autonomous-math-ai
AGENTS.md2 starsChanged 3 months agoMITView on GitHub
- This repository contains only the generic Autonomous Math AI harness, its schemas, policy packs, documentation, and neutral tests. - Never add conjecture-specific statements, claims, prompts, task packets, runs, outcomes, audits, proofs, or research artifacts. - Integration with a research project must use the public amr CLI and that project's autonomous/project.json; do not assume a monorepo or projects/ directory. - Preserve falsification-first scheduling, independent audit, append-only evidence, canonical guards, crash recovery, schema preflight, and mechanical worker isolation. - Model output…
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 31,700 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 repositoryOne other file it gives its agents.
- math-researchSkill
The file
We split this AGENTS.md into the rules it gives an agent
5 rules. About 1 is common, 3 are distinctive, and 1 is very specific to this project.
≈1
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 7
Never add conjecture-specific statements, claims, prompts, task packets, runs, outcomes, audits, proofs, or research artifacts.
- Very specific: line 20
When a project supplies autonomous/semantics.json, treat its research contract and registry as declarative canonical inputs, never as …
- Common: line 28
Do not read, print, persist, or request authentication secrets.
- Distinctive: line 36
Keep fixtures mathematically neutral and portable.
- Distinctive: line 37
Run unit tests, release-content scanning, build/install acceptance, and git diff --check before publishing.
≈ “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
Repository instructions
Scope
- This repository contains only the generic Autonomous Math AI harness, its schemas, policy packs, documentation, and neutral tests.
- Never add conjecture-specific statements, claims, prompts, task packets, runs, outcomes, audits, proofs, or research artifacts.
- Integration with a research project must use the public
amrCLI and that project'sautonomous/project.json; do not assume a monorepo orprojects/directory.
Trust boundary
- Preserve falsification-first scheduling, independent audit, append-only evidence, canonical guards, crash recovery, schema preflight, and mechanical worker isolation.
- Model output is never proof by itself. Trusted state changes require the deterministic controller and audit/canonical gates.
- When a project supplies
autonomous/semantics.json, treat its research contract and registry as declarative canonical inputs, never as trusted verification state. EnforceNo unverified bridge into trusted final claimsfrom controller-owned candidate/audit receipts; validator success or agreement among agents cannot replace bridge evidence. - Projects without semantic metadata remain legacy-compatible and report the
independent semantic status
UNREVIEWED; do not convert that status into a claim, trust, evidence, or execution failure. - Do not read, print, persist, or request authentication secrets.
- Fast is permitted only when
execution.fast_mode=trueis explicitly pinned for the run. Request onlyfast; acceptprioritysolely as its observed server alias. Otherwise reject fast/priority/ultrafast, and keep mechanical tiers null.
Validation
- Keep fixtures mathematically neutral and portable.
- Run unit tests, release-content scanning, build/install acceptance, and
git diff --checkbefore publishing.
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.

