cd /news/large-language-models/claudeisms-a-list-of-words-claude-co… · home topics large-language-models article
[ARTICLE · art-62505] src=github.com ↗ pub= topic=large-language-models verified=true sentiment=· neutral

Claudeisms: A list of words Claude Code overuses

A developer quantified word frequency in Claude Code's responses, finding that the AI assistant overuses terms like 'load-bearing' (7,500x more than Stack Overflow comments), 'gating' (762x), and 'decisive' (639x), based on analysis of 140MB of session transcripts. The analysis compared Claude's word usage against a public dataset of 1.72 billion words from Stack Overflow comments to identify 144 words used at least 20x more frequently.

read5 min views1 publishedJul 16, 2026
Claudeisms: A list of words Claude Code overuses
Image: source

*For list see *

claudeisms.csv

"load-bearing"

Claude Code - always

I've been using Claude code for several months now. I probably say more to Claude than any human (aside: this is bleak). This also means Claude says a lot back to me.

Claude really annoys me (and many others) with its word choices sometimes. It reuses a load of words at too high a frequency. I wanted to quantify this somehow as an exercise in catharsis.

Claude code saves session text locally so I already have the dataset of my experience. I decided to run pretty simple word frequency analysis to see what the biggest claudeisms were.

The transcripts are in ~/.claude/projects/**/*.jsonl

— 175 files, 140MB, about 5 weeks.

Some stuff I filtered out:

  • Only Opus 4.7/4.8 sessions. Haiku etc has different word usage.
  • Only agent response messages that get sent to me (not tool calls or subagents as I don't have to read those)

First I just looked at the top words in the messages that I sent to Claude, I am a human after all so I thought this would be a good baseline.

Wrong - I paste a lot into Claude code, and that text is often logs / machine generated content.

My top words were things like requestid

, sessionid

, organizationid

— ie the JSON keys from log dumps I'd pasted.

Next, I downloaded the wordfreq

dataset, which is a blend of web content like Wikipedia, Reddit, books, subtitles, and news. The dataset is pre-2021, so it doesn't contain LLM outputs.

I started with a raw comparison on the multiple of word frequency per million, but that ended up being dominated by the terms related to coding and programming like eslint

yaml

serializer

etc.

So I found a comparison against humans writing about code. BigQuery has 1.72 billion words in a public dataset of Stack Overflow comments from 2008-2020.

I ranked by word frequency ratio, then removed an exclude list of words specific to my own work which mainly contains proper nouns for our names, products and tools we use

Full ranking, 144 words at 20x or above, is in claudeisms.csv. These are the top 12

word claude /M Stack Overflow Comments /M ratio
load-bearing
87 <0.012 >7,500x*
gating
123 0.16 762x
dedup
102 0.16 649x
decisive
108 0.17 639x
verdict
200 0.32 633x
scaffolds
118 0.21 572x
settles
143 0.33 435x
handoff
113 0.31 360x
prod
2,516 9.14 275x
pr
2,552 9.34 273x
gated
133 0.51 263x
genuinely
328 1.46 224x
  • Below Stack Overflow cutoff for words so this is a lower bound. A lot of hits in there for claudeisms!

Some other interesting other ones I cherry picked. These are all above 20x frequency vs the Stack Overflow dataset.

word claude /M Stack Overflow Comments /M ratio
scaffolded
87 0.42 205x
blocker
241 1.30 185x
pre-existing
559 3.04 184x
drift
323 1.76 184x
surfaced
77 0.43 181x
divergence
123 0.74 166x
round-trip
307 1.89 162x
flips
205 1.47 140x
stale
892 6.57 136x
wiring
384 2.86 134x
mint
174 1.49 117x
end-to-end
225 2.06 110x
scaffold
282 2.75 102x
landed
159 1.58 100x
framing
108 1.27 84x
surfaces
164 2.11 78x
harness
133 1.78 75x
parity
179 2.45 73x
mirrors
138 2.12 65x
seam
123 1.97 62x
genuine
128 2.18 59x
wired
236 4.02 59x
authoritative
123 2.14 58x
floor
369 7.18 51x
probe
159 3.20 50x
cleanly
354 7.21 49x
kills
184 4.08 45x
idempotent
128 3.11 41x
bare
528 13 41x
guard
553 14 40x
gap
441 12 36x
silently
415 12 36x
proves
113 3.29 34x
truth
313 9.21 34x
verified
471 15 32x
verbatim
128 4.11 31x
mirror
231 7.92 29x
canonical
292 11 27x
wins
154 5.83 26x
transient
174 6.70 26x
clean
2,537 103 24x
fallback
323 14 23x
honest
225 10 22x
surface
374 18 21x

So there's my list! An authoritative list of Claudeisms.

  • All of this analysis was done with Claude code. I did not check the code, just vibed it, and fixed it along the way until the results felt kind of right.
  • Obviously this is all highly skewed to stuff I use and do with Claude code.
  • I installed caveman

at some point which will have influenced the word frequency. - Apparently load-bearing

is in the Claude code system prompt. It says: "give brief updates when you find something load-bearing" - I didnt stem the words eg merge scaffold

andscaffolds

orgating

andgated

.

── more in #large-language-models 4 stories · sorted by recency
── more on @claude code 3 stories trending now
sponsored brought to you by zahid.host 4,200+ EU-deployed projects
reading about agents? ship yours in a single git push.

Run your AI side-project on zahid.host

EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.

$git push zahid main
Live at https://your-agent.zahid.host
Get free account → Pricing
from €0/mo · no card required
LIVE [news/claudeisms-a-list-of…] indexed:0 read:5min 2026-07-16 ·