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/modelengine-group/nexent/agents-mdgit clone --depth 1 https://github.com/ModelEngine-Group/nexentWhat 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 | $0.01369 | $0.01369 |
| Opus 5 | $0.00685 | $0.00685 |
| Sonnet 5 | $0.00274 | $0.00274 |
| Haiku 4.5 | $0.00137 | $0.00137 |
Grade A, and why
nexent 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 yesterday.
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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS
<skills_system priority="1">
Available Skills
How to use skills:
- Invoke:
npx openskills read <skill-name>(run in your shell)- For multiple:
npx openskills read skill-one,skill-two
- For multiple:
- The skill content will load with detailed instructions on how to complete the task
- Base directory provided in output for resolving bundled resources (references/, scripts/, assets/)
Usage notes:
- Only use skills listed in <available_skills> below
- Do not invoke a skill that is already loaded in your context
- Each skill invocation is stateless
<available_skills>
</available_skills>
</skills_system>
Project Overview
Nexent is a zero-code platform for auto-generating AI agents. Monorepo with:
backend/- FastAPI HTTP APIsdk/nexent/- Core agent framework (pip package)frontend/- Next.js web UIdocker/&k8s/- Deployment configs
Developer Commands
Backend (Python 3.11)
# Setup
cd backend && uv sync --extra data-process --extra test
# Install SDK for development
cd backend && uv pip install -e "../sdk[dev]"
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.
- yesterday First seen · 181 lines · 1,369 tokens per session scan A 9b5bc75c3def
nexent AGENTS.md is an instructions file published in the GitHub repository ModelEngine-Group/nexent (5,841 stars, last pushed 3d ago), licensed MIT. It adds 1,369 tokens to every session, about $0.0068 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
turbo-flow CLAUDE.md
Claude Code instructions for marcuspat/turbo-flow, covering claude.md — turbo flow, what this repo is, key files and conventions.
claude-code-safety-net CLAUDE.md
Instructions for waleedkhanbaloch/claude-code-safety-net, covering claude.md, project overview, commands, architecture and code style (typescript).
claude-code-safety-net AGENTS.md
Instructions for waleedkhanbaloch/claude-code-safety-net, covering agent guidelines, commands, pre-commit hooks, commit conventions and code style (typescript).
joplin-server-vector-memory GEMINI.md
Gemini CLI instructions for adamoutler/joplin-server-vector-memory, covering joplin server vector memory mcp - gemini methodology, 1. code discovery & context gathering, 2. refactoring & code quality, 3. execution & testing and 4. architectural rules.
ai-agents-for-beginners AGENTS.md
Instructions for microsoft/ai-agents-for-beginners, covering agents.md, project overview, setup commands, prerequisites and initial setup.
deer-flow copilot-instructions.md
Instructions for bytedance/deer-flow, covering copilot onboarding instructions for deerflow, 1) repository summary, 2) runtime and toolchain requirements, 3) build/test/lint/run - verified command sequences and a. bootstrap and install.