FIT is an enterprise AI development framework with a multilingual function engine, a flow orchestration engine, and Java tools for connecting language models, knowledge bases, and external tools. It is for Java-oriented teams building AI applications that can run locally or in distributed deployments with plugins. The catalogue add-ons provide commands, skills, instructions, and settings for working with FIT.
Borrowing it
Nothing to install: this file belongs to ModelEngine-Group/fit-framework. 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/ModelEngine-Group/fit-framework/main/.codex/commands/fit-test.mdgit clone --depth 1 https://github.com/ModelEngine-Group/fit-frameworkWrote 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/commands/modelengine-group/fit-framework/fit-test)<a href="https://agentmods.dev/commands/modelengine-group/fit-framework/fit-test"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/fit-test.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 5.1 | $0.00005 | $0.00519 |
| Opus 5 | $0.00003 | $0.00260 |
| Sonnet 5 | $0.00001 | $0.00104 |
| Haiku 4.5 | $0.00001 | $0.00052 |
Grade A, and why
fit-test 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 3d 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
Test Command
使用前：自动识别仓库
命令会默认使用当前工作目录所在的 Git 仓库作为目标，无需传入仓库参数。若当前目录不在 Git 仓库内，请先 cd 到目标仓库根目录后再执行。
文中所有路径示例默认以仓库根目录为基准。
功能说明
执行完整的测试流程，包括单元测试、构建验证和集成测试。
用法：
/test- 执行完整测试流程
执行方式：
使用自动化测试脚本执行完整的测试流程：
./.agents/scripts/run-test.sh
测试流程包括：
- 清理构建产物 - 删除之前的 build 目录
- 执行单元测试和构建 - 运行
mvn clean install执行全量单元测试 - 创建动态插件目录 - 创建
dynamic-plugins目录 - 启动 FIT 服务 - 使用
build/bin/fit start启动服务 - 验证健康检查接口 - 访问
/actuator/plugins接口 - 验证 Swagger 文档 - 访问
/openapi.html页面 - 清理测试环境 - 停止服务并删除构建产物
测试报告：
脚本会自动生成测试报告，包含：
- ✅/✗ 单元测试结果
- ✅/✗ 构建状态
- ✅/✗ FIT 服务启动状态
- ✅/✗ 健康检查接口响应
- ✅/✗ Swagger 文档页面可访问性
下一步: 测试通过后，使用以下命令提交代码:
- Claude Code / OpenCode: /commit
- Gemini CLI: /fit:commit
- Codex CLI: /prompts:fit-commit
注意事项：
- 端口冲突：确保 8080 端口未被占用
- 权限配置：如需自动授权，请在本地 Codex 配置中放行相关命令
- 完全自动化：整个测试流程无需手动确认，自动执行所有步骤
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.
- 3d ago First seen · 58 lines · 5 tokens per session scan A c3459254fed9
fit-test is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,115 stars, last pushed 5mo ago), licensed MIT. It adds 5 tokens to every session and 519 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
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run-all-tests-and-fix
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create-python-tests
Command "create-python-tests" from codeready-toolchain/tarsy, covering writing tests for python llm service, running tests, from project root, from llm-service/ directory and critical rules.
create-dashboard-tests
You'll typically use this after implementing new dashboard functionality. Create tests for the complex logic you just added.
create-backend-tests
Command "create-backend-tests" from codeready-toolchain/tarsy, covering writing tests for go backend, running tests, from project root, direct go commands and critical rules.
sdlc-test
Generate and run multi-tiered enterprise test suites (Unit, Integration, E2E, Contract, Load/k6, Chaos, and Fuzz testing).