Agent Skills
› overmind-core/overmind
› backend-architecture
backend-architecture
GitHub提供overbae后端架构全景图,涵盖模块布局、核心设计模式及Celery/Modal等关键组件参考。用于快速导航不熟悉的后端子系统,或跨模块集成业务逻辑与数据流。
触发场景
需要了解后端整体模块结构
在陌生子系统中进行代码导航
需要跨子系统协调行为或集成
安装
npx skills add overmind-core/overmind --skill backend-architecture -g -y
SKILL.md
Frontmatter
{
"name": "backend-architecture",
"description": "Map of the overbae backend — module layout, the patterns every subsystem shares, and reference files for Celery topology, tracing and scoring, evaluation, capabilities and sync, auth and billing, training, serving and data-first workflows. Use when navigating an unfamiliar backend subsystem or wiring behavior across subsystems."
}
overbae backend map
Single Django app overbae, project-scoped tenancy.
Layout
api/— DRF views and serializers, one module per surface (eval_views.py,datasets.py,optimizer.py,otlp.py,billing.py,mcp.py, …).views.pyandserializers.pyare legacy monoliths; a new surface gets its own module. The global exception handler (api/exception_handler.py) flattens list errors to{detail}, addscode, mapsIntegrityErrorto 409conflict, and returns{detail, code: internal_error, error_id}for unhandled errors.models/— split by domain (iam.py,capabilities.py,traces.py,innermind.py,finetuning.py,datasets.py,dataset_pipeline.py,operations.py,billing.py, …).innermind/— the eval matrix evaluators, live span and trajectory scoring, deterministic checks, evaluation runs, the Default eval set, health and signals.services/— business logic. Largest subtrees:datasets/(Data Workshop; data-workshop skill),mcp/(mcp skill),connectors/,sft_assets/(training code baked into the Modal image),codebase/.tasks/— Celery tasks, roughly one module per feature;utils/task_lock.pyfor locking.modal/— Modal app entrypoints (vLLM serving, SFT, native decision evaluation and serving) andmodels.json. Code shared by those workers and the API lives in the rootmodal_shared/package.management/commands/—seed_demo(seed-demo-data skill),grant_credits,check_workshop_transition,sync_baseten_finetuning_costs.
Patterns every subsystem follows
- One domain service, three surfaces. REST, MCP and the Console call the same service. Never copy business logic into a view or an MCP adapter.
- Request keys. An operation that starts work takes a project-scoped
request_key. An identical retry returns the original record; a changed payload under the same key conflicts. - Durable admission. Waiting work lives in PostgreSQL rows, and a locked dispatcher admits it with per-project fairness. Do not park waiting work in the broker.
- Unknown provider outcomes are reconciled, never resubmitted. Intent is journaled before a provider call; a lost acknowledgement becomes
submission_unknownand is resolved from the saved call ID. - Measured, not invented. Missing usage, progress, scores or telemetry stay unknown or null. Never fill a percentage, a zero score, an ETA or a cost from elapsed time or a guess.
- Status writes use
.filter().update(). No Django signals fire; reload the row and wire side effects at the call site. - Model IDs: platform models (judges, generation, Jev) live in
core/model_registry.py; the training and serving catalog lives inmodal/models.json, read throughmodal/model_registry.py. Jev decisions go throughcore/decisions.py. - Modal code ships separately. Changes under
overbae/modal/,modal_shared/orservices/sft_assets/reach running jobs only aftermodal deploy; training and serving pin immutable releases.
Reference files
Read the one that matches the task:
| File | Covers |
|---|---|
| references/celery.md | Workers, queues, routing, admission, deployment order |
| references/tracing-and-scoring.md | Span model, OTLP ingest, span cost, live scoring, signals, Jev |
| references/evaluation.md | Evaluation runs, comparison, judges, context estimates, benchmark selection |
| references/capabilities.md | The agent graph, scans, toml sync, Default eval set, provenance |
| references/auth-and-billing.md | Clerk and local auth, guests, API keys, billing, Overwatch internal endpoints |
| references/training.md | Preparation, launch, monitoring, transfer, native decision training |
| references/serving.md | Deployments, shared-base LoRA, activation, inference requests, operational receipts |
| references/data-first.md | Training plans, partitions, native evaluation, experiments, performance runs |
版本历史
-
fe6ddf7
当前 2026-11-02 09:22
精简了目录路径前缀,更新了services和innermind模块的具体功能描述,新增了关于请求键、持久化准入及状态写入的详细架构规范。
- e146ecd 2026-10-18 15:41


