Borrowing it
Nothing to install: this file belongs to omnigentx/jarvis. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omnigentx/jarvis/main/AGENTS.mdgit clone --depth 1 https://github.com/omnigentx/jarvisWrote 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/omnigentx/jarvis/agents-md)<a href="https://agentmods.dev/instructions/omnigentx/jarvis/agents-md"><img src="https://agentmods.dev/badge/instructions/omnigentx/jarvis/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/omnigentx/jarvis/agents-md"><img src="https://agentmods.dev/badge/instructions/omnigentx/jarvis/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.08157 | $0.08157 |
| Opus 5.5 | $0.03263 | $0.03263 |
| Sonnet 5.5 | $0.01631 | $0.01631 |
| Haiku 4.5 | $0.00816 | $0.00816 |
Grade A, and why
jarvis AGENTS.md scanned grade A with 1 finding 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 1mo ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Dynamic** (runtime, no restart): create the row via `POST /api/agents` (dashboard or curl) or call the `spawn_agent` MCP tool from within Jarvis. The definition lands in the SQLite `agent_definitions` table; `service How it starts
The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jarvis AI Assistant — Repository Guidelines
- Repo: Open-source monorepo (omnigentx/jarvis)
- Core runtime: fast-agent (Python, git submodule at
backend/fast-agent) - Always reference files repo-root relative (e.g.
backend/routes/chat.py:187); never absolute paths.
Core Principles
-
Realtime monitoring — SSE, WebSocket, or equivalent push protocol. Absolutely no polling. All agent status, tool execution progress, and activity events must stream to clients in real-time via
services/activity_stream.py(ActivityStreamManager) andservices/sse_progress.py(ProgressManager). -
Production-ready code — Every feature must handle edge cases: connection drops, auth failures, SSE reconnection with exponential backoff, timeout handling, graceful degradation. No TODO placeholders or "happy path only" implementations.
-
Clarification before implementation — When requirements are ambiguous, always present specific questions with options to the user before writing code. Prefer numbered questions that are actionable.
-
Clean architecture — Code must be modular, well-separated, and easy to extend:
- Keep files under ~500 LOC; split when exceeding.
- Extract reusable composables/services; avoid copy-paste.
- Use clear naming that reflects domain concepts (not generic names).
- Every component should have a single responsibility.
-
fast-agent best practices — This project is built on fast-agent. Always:
- Reference official docs before implementing agent features: Tool Runner, Prompting, Instructions
- Use
ToolRunnerHooksfor monitoring and progress tracking (seeservices/spawn_progress_bridge.pyfor the pattern). - Persist runtime-created agents through
services/agent_definitions.py(SQLite). Replaces the legacy file-based agent cards. - Understand the session/history model via
services/session_service.py. - Never bypass fast-agent's built-in capabilities; extend through hooks, not monkey-patching.
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.
- 1mo ago First seen · 561 lines · 8,157 tokens per session scan A 58f09af31fe6
jarvis AGENTS.md is an instructions file published in the GitHub repository omnigentx/jarvis (39 stars, last pushed 4d ago), licensed MIT. It adds 8,157 tokens to every session, about $0.0326 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.