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For cross-cutting concerns: add behavior without modifying functions, caching, timing, logging, validation wrappers.
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Baseado na classificação ocupacional SOC
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 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.
For persistent state: closures capture outer variables, alternative to classes for simple state, factory functions that remember context.
| name | apply-decorator-wrap |
| description | For cross-cutting concerns: add behavior without modifying functions, caching, timing, logging, validation wrappers. |
Decorators wrap functions to add behavior before, after, or around the original.
def timing(func):
"""Decorator to time function execution."""
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
elapsed = time.time() - start
print(f"{func.__name__} took {elapsed:.3f}s")
return result
return wrapper
@timing
def slow_function():
time.sleep(1)
return "done"
# Equivalent to: slow_function = timing(slow_function)
# Memoization decorator (ngrams.py)
def memo(f):
"""Memoize function f."""
table = {}
def fmemo(*args):
if args not in table:
table[args] = f(*args)
return table[args]
fmemo.memo = table # Expose cache
return fmemo
@memo
def segment(text):
"""Optimal word segmentation."""
if not text:
return []
candidates = ([first] + segment(rest)
for first, rest in splits(text))
return max(candidates, key=word_prob)
# Using functools for cleaner decorators
from functools import cache, lru_cache, wraps
@cache # Built-in memoization
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
@lru_cache(maxsize=1000) # Limited cache size
def expensive_lookup(key):
...
# Reusable decorator with parameter
cache = lru_cache(None) # Alias for unlimited cache
@cache
def expressions(numbers):
...
@cache
def segment(text):
...
@functools.wraps@lru_cache(n) returns decorator