Nexent is a zero-code platform for creating AI agents from natural-language instructions, combining tools, skills, memory, orchestration, constraints, feedback loops, and control planes. It is intended for people who need to build and deploy agents without writing orchestration code. The catalogue entries are rules and skills for working with Nexent.
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.
git clone --depth 1 https://github.com/ModelEngine-Group/nexentWrote 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/rules/modelengine-group/nexent/pytest_unit_test_rules)<a href="https://agentmods.dev/rules/modelengine-group/nexent/pytest_unit_test_rules"><img src="https://agentmods.dev/badge/rules/modelengine-group/nexent/pytest_unit_test_rules/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/rules/modelengine-group/nexent/pytest_unit_test_rules"><img src="https://agentmods.dev/badge/rules/modelengine-group/nexent/pytest_unit_test_rules.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 | $0.00000 | $0.00099 |
| Opus 5.5 | $0.00000 | $0.00040 |
| Sonnet 5.5 | $0.00000 | $0.00020 |
| Haiku 4.5 | $0.00000 | $0.00010 |
Grade A, and why
pytest_unit_test_rules 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 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.
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.
What it actually says
Shared project rules
Read .agents/skills/nexent-python-tests/SKILL.md before working in this rule scope.
Paths are relative to the repository root. If linked context is unavailable, read the file directly. Load only applicable references. Maintain the linked Markdown source instead of duplicating rules here.
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 Changed · -41 lines 2c4a4a290bf9
- 1mo ago First seen · 52 lines · 0 tokens per session scan A f51c66663f80
pytest_unit_test_rules is a cursor rule published in the GitHub repository ModelEngine-Group/nexent (5,955 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 99 tokens. 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 cursor rules, from other repositories
testing
Testing conventions and patterns for the Laragentic package.
testing
Testing rules for vitest suite.
ai-actions-test-data
Each action defined in actions.json must have a corresponding server/testdata/ .json test fixture. A missing server/testdata/ directory or any missing fixture is a blocking issue.
latest-ai-models
Use only the latest AI models from all providers (updated Feb 2026).
callback-system
Lifecycle callback system design for agentic loops.
react-loop
ReAct loop implementation design and pattern details.