KS
Killer-Skills

aoc — Categories.community

v1.0.0
GitHub

About this Skill

Perfect for Code Analysis Agents needing advanced problem-solving capabilities with Test-Driven Development (TDD) and correctness-first approach. The most comprehensive Claude Code skills registry | Web Search: https://skills-registry-web.vercel.app

majiayu000 majiayu000
[0]
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Updated: 2/20/2026

Quality Score

Top 5%
75
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add majiayu000/claude-skill-registry/aoc

Agent Capability Analysis

The aoc MCP Server by majiayu000 is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Perfect for Code Analysis Agents needing advanced problem-solving capabilities with Test-Driven Development (TDD) and correctness-first approach.

Core Value

Empowers agents to solve Advent of Code puzzles using a language-agnostic approach with TDD, parsing input into data structures, and implementing minimal code to pass tests, all while utilizing correctness-first principles and adapting solutions for Part 2 challenges.

Capabilities Granted for aoc MCP Server

Automating Advent of Code puzzle solutions with TDD
Generating data structures from puzzle input
Debugging puzzle solutions using a correctness-first approach

! Prerequisites & Limits

  • Requires understanding of TDD and correctness-first principles
  • Language-agnostic, but may require additional setup for certain languages
  • Limited to Advent of Code puzzle solving
Project
SKILL.md
2.7 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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Advent of Code Solver

Language-agnostic problem-solving with TDD and correctness-first approach.

Workflow

1. READ      → Study problem + examples (examples are your spec)
2. PARSE     → Extract data structures from input
3. TEST      → Write tests from example input/output
4. IMPLEMENT → Minimal code to pass
5. RUN       → Execute on real input
6. ADAPT     → Refactor for Part 2

Solution Architecture

parse(input) → data structure
part1(data)  → answer
part2(data)  → answer

Parse once. Solve both parts. Test each function independently.

Algorithm Selection

ScenarioAlgorithm
Unweighted shortest pathBFS
Path existence / exhaustiveDFS
Weighted shortest pathDijkstra
Weighted + good heuristicA*
"After N iterations..." (huge N)Cycle detection
"Find minimum X such that..."Binary search
"Count ways..." / "Min/max..."Dynamic programming
Connected regionsFlood fill

Deep dive: See algorithms.md

Input Patterns

FormatApproach
Numbers in textRegex -?\d+
Grid of chars2D array or dict by coords
Blank-line groupsSplit on \n\n first
Key-value pairsParse into map/dict
Instructions/opcodesPattern match each line

Grids: Use (row, col) with row↓. Sparse dict for infinite/sparse grids. Directions: UP=(-1,0), DOWN=(1,0), LEFT=(0,-1), RIGHT=(0,1)

Deep dive: See parsing.md

Part 2 Patterns

  1. Scale up → Optimize algorithm
  2. Add dimensions → 2D → 3D/4D
  3. Many iterations → Find cycle, skip ahead
  4. Reverse question → "Find X" → "Given X, find Y"
  5. Add constraints → New rules or edge cases

Debugging

  • Print intermediate state at each step
  • Compare with example walkthrough
  • Add assertions for every assumption
  • Test parsing separately from logic
  • Binary search on input size to isolate failures

Complexity Targets

Input SizeTarget
n ≤ 20O(2^n) OK
n ≤ 500O(n³) OK
n ≤ 10,000O(n²) OK
n ≤ 1,000,000O(n log n)
n > 1,000,000O(n) or O(log n)

Research Tools

# gh search code for algorithm implementations
gh search code "heapq.heappush" --language=python   # Dijkstra/priority queue
gh search code "collections.deque" --language=python # BFS patterns
gh search code "fn dijkstra" --language=rust

References

  • algorithms.md - Graph traversal, DP, cycle detection, search
  • parsing.md - Input formats, grids, coordinates, hex grids
  • reference.md - Data structures, optimization, anti-patterns

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