: A team debating whether a hinted solve should count as solved uses this Skill to define assisted-solve handling, revisit policy, and retention metrics for their MVP. ## Quick Start Ask the Skill to define mastery criteria and a spaced-repetition revisit policy for your algorithm practice product's MVP.

Frequently Asked Questions about aps-learning-retention

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

FAQPage Schema
How do I distinguish an Accepted verdict from real learning in an algorithm practice product?▼

Use a learning-state ladder that separates Solved (AC received) from Understood, Owned, Retained, and Transferable states. AC only proves Apply-level ability; require evidence like explanations, delayed re-solves, or variation solves for higher states.

What spaced repetition algorithm should I use for problem revisit scheduling?▼

Leitner boxes are the simplest MVP for a re-solve queue. SM-2 adds per-item ease factors with 0-5 quality ratings, while FSRS predicts recall probability from review history and suits large-scale products with behavioral logging.

Should a hinted solve count as solved in mastery tracking?▼

Treat hint level as a learning signal that adjusts interpretation rather than a binary pass. Log hint and editorial usage, downweight assisted solves, and trigger delayed revisits to verify independent ability.

Why is interleaving problem types better than blocked practice?▼

Blocked practice hides the pattern-recognition step because learners already know the category. Interleaving forces technique selection before solving, which improves delayed-test performance and transfer to real interviews and contests.

What are the limitations of self-rated confidence as a learning signal?▼

Self-ratings are poorly calibrated due to fluency illusions and Dunning-Kruger effects, especially among the weakest users. Combine them with behavioral signals and use predict-then-check prompts at revisit time instead of post-AC ratings.

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