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Use Counter Frequency

skill-jimmc414-claude-code-plugin-marketplace-use-counter-frequency · by jimmc414

For counting and frequency: histograms, most common elements, vote tallying, neighbor counting, word frequencies, distribution analysis.

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Install

$ agentstack add skill-jimmc414-claude-code-plugin-marketplace-use-counter-frequency

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

use-counter-frequency

When to Use

  • Counting occurrences of items
  • Finding most common elements
  • Building histograms
  • Vote/tally counting
  • Word frequency analysis
  • Neighbor counting in grids
  • Any "how many of each" question

When NOT to Use

  • When you need the items themselves, not counts
  • Single occurrence checking (use set)
  • When counts need to be updated in complex ways

The Pattern

Use collections.Counter - a dict subclass optimized for counting.

from collections import Counter

# Count anything iterable
Counter('abracadabra')  # {'a': 5, 'b': 2, 'r': 2, 'c': 1, 'd': 1}
Counter([1, 1, 2, 3, 3, 3])  # {3: 3, 1: 2, 2: 1}

# Most common
counter.most_common(3)  # Top 3 as [(item, count), ...]

# Arithmetic
Counter('abc') + Counter('bcd')  # {'b': 2, 'c': 2, 'a': 1, 'd': 1}

# Total count
sum(counter.values())

Example (from pytudes)

from collections import Counter

# Word frequency from text (spell.py)
def words(text):
    return re.findall(r'\w+', text.lower())

WORDS = Counter(words(open('big.txt').read()))
# WORDS['the'] = 79809

def P(word, N=sum(WORDS.values())):
    """Probability of word."""
    return WORDS[word] / N

# Neighbor counting (Life.ipynb)
def neighbor_counts(world):
    """For each cell, count how many live neighbors it has."""
    return Counter(neighbor
                   for cell in world
                   for neighbor in neighbors(cell))

# Use for Game of Life rules
counts = neighbor_counts(world)
new_world = {cell for cell, count in counts.items()
             if count == 3 or (count == 2 and cell in world)}

# Probability distribution (Probability.ipynb)
class Dist(Counter):
    """A distribution of {outcome: frequency}."""
    pass

DK = Dist(GG=121801, GB=126840, BG=127123, BB=135138)
# Access: DK['GG'], DK.most_common(), sum(DK.values())

Key Principles

  1. Counter is a dict: All dict methods work
  2. Missing keys = 0: No KeyError for missing items
  3. most_common(n): Get top n items efficiently
  4. Arithmetic works: Add, subtract, intersect counters
  5. Generator input: Counter(x for x in iterable if condition)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.