TL;DR — I collected 558 AGENTS.md files from public repos and labeled each one against a 9-category
taxonomy with a rule-based classifier (no LLM in the loop, so it is auditable and recomputable). Then I
blind-labeled held-out samples and compared: 92% precision / 70% recall on 55 English files,
88% / 73% on 50 Chinese files. The most common categories are prohibitions (85.7%) and build/test
commands (82.8%). The rarest: gotchas (13.6%) and instructions about how the agent itself should behave
(25.8%).
Almost every discussion about AGENTS.md is anecdote-led: my repo's file works, my agent ignores it,
a good one is a model upgrade, a bad one is worse than nothing. All of that may be true — but nobody
seems to have the distribution. So I built it: snapshot of 558 files from 558 public repos
(2026-09-10, 5.3 MB, 516 usable for statistics), labeled, versioned, and published with the tooling.
Nine categories: boundaries, build_test, workflow, structure, style, environment, overview,
agent_meta (rules about the AI itself), gotchas. Labeling is done by pattern rules over headings and
body text — deliberately, because a rule set can be read, argued with, and re-run, and every number below
can be recomputed from the released dataset. I then measured how well the rules match a human reading:
100 files in-sample (upper bound, 90%/75%) and two held-out sets I had never tuned against —
55 English (92%/70%) and 50 Chinese (88%/73%). Held-out numbers use the conservative reading
(items I was unsure about count as classifier errors).
| category | share of 516 files |
|---|---|
boundaries (what you must never do) |
85.7% |
build_test (install/build/test/CI commands) |
82.8% |
workflow (branching, commits, review, release) |
67.1% |
structure |
59.1% |
style |
54.5% |
environment |
45.0% |
overview |
32.2% |
agent_meta |
25.8% |
gotchas |
13.6% |
The 2.9 pp gap between the top two is smaller than the known false-positive rate (~3%) of the
prohibition pattern — so the honest statement is tied for first, not "prohibitions beat build commands".
gotchas is dead last at 13.6%. Worse: when people do open a "known issues" section, a third of it
isn't a gotcha. I hand-read 120 items from those sections:
That 8% is the part an agent can never derive from the code — and it is exactly the part that is
almost never written down.
workflow
In a controlled experiment (11 repos × 3 prompt styles), prompts that listed topics explicitly produced
9/9 categories, while prompts that left the slots implicit skipped workflow in 11 out of 11 files.
Point at workflow by name and it appears 3/3 times, with real content. The gap is not knowledge,
it is questions — which is why I turned the corpus distribution into a checklist tool.
English files fail differently from Chinese ones. English: gotchas recall 32–38% — the classifier
misses casual "watch out" prose. Chinese: agent_meta recall 26% — Chinese files express agent rules
in the second person ("you are the dispatcher, not the executor"), and the body-pattern rules for that
category are entirely English, so the whole style is invisible to them. File-level exact agreement
(9/9 categories identical) is 12% in both languages.
49% point to some other file; 15% route to a knowledge or rules directory. That's a structural
fact about the format, and it means "does this repo have an AGENTS.md?" is a much weaker question than
"what is actually in it".
pip install agent-charters
agent-charters brief # checklist of the 9 slots + a paste-ready prompt
agent-charters compare your-AGENTS.md # your coverage vs the 558-file baseline
agent-charters refs your-AGENTS.md # does your file point at paths that exist
Honest note: compare is a checklist, not an oracle. It warned me that one of the nine categories was
missing from a file I wrote myself — it was actually present, but the heading used the tool's own slot name
instead of natural language. That is documented in the repo (along with the exact experiment) rather than
quietly patched, because a tool that tells you "you're missing X" should be checked by a human.
LIMITATIONS.md in the repo
I'm looking for 2–3 people who are not me to run compare on an AGENTS.md they actually maintain and
tell me where it's wrong — missing a category you clearly have, or claiming one you don't. That is the one
piece of evidence this project doesn't have yet: an external user. Issues and comments are both fine.