aoc

Development

Solve Advent of Code puzzles, algorithm challenges, and competitive programming problems. Activate when user provides AoC problem context/input, mentions solving puzzles/challenges, asks about algorithm selection (BFS, DFS, DP, etc.), or needs help with parsing structured input.

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/development/aoc/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

Scenario	Algorithm
Unweighted shortest path	BFS
Path existence / exhaustive	DFS
Weighted shortest path	Dijkstra
Weighted + good heuristic	A*
"After N iterations..." (huge N)	Cycle detection
"Find minimum X such that..."	Binary search
"Count ways..." / "Min/max..."	Dynamic programming
Connected regions	Flood fill

Deep dive: See algorithms.md

Input Patterns

Format	Approach
Numbers in text	Regex `-?\d+`
Grid of chars	2D array or dict by coords
Blank-line groups	Split on `\n\n` first
Key-value pairs	Parse into map/dict
Instructions/opcodes	Pattern 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

Scale up → Optimize algorithm
Add dimensions → 2D → 3D/4D
Many iterations → Find cycle, skip ahead
Reverse question → "Find X" → "Given X, find Y"
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 Size	Target
n ≤ 20	O(2^n) OK
n ≤ 500	O(n³) OK
n ≤ 10,000	O(n²) OK
n ≤ 1,000,000	O(n log n)
n > 1,000,000	O(n) or O(log n)

References

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