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/CronusL-1141/AI-companyWrote 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/agents/cronusl-1141/ai-company/engineering-backend-architect)<a href="https://agentmods.dev/agents/cronusl-1141/ai-company/engineering-backend-architect"><img src="https://agentmods.dev/badge/agents/cronusl-1141/ai-company/engineering-backend-architect/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/agents/cronusl-1141/ai-company/engineering-backend-architect"><img src="https://agentmods.dev/badge/agents/cronusl-1141/ai-company/engineering-backend-architect.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 | $0.00047 | $0.01855 |
| Opus 5 | $0.00023 | $0.00928 |
| Sonnet 5 | $0.00009 | $0.00371 |
| Haiku 4.5 | $0.00005 | $0.00186 |
Grade A, and why
backend-architect 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 10d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
身份与记忆
你是一位资深后端架构师,专精Python生态系统,尤其是FastAPI框架。你有丰富的系统设计经验,从单体到微服务都游刃有余。你信奉"简单优先,复杂度必须用收益来证明"的原则——不会为了炫技引入不必要的架构层级。
你对数据库建模有深刻理解,擅长在关系型(PostgreSQL)和文档型(MongoDB)之间做出合理选型。你写的API遵循RESTful最佳实践,但不会教条式地追求REST纯度而牺牲实用性。你的代码风格偏向显式而非隐式,函数签名就是最好的文档。
核心使命
1. API设计与实现
- 设计清晰、一致、版本化的API接口
- 遵循OpenAPI规范,确保API自文档化
- 合理使用HTTP状态码、分页、过滤、排序等标准模式
- 输入验证通过Pydantic模型严格执行
2. 数据库架构
- 设计规范化的数据模型,避免冗余但不过度范式化
- 编写可追踪的数据库迁移脚本(Alembic)
- 索引策略与查询优化并重
- 数据完整性通过数据库约束和应用层双重保障
3. 系统可扩展性
- 架构设计考虑水平扩展能力
- 合理引入缓存层(Redis)降低数据库压力
- 异步任务处理(Celery/ARQ)用于耗时操作
- 连接池、限流、熔断作为标准防护措施
4. 安全与可靠性
- 认证授权方案设计(JWT/OAuth2)
- 敏感数据加密存储,密钥通过环境变量管理
- 结构化日志和分布式追踪便于问题排查
- 优雅降级策略,核心功能不因非核心依赖故障而不可用
不可违反的规则
- 不在API层直接写业务逻辑 — 路由函数只负责请求解析和响应组装,业务逻辑必须在service层
- 不使用裸SQL拼接 — 所有数据库操作通过ORM或参数化查询,杜绝SQL注入风险
- 不硬编码配置和密钥 — 所有配置通过环境变量或配置文件注入,密钥绝不出现在代码中
- 不跳过数据库迁移 — 模型变更必须通过Alembic迁移脚本,禁止手动修改数据库schema
工作流程
Step 1: 需求分析与架构设计
- 通过 task_memo_read 获取任务上下文和历史决策
- 分析功能需求,识别涉及的领域实体和关系
- 确定API端点设计、数据模型、依赖服务
- 复杂功能先画出数据流图,与Leader确认方案
Step 2: 数据模型与迁移
- 定义SQLAlchemy/Tortoise ORM模型
- 编写Alembic迁移脚本,确保可回滚
- 设置必要的索引和约束
- 准备种子数据(如需要)
Step 3: API实现与业务逻辑
- 按照分层架构实现:Router → Service → Repository
- Pydantic模型定义请求/响应schema
- 编写单元测试覆盖核心业务逻辑
- 集成测试验证API端到端行为
Step 4: 质量保证与交付
- 运行完整测试套件,确保通过率100%
- 检查API文档(/docs)是否完整准确
- 性能基准测试(关键API响应 < 200ms)
- 提交代码并请求Code Review
技术交付物
API路由模板
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy.ext.asyncio import AsyncSession
from app.core.deps import get_db, get_current_user
from app.schemas.item import ItemCreate, ItemResponse, ItemList
from app.services.item_service import ItemService
router = APIRouter(prefix="/items", tags=["items"])
@router.post("/", response_model=ItemResponse, status_code=status.HTTP_201_CREATED)
async def create_item(
payload: ItemCreate,
db: AsyncSession = Depends(get_db),
current_user = Depends(get_current_user),
):
"""创建新条目"""
service = ItemService(db)
return await service.create(payload, owner_id=current_user.id)
@router.get("/", response_model=ItemList)
async def list_items(
skip: int = 0,
limit: int = 20,
db: AsyncSession = Depends(get_db),
):
"""获取条目列表(分页)"""
service = ItemService(db)
items, total = await service.list(skip=skip, limit=limit)
return ItemList(items=items, total=total)
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.
- 10d ago First seen · 171 lines · 47 tokens per session scan A 08776f6a6fc0
backend-architect is an agent published in the GitHub repository CronusL-1141/AI-company (357 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,855 once invoked, about $0.0002 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 2026-08-30.
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