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/sync-pr.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/sync-pr)<a href="https://agentmods.dev/commands/modelengine-group/fit-framework/sync-pr"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/sync-pr/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/sync-pr"><img src="https://agentmods.dev/badge/commands/modelengine-group/fit-framework/sync-pr.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 | $0.02589 |
| Opus 5.5 | $0.00005 | $0.01036 |
| Sonnet 5.5 | $0.00003 | $0.00518 |
| Haiku 4.5 | $0.00001 | $0.00259 |
Grade A, and why
sync-pr 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sync PR Command
功能说明
将任务的处理进度摘要同步到对应的 Pull Request 评论区中,方便审查者了解开发进展和关键决策。
执行流程
1. 验证任务存在
检查任务文件是否存在:
- 查找
.ai-workspace/active/{task-id}/task.md - 如果不存在,检查
completed/和blocked/目录 - 如果都不存在,提示用户任务不存在
2. 读取任务信息
从任务文件中获取:
- PR 号码(
pr_number字段) - 任务标题和描述
- 当前步骤(
current_step) - 任务状态(
status) - 创建和更新时间
- 关联的 Issue(
issue_number,如果有)
3. 读取上下文文件
注意:{task-id} 格式为 TASK-{yyyyMMdd-HHmmss},例如 TASK-20260205-202013
检查并读取以下文件(如果存在):
.ai-workspace/{status}/{task-id}/analysis.md- 需求分析.ai-workspace/{status}/{task-id}/plan.md- 技术方案.ai-workspace/{status}/{task-id}/implementation.md- 实现报告.ai-workspace/{status}/{task-id}/review.md- 审查报告
4. 生成进度摘要
根据当前状态生成清晰的进度摘要:
基本格式:
## 🤖 开发进度更新
**任务ID**: {task-id}
**更新时间**: {当前时间}
**当前状态**: {状态描述}
### ✅ 已完成
- [x] 需求分析 - {完成时间}
- {核心要点摘要 1-2 条}
- [x] 技术方案设计 - {完成时间}
- {方案选择和关键决策 1-2 条}
- [x] 代码实现 - {完成时间}
- 修改文件: {数量}
- 新增测试: {数量}
- [ ] 代码审查(进行中)
- [ ] 最终合并
### 📋 当前进展
{当前步骤的详细说明}
### 🎯 下一步
{下一步计划}
### 📊 技术要点
{关键的技术决策和实现细节,方便审查者理解}
### 📂 相关文档
- 任务文件: `.ai-workspace/active/{task-id}/task.md`
- 需求分析: `.ai-workspace/active/{task-id}/analysis.md`
- 技术方案: `.ai-workspace/active/{task-id}/plan.md`
- 实现报告: `.ai-workspace/active/{task-id}/implementation.md`
---
*由 Claude Code 自动生成 - [任务管理系统](../.agents/README.md)*
摘要原则:
- 面向审查者:突出技术决策和实现要点
- 简洁清晰:每个阶段只提取核心要点
- 逻辑连贯:按开发流程展示进展
- 便于审查:说明关键变更的原因和影响
5. 同步到 PR
使用 gh 命令将摘要发布到 PR 评论:
gh pr comment {pr-number} --body "$(cat <<'EOF'
{生成的进度摘要}
EOF
)"
6. 更新任务状态
在任务文件中记录同步时间:
- 添加或更新
last_synced_to_pr_at字段 - 记录同步的 PR 评论链接(如果 gh 返回)
7. 告知用户
输出格式:
✅ 任务进度已同步到 PR #{pr-number}
**同步内容**:
- 已完成步骤: {数量}
- 当前状态: {状态}
- 下一步: {下一步说明}
**查看链接**:
https://github.com/{owner}/{repo}/pull/{pr-number}
参数说明
<task-id>: 任务ID,格式为 TASK-{yyyyMMdd-HHmmss}(必需)
使用示例
# 同步任务进度到对应的 PR
/sync-pr TASK-20251227-104654
进度摘要示例
示例 1:代码实现完成,等待审查
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 · 319 lines · 13 tokens per session scan A b397fc197649
sync-pr 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 2,589 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.
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.
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.