What language are agent skills written in? · Plicara Labs

In the first quarter of 2026, 13.0% of newly written agent skills were in a language other than English, and one quarter later it was 16.3%. That is three points in three months across 255,068 skills, with confidence intervals nowhere near touching. For comparison, GitHub-wide non-English documentation took ten years to travel from 3.7% to 13.0%, so whatever is happening here is happening at a different speed entirely, and the most plausible explanation is that AI development has arrived somewhere other than San Francisco. Reviewing the data, it turns out that the claim is stronger than the obvious version of it, because English is not a proxy for American. GitHub's fastest-growing developer population by a wide margin is India, which writes in English, as do Nigeria and Singapore, so a language count cannot see any of them. The non-English share is therefore not a measure of how much of this ecosystem sits outside the United States. It is a floor beneath it, and everything below should be read that way. For context, a skill is a SKILL.md file in a folder, holding instructions for an AI agent in plain prose, loaded when the agent judges the task relevant. Anthropic published the specification in October 2025, and it spreads the way a recipe spreads: somebody copies it. Nine months later there were 3.8 million of them across 282,200 public repositories, which is what the GitSkills dataset collects. Skills are strange as software, by which we mean the traditional kind, because this is one of the things AI has upended. They are written in human language and the runtime is a multilingual model, so there is no technical reason to write one in English: a developer in Shenzhen or São Paulo can state a procedure more precisely in their own language, and the agent will follow it. Whether it follows it as well is a better question, and much harder to answer than anything a file crawl can settle. the distribution We ran language identification over the prose body of every distinct skill, after stripping front matter and fenced code. horizontal bar chart, language distribution English 85.3% Chinese 中文 6.2% Japanese 日本語 1.7% German Deutsch 1.6% Korean 한국어 1.2% Portuguese Português 1.1% Spanish Español 0.9% French Français 0.4% 1,870,299 distinct skill contents. The 14.3% that are not English are led by Chinese. Language Share of distinct skills English 85.3% Chinese 6.2% Japanese 1.7% German 1.6% Korean 1.2% Portuguese 1.1% Spanish 0.9% French 0.4% So 14.3% of skills are not in English, and split by script the Chinese ones run 104,985 simplified against 9,112 traditional. The rows above do not quite sum to that, because 6,810 skills came back below our confidence floor and are counted as neither. The comparison worth making is against GitHub's own documentation instead of its issues or pull requests, and a 2026 ICSE study put repository documentation at 13.0% non-English, with Chinese at 3.3% of repositories. In aggregate that makes skills unremarkable, 14.3% against 13.0% being a dead heat. They are markedly more Chinese, though, 6.2% against 3.3%. why every published number disagrees Ours is not the only published figure, and the published figures do not agree with each other. Reported English share Corpus Method 65.0% 557 healthcare skills, ClawHub (2605.02709) not stated 81.8% 26,502 skills, ClawHub (2604.13064) not stated 85.3% 1,870,299 distinct, GitHub (ours) py3langid, conf >= 0.80 92.6% 133,149 skills, skills.sh (2607.01456) fast-langdetect 99.7% English-seeded crawl (2606.03565) seeded These are not contradictions, they are five different populations: curated marketplaces skew English, domain slices skew toward wherever that domain happens to be active, and a crawl seeded with English queries will find English. The first candidate to rule out is us, because if our identifier simply saw less English than everyone else's then the whole comparison would be an artifact of tooling. So we ran both over the same documents, py3langid which we use and fast-langdetect which the 92.6% study used. They agree on 97.6% of documents, and their English shares sit +1.2 points apart against a gap of around seven. Quality screening looks like the next good candidate and leads nowhere either: if corpora that filter for valid front matter were quietly discarding non-English skills that would explain some of the spread, but non-English skills have slightly better front-matter validity, 88.1% against 86.6%, and filtering moves the English share only from 85.6% to 85.4%. What is left is where you looked. That generalises well past this dataset, so when someone tells you what "the AI ecosystem" looks like, the registry they scraped may hold more of the answer than anything else they say. skills are getting less English Skills carry commit history, so each one has a creation date, and that turns a static pie chart into a trend. non-English share by month, with confidence band 0%8%16%24% 25·1025·1125·1226·0126·0226·0326·0426·0526·0626·07 Band is the 95% Wilson interval. July 2026 is shaded: collection ran mid-month, so that cohort is censored and excluded from comparisons. Quarter Non-English share 2026 Q1 13.0% [12.8, 13.1] 2026 Q2 16.3% [16.1, 16.4] Month by month the climb is not smooth, since February dips to 10.9% before March resumes at 14.2%, but the direction across the window is not in doubt: 13.1% in January against 17.6% in June. That is roughly what you would expect of a format eighteen months old, since new artifact types acquire their demographics much faster than mature ones when there is no incumbency to overcome. But "non-English" is not one thing, and broken out, the rise turns out to be carried by two of the four groups rather than by all of them. small multiples, share by quarter per language, with confidence bands 0%4%8% 25-Q326-Q3 Chinese +2.1 pts 25-Q4 → 26-Q2 0%4%8% 25-Q326-Q3 Japanese -1.0 pts 25-Q4 → 26-Q2 0%4%8% 25-Q326-Q3 Korean +0.3 pts 25-Q4 → 26-Q2 0%4%8% 25-Q326-Q3 European +4.2 pts 25-Q4 → 26-Q2 Shaded band is the 95% Wilson interval; the hollow final point is the censored July cohort, plotted but never compared. European groups German, French, Spanish, Portuguese, Italian, Russian and Dutch. 2026 Q1 2026 Q2 Change Chinese 4.2% [4.1, 4.4] 5.3% [5.2, 5.4] +1.1 European 2.7% [2.6, 2.8] 5.5% [5.4, 5.6] +2.8 Korean 2.2% [2.1, 2.3] 2.0% [1.9, 2.0] -0.2 Japanese 3.2% [3.1, 3.3] 2.5% [2.4, 2.5] -0.7 European languages, by which we mean German, French, Spanish, Portuguese, Italian, Russian and Dutch grouped together, more than double across the window while Chinese climbs steadily, and Japanese and Korean do neither: Japanese was the most common non-English language at the end of 2025 and slipped through the first half of 2026 as everyone else arrived, while Korean stays flat throughout. The censored July cohort hints that Japanese is recovering, and we are not counting it. Changes are measured between the two complete quarters, 2026 Q1 and Q2, since the final column is the July collection month and is censored, so it appears in the chart but never in a comparison. why we believe it A trend like this is exactly the kind of thing that turns out to be an artifact, so we spent longer trying to break it than we did finding it. Commit history exists for only 24% of skills, and that subsample leans toward heavily copied ones, which matters because copying turns out to be strongly related to language. The worry, in other words, is that we are watching a selection effect and not a change in what people write. Holding copies fixed at one, the rise is larger than the headline, 14.7% to 18.1%; counting each repository only once, so that no bulk uploader can swing it, the rise survives at 14.5% to 16.8%. the clock Commit timestamps are stored in UTC, so an author's local timezone is gone before we ever see the file. But people mostly commit while they are awake, and if a group of skills is written by people in one part of the world, their commits should vanish during that region's night. 24-hour dials, one per language 35.7% Englishn=384,979 15.3% Chinesen=21,939 15.9% Japanesen=12,908 18.7% Koreann=9,388 46.5% Spanish/Portuguesen=9,936 Centre figure is the share of first commits in that window. The non-English groups are small, so read the contrast, not the decimals. Language Commits during East Asian night n English 35.7% 384,979 Chinese 15.3% 21,939 Japanese 15.9% 12,908 Korean 18.7% 9,388 Spanish/Portuguese 46.5% 9,936 Chinese-language skills fall to less than half the English rate in that window, while English itself stays flat across all twenty-four hours, which is the signature of a globally distributed population with no single night. Spanish and Portuguese run the opposite way and peak at 19:00 UTC, mid-afternoon in Brazil and late evening in Iberia, which places those authors in the Americas. Nothing in the language identifier knows what time a file was committed, so the two signals are independent, and they agree. Honest limits. This is a population-level phase estimate, good to a couple of hours at best; it cannot separate UTC+8 from UTC+9, a language is not a country, and it says nothing whatsoever about any individual author. We found no published validation of hour-of-day inference at this granularity, so treat it as corroboration and not as geolocation. A raw git commit does record the author's UTC offset, and this dataset normalised it away, which is the fix for anyone building on this. the same story from outside We are reading one artifact type on one platform, so the question that matters is whether anyone measuring something else sees the same movement, and they do, at a larger scale than we can.