AIPass
AGENTS.md312 starsChanged 10 days agoMITView on GitHub
Multi-agent framework. Autonomous agents (citizens) live in branches, deploy disposable sub-agents to do work. On any greeting, silently run this sequence — no narration, no announcing steps. Just do it and respond with the status. These steps are sequential and dependent — run each ONCE, wait for the result, then proceed. Never batch a command with its own follow-up read, and never fire duplicate calls. If output looks blank, wait — don't retry. Use drone commands for all operations. Never…
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,774 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository4 other files it gives its agents.
- .claude/AGENTS.mdAGENTS.md
- compassSkill
- memoSkill
- prepSkill
The file
We split this AGENTS.md into the rules it gives an agent
4 rules. About 0 are common, 1 is distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 13
Read, one file per call, never concatenated: .trinity/passport.json, .trinity/local.json, .trinity/observations.json.
- Very specific: line 14
Never read your README.md at startup — it is the face for strangers.
- Distinctive: line 19
Use drone commands for all operations.
- Very specific: line 23
Update .trinity/ at natural breakpoints, after milestones, and on /memo (Claude Code, OpenCode) or $memo (Codex).
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AIPass
Multi-agent framework. Autonomous agents (citizens) live in branches, deploy disposable sub-agents to do work.
User: user
Startup protocol
On any greeting, silently run this sequence — no narration, no announcing steps. Just do it and respond with the status.
These steps are sequential and dependent — run each ONCE, wait for the result, then proceed. Never batch a command with its own follow-up read, and never fire duplicate calls. If output looks blank, wait — don't retry.
- Read, one file per call, never concatenated:
.trinity/passport.json,.trinity/local.json,.trinity/observations.json. A "persisted output" notice means you batched: read the originals one per call, never the persisted file. - Never read your
README.mdat startup — it is the face for strangers. Your inventory isdrone @<self>(live) anddrone @<self> --help. - Refresh:
drone @prax dashboard refresh @<self>— where<self>is your branch name (CWD directory name) - Dashboard: Read
DASHBOARD.local.json— act on what needs attention (new mail → check inbox, active plans → note them). This is your single status glance.
Use drone commands for all operations. Never raw git, gh, file access, or python -m when drone provides it.
Memories
Update .trinity/ at natural breakpoints, after milestones, and on /memo (Claude Code, OpenCode) or $memo (Codex).
Todos[] are a sticky-note pad, not a log: 10 live, each {number, date, task, priority?}, the task one line under 100 chars ("Check on sentry's errors in logs"), no status field — the story lives in plans and sessions. Delete each todo the moment it's done. The oldest roll off by number to .backup/todo/<branch>/backlog.json at rollover (PreCompact) and at @memory's push; drone @memory todo backlog reads them, todo restore <n> brings one back. Rolling never closes anything, so reconcile on load: remove what is finished so it never resurfaces as open. A truncated .trinity read is a signal, not noise — say so first, then measure the file.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

