Daily Code Challenge

Generates a fresh coding challenge every day matched to your skill level, preferred languages, and focus areas. Includes progressive hints, optimal solutions with complexity analysis, and tracks your progress over time. Supports practice for LeetCode-style interviews, competitive programming, or general skill building across Python, JavaScript, TypeScript, Go, Rust, Java, and more.

Supported Platforms & Integrations

Platform	Setup Method	Auth Type	Notes
VS Code	Terminal integration	None	Run directly in integrated terminal
JetBrains IDEs	Terminal integration	None	Works in any JetBrains terminal
GitHub	Gist API for solution sharing	OAuth token	Optionally save solutions as private gists
LeetCode	Problem mapping reference	None	Maps challenges to equivalent LeetCode problems

When to Use This Skill

• Use this when preparing for technical interviews and want daily practice
• Use this when you want to learn a new language through practical problem-solving
• Use this when you want to maintain algorithmic skills while working on non-algorithmic day jobs
• Consider alternatives when you want collaborative/competitive challenges (use LeetCode contests)

Quick Start

Copy
# Minimal configuration - code-challenge.yml
skill: daily-code-challenge
languages: ["python", "typescript"]
difficulty: intermediate
focus_areas:
  - "arrays-and-hashing"
  - "dynamic-programming"
  - "trees-and-graphs"
  - "system-design-coding"
challenge_type: "interview-prep"
schedule: "09:00"

Copy
claude /daily-code-challenge

Expected Output

DAILY CODE CHALLENGE #73 - March 15, 2026
Difficulty: Medium | Topic: Dynamic Programming | Est. time: 25 min

PROBLEM: Minimum Cost to Merge Stones

You have N piles of stones arranged in a row. The i-th pile has
stones[i] stones. A move consists of merging exactly K consecutive
piles into one pile at a cost equal to the total number of stones
in those K piles.

Find the minimum cost to merge all piles into one pile.
Return -1 if impossible.

EXAMPLES:
...

Advanced Configuration

Platform-Specific Setup

Interview Prep Mode

Copy
challenge_type: "interview-prep"
interview_prep:
  target_company: "FAANG"
  timeline_weeks: 12
  topics_per_week: 3
  include_patterns: true
  difficulty_distribution:
    easy: 20

Full Options Reference

Parameter	Type	Default	Description
languages	array	["python"]	Preferred solution languages
difficulty	string	"medium"	Base difficulty: easy, medium, hard, adaptive
focus_areas	array	all	Algorithm/data structure topics to focus on
challenge_type	string	"general"	Type: general, interview-prep, competitive, real-world
schedule	string	"09:00"	Daily challenge delivery time
hints_available	number	3	Number of progressive hints per problem

Core Concepts

Concept	Purpose	How It Works
Adaptive Difficulty	Matches challenge to your growth	Tracks solve time and hint usage; adjusts difficulty dynamically
Topic Cycling	Ensures balanced practice	Rotates through focus areas on a weekly cycle, preventing topic neglect
Pattern Recognition	Builds algorithmic intuition	Tags each problem with common patterns (sliding window, two pointers, etc.)
Progressive Hints	Encourages independent thinking	Three-tier hints: approach direction, key insight, partial pseudocode

Architecture

Problem Bank ──> Topic Selector ──> Difficulty Filter ──> Challenge Generator
       |                                                          |
  Progress DB ──> Adaptive Engine ──────────────────────> Hint System

Workflow Examples

Scenario 1: Interview prep -- 8 weeks to Google on-site

Input: Target FAANG, 8-week timeline, current level intermediate, focus on DP and graphs Processing: Generates structured 8-week plan with 5 challenges per week. Weeks 1-3 build foundations with medium problems. Weeks 4-6 introduce hard problems. Weeks 7-8 are mixed mock interview sets with time pressure. Output: Daily challenge with countdown ("Week 3 of 8, Day 4") and topic progress bars showing coverage across all interview categories.

Scenario 2: Learning Go through daily problems

Input: Languages ["go"], level beginner, prior knowledge in Python, challenge_type "general" Processing: Generates problems solvable in any language but provides Go-specific starter code with idiomatic patterns. Each solution explanation highlights Go-specific features (goroutines, channels, slices vs arrays). Output: Challenge with Go starter file, solution in Go with Python comparison, and "Go idiom of the day" bonus tip.

Best Practices

1. Attempt before hinting -- Spend at least 15 minutes on a problem before requesting hints. The struggle phase is where pattern recognition develops. If.

2. Review solutions even when you solve correctly -- The optimal solution often differs from your first working solution. Reviewing teaches cleaner approaches and better.

3. Maintain a problem journal -- After each challenge, note the pattern used and any mistakes made. Review this journal weekly. Patterns you have seen 3+ times.

Common Issues

1. Challenge too easy or hard for days in a row -- The adaptive engine needs 10-15 data points to calibrate. Run claude /daily-code-challenge --recalibrate to take a placement.

2. Solution submission fails validation -- Ensure your function signature matches the starter code exactly. The test runner is strict about return types. Check edge.

Privacy & Data Handling

All challenges are generated and evaluated locally. Your solution code never leaves your machine. Progress data (problems attempted, solve times, difficulty ratings) is stored in ~/.claude/data/code-challenge/ as JSON. No code or progress data is sent to external services.