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-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/fit-review-task)<a href="https://agentmods.dev/commands/modelengine-group/fit-framework/fit-review-task"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/fit-review-task.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 | $0.00010 | $0.00646 |
| Opus 5 | $0.00005 | $0.00323 |
| Sonnet 5 | $0.00002 | $0.00129 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
fit-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 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
审查任务 $1 的实现代码,输出代码审查报告。
执行以下步骤:
-
验证前置条件: 检查必需文件是否存在:
- .ai-workspace/active/$1/task.md
- .ai-workspace/active/$1/implementation.md 如果任一文件不存在,提示用户先完成前置步骤。
-
读取上下文:
- 读取 task.md 了解任务描述和要求
- 读取 plan.md 了解技术方案
- 读取 implementation.md 了解实现细节
- 查看 git diff 获取实际代码变更
-
执行代码审查: 对照 plan.md 和实际代码变更,检查:
- 功能正确性: 实现是否符合技术方案
- 代码质量: 编码规范、命名、注释、复杂度
- 测试覆盖: 是否有充分的测试用例
- 安全性: SQL注入、XSS、权限控制等
- 性能: 算法复杂度、资源使用
- 边界情况: 空值处理、异常处理
-
输出审查报告: 创建 .ai-workspace/active/$1/review.md,包含:
- 审查概要(审查者、时间、范围、总体评价)
- 审查发现(分级: 🔴 Blocker / 🟡 Major / 🟢 Minor)
- 规范检查(编码规范、测试规范合规性)
- 安全/性能审查结果
- 与方案的一致性
- 总结与建议(是否批准: ✅批准 / ⚠️修改后批准 / ❌需要重大修改)
-
更新任务状态: 使用 Edit 工具更新 .ai-workspace/active/$1/task.md:
- current_step: code-review
- assigned_to: codex
- updated_at: 当前时间
- 标记 review.md 为已完成
- 在"工作流进度"中标记 code-review 为完成 ✅
-
告知用户:
- 输出审查结论(批准/需修改)
- 如果需要修改,提示修复:
- Claude Code / OpenCode: /refine-task $1
- Gemini CLI: /fit:refine-task $1
- Codex CLI: /prompts:fit-refine-task $1
- 如果批准,提示提交代码:
- Claude Code / OpenCode: /commit
- Gemini CLI: /fit:commit
- Codex CLI: /prompts:fit-commit
注意事项:
- 对照 plan.md 审查,确保实现符合设计意图
- 关注潜在的安全和性能问题
- 给出具体的修改建议,而不是模糊的描述
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 · 63 lines · 10 tokens per session scan A bdd2ff75b0a0
fit-review-task is a command published in the GitHub repository ModelEngine-Group/fit-framework (2,115 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 646 once invoked, about $0.0001 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
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.
backlog-status
Display a live backlog status dashboard — active tasks table with PR state and branch-to-test. Optional flag: --qa also generates a QA test guide for all Ready-for-Review tasks.
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
You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.