Navigate GigaChat Ecosystem
Routing skill that decides which GigaChat integration layer fits a task - raw SDK, LangChain, or the OpenAI-compatible gpt2giga proxy.
Why it matters
Seamlessly navigate the GigaChat ecosystem, connecting SDKs, LangChain, and gpt2giga for enhanced AI interactions.
Outcomes
What it gets done
Route requests between GigaChat SDK and LangChain.
Facilitate interaction with gpt2giga.
Enable efficient ecosystem navigation.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/gigachat-navigation | bash Overview
GigaChat Navigation
Routes a task to the right layer of the GigaChat ecosystem - the raw gigachat SDK, langchain-gigachat, or the OpenAI-compatible gpt2giga proxy - and hands off to the matching implementation skill. Use it first whenever it's unclear which GigaChat integration layer fits a task. It doesn't implement anything itself; use the SDK chat/files/functions skills, langchain-gigachat, or gpt2giga afterward for the actual work.
What it does
GigaChat Navigation is a routing skill for the GigaChat ecosystem: it decides whether a task should be built on the official gigachat Python SDK, on langchain-gigachat, or on gpt2giga (the OpenAI-compatible proxy), based on the integration style the task actually needs rather than which API buzzwords appear in the request. It also tracks which capabilities and limitations across these three layers are actually verified, and points to the right next skill or reference file once a layer is chosen.
When to use - and when NOT to
Use it first, before any implementation work, whenever it's unclear which GigaChat integration layer fits the task at hand. It is explicitly a decision and hand-off point, not an implementation skill itself - it does not replace the dedicated skills for SDK chat, SDK files/functions, LangChain usage, or proxy work, and should always route into one of them rather than trying to do the implementation directly.
Inputs and outputs
The routing logic is a short decision tree: if the user is writing a native Python integration, prefer gigachat; if the application already uses LangChain primitives, use langchain-gigachat; if the client needs to keep an OpenAI-compatible or Anthropic-compatible SDK unchanged, use gpt2giga. When more than one layer could technically work, the guidance is to pick the simplest path that's been locally verified rather than the most feature-complete one in theory. One cross-cutting constraint carried over into every layer: file-based requests should stay limited to a single modality per request. Two reference files back the routing: references/decision-rules.md for the routing logic itself, and references/feature-matrix.md for which features are verified as working on each layer.
Integrations
This skill is the entry point to a small family of GigaChat-focused skills - the gigachat-sdk-* skills for direct SDK usage (chat, files, embeddings, functions), langchain-gigachat for LangChain-based chains and RAG, and gpt2giga for OpenAI-compatible proxying - and its main job is naming which of those to use next and stating explicitly why the others aren't the default for this particular task.
Who it's for
Developers or agents starting a new GigaChat integration who aren't sure yet whether to reach for the raw SDK, a LangChain wrapper, or an OpenAI-compatible proxy, and want a quick, verified answer before committing to an implementation path.
Source README
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, andgpt2giga - mapping user intent to the right skill
- understanding verified 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 more than one layer could work, choose the simplest locally verified path.
- For files, keep one request limited to one modality.
- Prefer the simplest verified path described in the relevant skill and references.
Read these references as needed
- For routing logic:
references/decision-rules.md - For verified 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.
FAQ
Common questions
Discussion
Questions & comments · 0
Sign In Sign in to leave a comment.