Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/aantich/oneringai/agents-mdgit clone --depth 1 https://github.com/aantich/oneringaiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/aantich/oneringai/agents-md)<a href="https://agentmods.dev/instructions/aantich/oneringai/agents-md"><img src="https://agentmods.dev/badge/instructions/aantich/oneringai/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.04114 | $0.04114 |
| Opus 5 | $0.02057 | $0.02057 |
| Sonnet 5 | $0.00823 | $0.00823 |
| Haiku 4.5 | $0.00411 | $0.00411 |
Grade A, and why
oneringai AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OneRingAI Agent Guide
This is the canonical, vendor-neutral guide for coding agents that use or
modify @everworker/oneringai. It is written for OpenAI Codex, Claude Code,
custom coding agents, and humans delegating integration work to them.
Read this file before generating OneRingAI code. For exhaustive detail, follow the documentation links near the end instead of guessing an API.
Fast facts
- Package:
@everworker/oneringai - Runtime: Node.js 22.13+ or 24+
- Language: strict TypeScript
- Package format: ESM with ESM and CJS build outputs
- Public API: import from
@everworker/oneringaiunless a documented subpath is explicitly required - Architecture: connector-first, plugin-first context, one shared tool manager
Determine your operating mode
Using OneRingAI as a dependency
- Import only from the package's public exports.
- Do not import
src/**,dist/**, or undocumented implementation paths. - Read the installed package's
README.md,AGENTS.md,USER_GUIDE.md, andAPI_REFERENCE.mdwhen they are available undernode_modules/@everworker/oneringai/. - Prefer runnable patterns from
examples/in the repository over invented method names or provider-specific SDK calls.
Modifying the OneRingAI repository
- Preserve the connector-first and plugin-first invariants below.
- Relative TypeScript imports include the
.jsextension. - Use
export type { X }for types andexport { X }for runtime values. - Use errors from
src/domain/errors/AIErrors.tswhere applicable. - Do not hand-edit generated registries. Run the relevant generator.
- Keep changes focused and validate them with the commands at the end.
The mental model
Application
-> named Connector (credentials + service identity)
-> Agent (model + instructions + tools + context)
-> provider implementation
AgentContextNextGen
-> context plugins (state, memory, catalog, workspace)
-> one ToolManager shared by agent and context
-> compaction and persistence
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed ed3d2eb7a78c
- 4d ago First seen · 493 lines · 4,114 tokens per session scan A 9c3e5bd2fa12
oneringai AGENTS.md is an instructions file published in the GitHub repository aantich/oneringai (73 stars, last pushed 3d ago), licensed MIT. It adds 4,114 tokens to every session, about $0.0206 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
AutoAgents AGENTS.md
Instructions for liquidos-ai/AutoAgents, covering autoagents repository guidelines, project structure & module organization, code quality, naming conventions and error handling.
playbooks CLAUDE.md
Instructions for playbooks-ai/playbooks, covering claude.md, project overview, core architecture, framework components and key architectural insights.
deliberation CLAUDE.md
Claude Code instructions for antonbabenko/deliberation, covering claude.md, what this is, development commands, test plugin locally (loads from working directory) and run setup to test installation flow.
llm-cli-gateway CLAUDE.md
Claude Code instructions for verivus-oss/llm-cli-gateway, covering claude.md, repository overview, development commands, build and test and run all tests.
grok-mcp-server AGENTS.md
Instructions for djtelicloud/grok-mcp-server: cloning this repository and running the commands in README.md to use UniGrok Core on :4765. Every tracked file must support that contract or public contribution/verification of it.
ThreadShelf AGENTS.md
Instructions for ChrystianSchutz/ThreadShelf, covering agents.md, what this project is, repository layout, commands and conventions.