| 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
- Classify the task by integration style, not by API buzzwords.
- If the user is writing a native Python integration, prefer
gigachat.
- If the app already uses LangChain primitives, use
langchain-gigachat.
- If the client must keep an OpenAI-compatible or Anthropic-compatible SDK, use
gpt2giga.
- If the app is a Deep Agents or
deepagents-code harness using GigaChat, use deepagents-gigachat.
- If more than one layer could work, choose the simplest source-backed path.
- For files, keep one request limited to one modality.
- 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.
