GigaChat Skills
Eight skills for coding against the GigaChat API: choosing a layer, access setup, SDK, LangChain, gpt2giga and Deep Agents
- 4 ★ on the repository
- Last commit
- MIT
Extended access
Why this level
- The verification folder holds smoke scripts the agent can run
- With --live the scripts call the GigaChat API with your credentials
Install
- Works in
- Services
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/gigachat-skillsDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- The verification folder holds smoke scripts the agent can run
- With --live the scripts call the GigaChat API with your credentials
This is third-party code. Review the repository files before installing.
What it does
A set of portable folders with SKILL.md and references that teaches an agent to build GigaChat integrations. gigachat-navigation helps choose between the official SDK, langchain-gigachat, the gpt2giga proxy and deepagents-gigachat. The other skills cover credentials, OAuth scopes and TLS certificates, chat and streaming, function calling, files and embeddings, LangChain RAG and OpenAI client compatibility.
Who it is for. For Python developers connecting GigaChat to their applications or agents.
Good fit when
- You need to pick a GigaChat integration approach
- You need to set up certificates and OAuth scopes for the GigaChat API
- You want an existing OpenAI client to talk to GigaChat via gpt2giga
- You need RAG or tool calling with langchain-gigachat
Not a fit when
- The project does not use GigaChat
Example request
Write a Python service that answers questions over a document base using langchain-gigachatLimitations
There is no universal install path; copy the folders into your agent's skills directory or adapt them to its instruction format. The verification scripts are smoke tests, not a full benchmark. File requests must not mix modalities in one call.
How to disable. Remove the copied skill folders from your agent's directory.
FAQ
Which skill should I start with?
gigachat-navigation to choose a layer, then gigachat-setup for credentials and certificates.
What do the confidence labels mean?
smoke-covered is checked by a script, source-backed is supported by docs or code, caution and inference mark risky or inferred advice.
Discussion
No questions or reviews yet.
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README in short
The README describes tool-agnostic skills for the GigaChat API and GigaChain. Each folder holds a SKILL.md and on-demand references. The verification folder has smoke scripts that skip live calls by default and run with --live. It notes that gpt2giga is a compatibility layer, not a byte-for-byte OpenAI API replacement.
README badge
Original SKILL.md
--- 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.