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Oma Backend

OrganizationPopular
first-fluke
oma-backend

Implement server APIs, authentication, and application data access. Schema modeling and query tuning use oma-db.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-backend
Stars
1.3K
Forks
149
Bundled files
17
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 17 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Backend AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-backend .claude/skills/oma-backend
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Backend in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oma Backend on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oma Backend is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Backend Agent - API & Server Specialist

Scheduling

Goal

Implement or review backend APIs, authentication, database integration, server-side business logic, and migrations using the project's existing backend stack and clean architecture boundaries.

Intent signature

  • User asks for API, endpoint, REST, GraphQL, auth, server, migration, repository, service, router, or background job work.
  • User needs backend code that coordinates validation, business logic, persistence, transactions, and backing services.

When to use

  • Building REST APIs or GraphQL endpoints
  • Database design and migrations
  • Authentication and authorization
  • Server-side business logic
  • Background jobs and queues

When NOT to use

  • Frontend UI -> use Frontend Agent
  • Mobile-specific code -> use Mobile Agent

Expected inputs

  • Target feature, endpoint, migration, auth flow, or server behavior
  • Existing backend stack files such as manifests, routes, services, models, and database config
  • API contracts, schemas, validation rules, and persistence requirements
  • Required verification commands or project conventions

Expected outputs

  • Backend code changes in router, service, repository, model, migration, or test files
  • Validated inputs, safe queries, transaction boundaries, and error handling
  • Verification results from the execution checklist

Dependencies

  • Project stack manifests and existing backend conventions
  • resources/execution-protocol.md, resources/checklist.md, and resources/orm-reference.md
  • Optional stack/stack.yaml, stack/tech-stack.md, snippets, and API templates
  • Database, queue, cache, mail, auth, or external API resources configured through environment or secret managers

Control-flow features

  • Branches by detected stack, ORM/query pattern, auth requirement, migration impact, and transaction scope
  • Reads and writes codebase files
  • May touch local database migrations or generated code
  • Must not hardcode secrets or share unsafe ORM lifecycle objects across concurrent work

Structural Flow

Entry

  1. Detect the backend stack from project files first.
  2. Identify affected router, service, repository, model, migration, and test boundaries.
  3. Load stack-specific references only when needed.

Scenes

  1. PREPARE: Determine stack, architecture boundaries, and acceptance criteria.
  2. ACQUIRE: Read existing routes, services, repositories, models, schemas, and config.
  3. ACT: Implement backend changes with validation, business logic, persistence, and tests.
  4. VERIFY: Run relevant lint, type, test, migration, and checklist commands.
  5. FINALIZE: Report changed behavior, verification, and unresolved risks.

Transitions

  • If stack files exist, follow them before generic guidance.
  • If ORM performance, relationship loading, transactions, or N+1 risk appears, use resources/orm-reference.md.
  • If database schema impact is primary and API work is secondary, coordinate with oma-db.
  • If auth server setup touches DB adapters or server libraries, keep it in backend scope.

Failure and recovery

  • If stack cannot be determined, ask the user or suggest running /stack-set.
  • If verification fails, fix root cause before handoff.
  • If required secrets or services are unavailable, document the blocker and keep code configurable.

Exit

  • Success: backend change is implemented, tested, and aligned with local architecture.
  • Partial success: blocker, missing dependency, or verification gap is explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Detect stack and conventionsREADManifests, stack files, existing code
Select implementation boundarySELECTRouter/service/repository pattern
Validate inputs and schemasVALIDATEStack validation library
Implement business logicWRITEService layer code
Implement persistenceWRITERepository/model/migration code
Call external/backing servicesCALL_TOOLDB, queue, cache, auth, or API clients
Run verificationCALL_TOOLTests, typecheck, lint, migrations
Report resultNOTIFYFinal summary

Tools and instruments

  • Project language/framework toolchain
  • ORM or database client
  • Test, lint, typecheck, and migration commands
  • Stack-specific templates and snippets when present

Canonical workflow path

bash
rg --files
rg "route|router|service|repository|model|schema|migration" .

Then run the project's discovered verification commands, usually lint/typecheck/tests and migrations when schema changes are involved. Prefer stack/stack.yaml verify: commands when present.

Resource scope

ScopeResource target
CODEBASEBackend source, tests, schemas, migrations
LOCAL_FSStack references and generated artifacts
PROCESSTest, lint, typecheck, migration commands
CREDENTIALSEnvironment-managed DB URLs, API keys, secrets
NETWORKExternal APIs or backing services when required

Preconditions

  • Target behavior and affected backend boundary are identifiable.
  • Project stack and verification commands can be inferred or are provided.
  • Required credentials remain outside source code.

Effects and side effects

  • Mutates backend source files, tests, and possibly migrations.
  • May change database schema, API behavior, auth behavior, or service contracts.
  • May require generated clients or migration artifacts.

Guardrails

Apply framework, library, architecture, and data-model defaults only when the target project has no established choice. Scoped edits do not authorize a stack migration or unrelated infrastructure.

  1. DRY (Don't Repeat Yourself): Business logic in Service, data access logic in Repository
  2. SOLID:
    • Single Responsibility: Classes and functions should have one responsibility
    • Dependency Inversion: Use your framework's DI mechanism
  3. KISS: Keep it simple and clear

Architecture Pattern

Router (HTTP) → Service (Business Logic) → Repository (Data Access) → Models

Repository Layer

  • Encapsulate DB CRUD and query logic
  • No business logic, return ORM entities

Service Layer

  • Business logic, Repository composition, external API calls
  • Business decisions only here

Router Layer

  • Receive HTTP requests, input validation, call Service, return response
  • No business logic, inject Service via DI

Core Rules

  1. Clean architecture: router → service → repository → models
  2. No business logic in route handlers
  3. All inputs validated with your stack's validation library
  4. Parameterized queries only (never string interpolation)
  5. JWT + Argon2id for auth (bcrypt acceptable for legacy compatibility); rate limit auth endpoints
  6. Async where supported; type annotations on all signatures
  7. Custom exceptions via centralized error module (not raw HTTP exceptions)
  8. Explicit ORM loading strategy: do not rely on default relation loading when query shape matters
  9. Explicit transaction boundaries: group one business operation into one request/service-scoped unit of work
  10. Safe ORM lifecycle: do not share mutable ORM session/entity manager/client objects across concurrent work unless the ORM explicitly supports it
  11. Config from environment, with graceful fallback: DB URLs, API keys, secrets, and feature flags come from env vars or secret managers; never hardcode in source. When integrating a third-party API (OpenAI, Anthropic, Stripe, etc.), write BOTH paths: (a) real call when the env key is present, (b) deterministic local fallback when absent, marked with // TODO(oma-deferred): integrate <vendor> when key is provisioned. Fallback-only leaves the spec unmet; real-call-only breaks demos when the key is missing
  12. Stateless services: no in-memory session or user state between requests; use external stores (DB, Redis, cache) for shared state
  13. Backing services as resources: DB, queue, cache, mail are swappable attached resources connected via config; Repository layer must not assume a specific instance

Stack Detection

  1. Project files first: Read existing code, package manifests (pyproject.toml, package.json, Cargo.toml, go.mod, pom.xml, etc.) to determine the tech stack
  2. stack/ second: If stack/ exists, use it as supplementary reference for coding conventions and snippet templates
  3. Neither exists: Ask the user or suggest running /stack-set

Stack-Specific Reference

  • Stack manifest (SSOT): stack/stack.yaml: structured declaration (language, framework, orm) and verify: contract consumed by oma verify backend. Schema: variants/stack.schema.json.
  • Tech stack narrative: stack/tech-stack.md: human-readable reference only; stack.yaml wins on conflict.
  • Code snippets (copy-paste ready): stack/snippets.md
  • API template: stack/api-template.*

References

  • Local code tools: ../_shared/core/code-intelligence.md (code search/navigation)

  • Execution steps (follow for the selected task): resources/execution-protocol.md

  • Checklist (run before handoff): resources/checklist.md

  • ORM reference: resources/orm-reference.md

  • Error recovery: resources/error-playbook.md

  • Context loading: ../_shared/core/context-loading.md

  • Clarification: ../_shared/core/clarification-protocol.md

  • Context budget: ../_shared/core/context-budget.md

  • Lessons learned: ../_shared/core/lessons-learned.md (matching prior failure or requested retrospective)

  • Observability handoff: ../oma-observability/SKILL.md §Integrations — propagators/baggage, span conventions, log correlation, PII redaction

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Oma Backend AI skill do?

Implement server APIs, authentication, and application data access. Schema modeling and query tuning use oma-db.

Why use Oma Backend on TypingMind?

Because you install it once and use it with any model. Oma Backend is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oma Backend in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-backend. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Oma Backend?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oma Backend?

As many as you like. As long as a model supports skills, you can use Oma Backend with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oma Backend AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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