Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jimmc414/claude-code-plugin-marketplace --skill use-counter-frequencygit clone --depth 1 https://github.com/jimmc414/claude-code-plugin-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/use-counter-frequency)<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/use-counter-frequency"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/use-counter-frequency/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jimmc414/claude-code-plugin-marketplace/use-counter-frequency"><img src="https://agentmods.dev/badge/skills/jimmc414/claude-code-plugin-marketplace/use-counter-frequency.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00635 |
| Opus 5.5 | $0.00012 | $0.00254 |
| Sonnet 5.5 | $0.00006 | $0.00127 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
use-counter-frequency scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Counter is a dict: All dict methods work
- Missing keys = 0: No KeyError for missing items
- most_common(n): Get top n items efficiently
- Arithmetic works: Add, subtract, intersect counters
- Generator input:
Counter(x for x in iterable if condition)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 86 lines · 29 tokens per session scan A 3058fe804061
use-counter-frequency is a skill published in the GitHub repository jimmc414/claude-code-plugin-marketplace (4 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 635 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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