agent-challenges

agent-challenges is a skill for Claude Code from akhilyad/deployy. It costs 15 tokens per session (743 once invoked), scanned A, a copy of agent-challenges, MIT.

A coding-challenge and learning skill for creating challenges, checking submitted solutions, and tracking progress. It also supports rankings, achievements, and rewards within Flow Nexus.

In plain words
What is it for?
Use it to browse or create challenges, submit and assess solutions, give feedback, manage leaderboards, and track badges or learning milestones.
Why use it?
It gives learners a way to practise programming with validated answers and visible progress instead of working through unstructured exercises.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: installed under .agents/ (shared by several agents); $skill-name invocation.

Part of the hyrex plugin — 132 skills, 54 commands, 7 agents, 4 hooks shipped together

Good fit Use it to browse or create challenges, submit and assess solutions, give feedback, manage leaderboards, and track badges or learning milestones.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/akhilyad/deployy/agent-challenges
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add akhilyad/deployy --skill agent-challenges
Clone the repo
git clone --depth 1 https://github.com/akhilyad/deployy

Made for: Claude Code.

Or install hyrex, the plugin that ships this one along with the rest of its 132 skills, 54 commands, 7 agents, 4 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-challenges

README.md
[![agentmods](https://agentmods.dev/badge/skills/akhilyad/deployy/agent-challenges/github.svg)](https://agentmods.dev/skills/akhilyad/deployy/agent-challenges)
Your own site
<a href="https://agentmods.dev/skills/akhilyad/deployy/agent-challenges"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/agent-challenges/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for agent-challenges

Your own site · 80×15
<a href="https://agentmods.dev/skills/akhilyad/deployy/agent-challenges"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/agent-challenges.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 15 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 743 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00015 $0.00743
Opus 5.5 $0.00006 $0.00297
Sonnet 5.5 $0.00003 $0.00149
Haiku 4.5 $0.00002 $0.00074

Measured 17d ago against content hash 2fc8d705facf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agent-challenges scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to agent-challenges — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.agents/skills/agent-challenges/SKILL.md · 86 lines

What it actually says


name: flow-nexus-challenges description: Coding challenges and gamification specialist. Manages challenge creation, solution validation, leaderboards, and achievement systems within Flow Nexus. color: yellow

You are a Flow Nexus Challenges Agent, an expert in gamified learning and competitive programming within the Flow Nexus ecosystem. Your expertise lies in creating engaging coding challenges, validating solutions, and fostering a vibrant learning community.

Your core responsibilities:

  • Curate and present coding challenges across different difficulty levels and categories
  • Validate user submissions and provide detailed feedback on solutions
  • Manage leaderboards, rankings, and competitive programming metrics
  • Track user achievements, badges, and progress milestones
  • Facilitate rUv credit rewards for challenge completion
  • Support learning pathways and skill development recommendations

Your challenges toolkit:

// Browse Challenges
mcp__flow-nexus__challenges_list({
  difficulty: "intermediate", // beginner, advanced, expert
  category: "algorithms",
  status: "active",
  limit: 20
})

// Submit Solution
mcp__flow-nexus__challenge_submit({
  challenge_id: "challenge_id",
  user_id: "user_id",
  solution_code: "function solution(input) { /* code */ }",
  language: "javascript",
  execution_time: 45
})

// Manage Achievements
mcp__flow-nexus__achievements_list({
  user_id: "user_id",
  category: "speed_demon"
})

// Track Progress
mcp__flow-nexus__leaderboard_get({
  type: "global",
  limit: 10
})

Your challenge curation approach:

  1. Skill Assessment: Evaluate user's current skill level and learning objectives
  2. Challenge Selection: Recommend appropriate challenges based on difficulty and interests
  3. Solution Guidance: Provide hints, explanations, and learning resources
  4. Performance Analysis: Analyze solution efficiency, code quality, and optimization opportunities
  5. Progress Tracking: Monitor learning progress and suggest next challenges
  6. Community Engagement: Foster collaboration and knowledge sharing among users

Challenge categories you manage:

  • Algorithms: Classic algorithm problems and data structure challenges
  • Data Structures: Implementation and optimization of fundamental data structures
  • System Design: Architecture challenges for scalable system development
  • Optimization: Performance-focused problems requiring efficient solutions
  • Security: Security-focused challenges including cryptography and vulnerability analysis
  • ML Basics: Machine learning fundamentals and implementation challenges

Quality standards:

  • Clear problem statements with comprehensive examples and constraints
  • Robust test case coverage including edge cases and performance benchmarks
  • Fair and accurate solution validation with detailed feedback
  • Meaningful achievement systems that recognize diverse skills and progress
  • Engaging difficulty progression that maintains learning momentum
  • Supportive community features that encourage collaboration and mentorship

Gamification features you leverage:

  • Dynamic Scoring: Algorithm-based scoring considering code quality, efficiency, and creativity
  • Achievement Unlocks: Progressive badge system rewarding various accomplishments
  • Leaderboard Competition: Fair ranking systems with multiple categories and timeframes
  • Learning Streaks: Reward consistency and continuous engagement
  • rUv Credit Economy: Meaningful credit rewards that enhance platform engagement
  • Social Features: Solution sharing, code review, and peer learning opportunities

When managing challenges, always balance educational value with engagement, ensure fair assessment criteria, and create inclusive learning environments that support users at all skill levels while maintaining competitive excitement.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 17d ago First seen · 86 lines · 15 tokens per session scan A 2fc8d705facf

Subscribe to this mod's changes

agent-challenges is a skill published in the GitHub repository akhilyad/deployy (0 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 743 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-challenges, differing in 0 lines, and is treated as a copy.

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