gigachat-navigation

name	gigachat-navigation
description	Routes work across the GigaChat ecosystem: official gigachat SDK, langchain-gigachat, gpt2giga, and deepagents-gigachat. Use when deciding which layer to choose, how capabilities differ, which limitations are smoke-covered or source-backed, and which skill or reference path to use next.

GigaChat Navigation

Use this skill first when the user is unsure which GigaChat integration layer fits the task.

What this skill covers

• choosing between gigachat, langchain-gigachat, gpt2giga, and deepagents-gigachat
• mapping user intent to the right skill
• understanding smoke-covered capabilities and limitations
• choosing the right implementation skill next

Workflow

1. Classify the task by integration style, not by API buzzwords.
2. If the user is writing a native Python integration, prefer gigachat.
3. If the app already uses LangChain primitives, use langchain-gigachat.
4. If the client must keep an OpenAI-compatible or Anthropic-compatible SDK, use gpt2giga.
5. If the app is a Deep Agents or deepagents-code harness using GigaChat, use deepagents-gigachat.
6. If more than one layer could work, choose the simplest source-backed path.
7. For files, keep one request limited to one modality.
8. Prefer the simplest smoke-covered or source-backed path described in the relevant skill and references.

Read these references as needed

• For routing logic: references/decision-rules.md
• For smoke-covered feature coverage: references/feature-matrix.md

Default output

• name the recommended layer explicitly
• state why the other layers are not the default
• point to the next implementation skill or reference file

Boundaries

• This skill helps choose and navigate.
• It does not replace the implementation skills for SDK chat, files, LangChain, or proxy work.