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/.claude/commands/review-task.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/review-task)<a href="https://agentmods.dev/commands/modelengine-group/fit-framework/review-task"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/review-task/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/commands/modelengine-group/fit-framework/review-task"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/review-task.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 | $0.00013 | $0.02657 |
| Opus 5.5 | $0.00005 | $0.01063 |
| Sonnet 5.5 | $0.00003 | $0.00531 |
| Haiku 4.5 | $0.00001 | $0.00266 |
Grade A, and why
review-task 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Task Command
功能说明
审查任务的代码实现,检查代码质量、规范合规性、测试覆盖等,输出审查报告。
⚠️ CRITICAL: 状态更新要求
执行此命令后,你必须立即更新任务状态。参见规则 7。
执行流程
1. 验证前置条件
检查必需文件:
.ai-workspace/active/{task-id}/task.md- 任务文件.ai-workspace/active/{task-id}/implementation.md- 实现报告
注意:{task-id} 格式为 TASK-{yyyyMMdd-HHmmss},例如 TASK-20260205-202013
如果任一文件不存在,提示用户先完成前置步骤。
2. 读取实现报告
仔细阅读 implementation.md,了解:
- 已修改的文件列表
- 实现的关键功能
- 测试情况
- 实现者标注的需要关注的点
3. 执行代码审查
按照 .agents/workflows/feature-development.yaml 中的 code-review 步骤:
必须审查的内容:
- 代码质量和编码规范(遵循 CLAUDE.md)
- Bug 和潜在问题检测
- 测试覆盖率和测试质量
- 错误处理和边界情况
- 性能和安全问题
- 代码注释和文档
- 与技术方案的一致性
审查原则:
- 严格但公正:指出问题,但也认可优点
- 具体明确:给出具体的文件和行号
- 提供建议:不仅指出问题,还要提供改进建议
- 分级处理:区分必须修复和建议优化
4. 调用专业审查工具(可选)
如果需要更深度的审查,可以调用:
方案1:快速审查(推荐用于日常 PR)
/code-review:code-review <pr-number>
- 5个并行 Sonnet 代理
- CLAUDE.md 规范合规性
- Bug 检测与历史上下文分析
方案2:深度审查(推荐用于重要功能)
/pr-review-toolkit:review-pr
- 6个专业审查代理
- 代码注释准确性、测试覆盖、错误处理、类型设计等多维度审查
5. 输出审查报告
创建 .ai-workspace/active/{task-id}/review.md,必须包含以下章节:
# 代码审查报告
## 审查概要
- **审查者**: {审查者}
- **审查时间**: {时间}
- **审查范围**: {文件数量和主要模块}
- **总体评价**: {通过/需要修改/不通过}
## 审查发现
### 🔴 必须修复(Blocker)
#### 1. {问题标题}
**文件**: `{file-path}:{line-number}`
**问题描述**: {详细描述}
**修复建议**: {具体建议}
**严重程度**: 高
### 🟡 建议修改(Major)
#### 1. {问题标题}
**文件**: `{file-path}:{line-number}`
**问题描述**: {详细描述}
**修复建议**: {具体建议}
**严重程度**: 中
### 🟢 优化建议(Minor)
#### 1. {优化点}
**文件**: `{file-path}:{line-number}`
**建议**: {优化建议}
## 优点与亮点
- {做得好的地方1}
- {做得好的地方2}
## 规范检查
### CLAUDE.md 合规性
- [ ] 编码规范
- [ ] 命名规范
- [ ] 注释规范
- [ ] 测试规范
### 代码质量指标
- 圈复杂度: {评估}
- 代码重复: {评估}
- 测试覆盖率: {百分比}
## 测试审查
### 测试覆盖
- 单元测试: {评价}
- 边界情况: {是否覆盖}
- 异常情况: {是否覆盖}
### 测试质量
- 测试命名: {评价}
- 断言充分性: {评价}
- 测试独立性: {评价}
## 安全审查
- SQL 注入风险: {检查结果}
- XSS 风险: {检查结果}
- 权限控制: {检查结果}
- 敏感信息泄露: {检查结果}
## 性能审查
- 算法复杂度: {评估}
- 数据库查询: {优化建议}
- 资源释放: {检查结果}
## 与方案的一致性
- [ ] 实现符合技术方案
- [ ] 未偏离设计意图
- [ ] 无计划外功能
## 总结与建议
### 是否批准
- [ ] ✅ 批准合并(无阻塞问题)
- [ ] ⚠️ 修改后批准(有建议修改项)
- [ ] ❌ 需要重大修改(有阻塞问题)
### 下一步行动
{根据审查结果给出下一步建议}
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.
- 2mo ago First seen · 346 lines · 13 tokens per session scan A 4a3ea84e2e94
review-task is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,117 stars, last pushed 7mo ago), licensed MIT. It adds 13 tokens to every session and 2,657 once invoked, about $0.0001 per session on Opus 5.5. 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.
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me-code-review
Perform a full thermal-fluid research-code review covering architecture, reproducibility, units, physics, interfaces, tests, and released artifacts.
check-pr
Check the current branch's PR for external review feedback, fix errors and warnings, run a final external review, and push.
review
Run the adversarial critic against existing code. Default: parent reviews inline, zero subagent spawns. --strict: critic subagent + external review.
external-review
Run a single external code review via an external LLM API on the full branch diff vs main. Standalone usage — no aggregation with internal review.
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
ingest-cwe-taxonomies
Ingest CWE XML from MITRE and generate per-domain security audit taxonomies for PromptKit.