| name | aoc |
| description | 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. |
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) |
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
