skills.rest
100Thieves-team
100Thieves-teamOFFICIAL
Explore all skills by this author →
GitHub

aps-assessment-contest

Defines mode rules, fairness constraints, and score semantics for algorithm assessment and contest features.

Updated Jul 4, 2026
Search Tags:#integrity#assessment design#product requirements#mock test#contest simulator#rating systems#score semantics
One-click install
npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-assessment-contest-100thieves-team
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aps-assessment-contest
Source: https://github.com/100Thieves-team/plady-expert-skills/tree/main/algo-experts/skills/aps-assessment-contest
Command: npx skills add https://github.com/100Thieves-team/plady-expert-skills --skill aps-assessment-contest-100thieves-team

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Product teams building algorithm-problem-solving platforms often blur the line between practice and evaluation, producing misleading scores, unfair rankings, and integrity risks. This Skill helps teams cleanly separate learning, practice, mock test, contest, and interview modes by defining mode rules, feedback limits, score meaning, and integrity constraints. ## Core Features & Use Cases - Mode Taxonomy & Policy Design: Distinguishes learning, practice, mock test, coding-test prep, live/virtual contest, interview training, and assignment modes with allowed support and feedback rules per mode. - Contest Format & Scoring Knowledge: Covers ICPC, IOI, Codeforces, AtCoder, TopCoder formats, Elo-style rating systems, IRT/CAT adaptive testing, and psychometric validity/reliability/fairness concepts. - Integrity & Proctoring Guidance: Frames plagiarism detection, proctoring, telemetry flags, and honor codes scaled to assessment stakes. - Use Case: When scoping a contest simulator MVP, the team asks how to handle pretests vs system tests, penalty scoring, and AI hint restrictions, and receives structured mode policy and requirements documents. ## Quick Start Ask the skill to define the rules and score semantics for separating practice mode from mock test mode in your algorithm practice product.

Frequently Asked Questions about aps-assessment-contest

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

FAQPage Schema
How do I separate practice mode from mock test mode in a coding practice product?▼

Define explicit mode selection with different assistance and feedback rules per mode. Practice allows gradual hints and tracking, while mock tests restrict feedback during the attempt and log the mode at submission time so scores stay meaningful.

What contest formats should a contest simulator support?▼

Established formats include ICPC (binary AC with penalty time), IOI (subtask partial scoring), Codeforces (pretests vs system tests, dynamic scoring), AtCoder (including optimization-scored AHC), and TopCoder (speed-based points). Naming the target format fixes most scoring and feedback requirements.

Should AI hints be allowed during coding test preparation?▼

It depends on the mode. Hints are acceptable in learning and practice modes but invalidate scores in summative mock tests. The skill recommends logging assistance at attempt time and separating assisted from unassisted metrics.

How do contest rating systems like Codeforces work?▼

Ratings are Elo-derived relative skill estimates that change based on expected versus actual rank against the field. Allowing assistance in rated contexts contaminates the user's estimate and distorts rankings for others, making it a primary integrity concern.

What are the limits of mock test scores for predicting real coding test readiness?▼

A readiness score claims predictive validity, which requires outcome data to support it. Scores are only meaningful relative to their rule set, so comparing across different assistance levels or claiming company readiness from generic problems is unreliable.

When is proctoring justified for online assessments?▼

Proctoring intensity should scale with stakes. Low-stakes learning mocks justify only honor codes and telemetry flags, while certification or hiring tests may justify similarity detection like MOSS and remote proctoring despite privacy and fairness trade-offs.

Related Skills

Explore More Product & Management Skills→
martinffxmartinffx
39

spec:product

Document product requirements and define feature scope from discovery interviews.

Community
Advanced
rakedziorrakedzior

measure

Defines UX success metrics, A/B tests, and funnel analyses using HEART and GSM frameworks.

Community
Advanced
xiaoshan1234xiaoshan1234

ielts-study-plan

Plan evidence-based IELTS study sessions, reviews, and course routes for Chinese learners.

Community
Advanced
vicharanashalavicharanashala
6

Bounds

Generate randomized arithmetic problems and validate answers against precision tolerances.

Official
Intermediate