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For 2D debugging: visualize grid/board state, show puzzle progress, make algorithm behavior visible.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | display-grid-state |
| description | For 2D debugging: visualize grid/board state, show puzzle progress, make algorithm behavior visible. |
Create a formatted text representation of grid state.
def display_grid(grid, width, height):
"""Print grid as 2D representation."""
for y in range(height):
row = ''.join(grid.get((x, y), '.') for x in range(width))
print(row)
print()
# sudoku.py - display with separators
def display(values):
"""Display values as a 2-D grid."""
width = 1 + max(len(values[s]) for s in squares)
line = '+'.join(['-' * (width * 3)] * 3)
for r in rows:
print(''.join(
values[r + c].center(width) + ('|' if c in '36' else '')
for c in cols
))
if r in 'CF':
print(line)
print()
# Output:
# 4 8 3 |9 2 1 |6 5 7
# 9 6 7 |3 4 5 |8 2 1
# 2 5 1 |8 7 6 |4 9 3
# ------+------+------
# 5 4 8 |1 3 2 |9 7 6
# ...
# Side by side comparison (Sudoku.ipynb)
def print_side_by_side(left, right, width=20):
"""Print two strings side-by-side, line-by-line."""
for L, R in zip(left.splitlines(), right.splitlines()):
print(L.ljust(width), R.ljust(width))
# Grid class with print method (AdventUtils.ipynb)
class Grid(dict):
def print(self, sep='', xrange=None, yrange=None):
"""Print a representation of the grid."""
for row in self.to_rows(xrange, yrange):
print(*row, sep=sep)
def to_rows(self, xrange=None, yrange=None):
"""Contents as rectangular list of lists."""
xrange = xrange or cover(Xs(self))
yrange = yrange or cover(Ys(self))
default = ' ' if self.default in (KeyError, None) else self.default
return [[self.get((x, y), default) for x in xrange]
for y in yrange]
Conducts iterative deep research on any topic using web search, progressive exploration, and structured synthesis. Use when asked for comprehensive research, deep investigation, thorough analysis, or multi-source exploration of any topic. Triggers: research, investigate, deep dive, comprehensive analysis, explore thoroughly, find everything about.
For cross-cutting concerns: add behavior without modifying functions, caching, timing, logging, validation wrappers.
For performance work: measure before changing, profile to find bottlenecks, compare before and after.
For symbolic computation: ASTs, mathematical expressions, code that manipulates code structure, expression transformations.
For ordered processing: A* search, Dijkstra, event simulation, task scheduling. Efficient min/max extraction with heap-based queue.
For dynamic programming: overlapping subproblems, recursive solutions with repeated computations, memoization to avoid redundant work.