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Deepseek Provider Contract

Community
AnastasiyaW
deepseek-provider-contract

Validate a proposed DeepSeek API integration before any key or project context is sent: check thinking-mode tool-call history, strict-schema assumptions, bounded output, and provider data boundaries. Use when integrating DeepSeek, adding DeepSeek tool calls or streaming, debugging DeepSeek 400 after a tool call, or evaluating a DeepSeek harness/MCP. Do NOT use for a generic model comparison, ordinary local coding, or to send a repository transcript to a provider by default.

Overview

PublisherAnastasiyaW
Repositorycodex-claude-code-config
Skill namedeepseek-provider-contract
Stars
212
Forks
32
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by AnastasiyaW on GitHub. Read the source before you install it.

Installation

Install the Deepseek Provider Contract 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.
    https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/operational/deepseek-provider-contract
  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/AnastasiyaW/codex-claude-code-config.git /tmp/codex-claude-code-config
mkdir -p .claude/skills
cp -r /tmp/codex-claude-code-config/skills/operational/deepseek-provider-contract .claude/skills/deepseek-provider-contract
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepseek Provider Contract 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 Deepseek Provider Contract 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 Deepseek Provider Contract 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.

DeepSeek Provider Contract

This is a local, offline contract check. It neither reads DEEPSEEK_API_KEY nor sends code, transcripts, or secrets. A third-party harness is an implementation candidate, not a trusted route.

Use It In This Order

  1. Classify the proposed prompt/data. Private archive, credentials, customer data, and whole transcripts stay local unless the user explicitly authorizes that provider and scope.

  2. Freeze the provider model, endpoint, request budget, and fallback in the integration plan. Do not inherit defaults from a community wrapper.

  3. Save a redacted request-history fixture that explicitly declares whether the outbound request carries tools, then run:

    The fixture shape is {"outbound_request":{"tools":[...]},"messages":[...]}; use "tools": [] only for an outbound tools-free request.

    text
    python skills/operational/deepseek-provider-contract/scripts/validate_deepseek_history.py fixture.json
  4. Run the provider only in a bounded, opt-in experiment. Record model, endpoint, max output, cache-usage fields, request digest, and observed result.

  5. Compare against the incumbent on the same frozen task before adding any route to a workflow. A passing history fixture proves message lifecycle only; it does not prove quality, price, availability, or data handling.

Required Invariants

  • In thinking mode with tools, preserve the provider's complete assistant message, including reasoning_content and tool_calls. Do not rebuild a reduced assistant message from content alone.
  • Each tool result has the exact prior tool_call_id.
  • While a request carries tools, retain the documented reasoning_content from prior assistant turns in its history. Do not reduce this to only the most recent tool-call turn; check the current provider documentation because model and endpoint semantics change.
  • Use strict schema only with the beta endpoint and only after validating the supported JSON Schema subset. Never call a schema "strict" merely because it looks valid locally.
  • Set output bounds explicitly and make cache hits/misses observability, not a correctness promise.

Adoption Decision

Adopt a DeepSeek route only when all are true:

  1. The offline history validator and its regression tests pass.
  2. A redacted live canary succeeds with the exact frozen integration.
  3. The frozen benchmark beats or materially supplements the current route.
  4. Provider data scope and failure fallback are written down.

Otherwise keep this skill and the fixture only; do not install a global MCP.

Gotchas

  • For a tools-bearing request, the current DeepSeek documentation requires the prior assistant reasoning_content; a tools-free request has different context semantics. Treat the offline validator as a narrow fixture check, not a substitute for the live API contract.
  • A response can say cache-hit while quality or latency still changes; measure those separately.
  • A history normalizer that strips reasoning_content can make the next tools-bearing request request fail with HTTP 400 even if the first request succeeded.
  • Model names, limits, pricing, and beta semantics are provider facts: re-check the official documentation at the integration date.

Troubleshooting

SymptomLikely causeFix
HTTP 400 after a tool resultAssistant reasoning was discardedPreserve the complete assistant tool-call message and rerun the validator
Tool JSON is rejected in strict modeWrong endpoint or unsupported schemaUse the beta endpoint, validate the schema subset, or turn strict mode off
Wrapper works once then loses contextHistory was normalized too aggressivelyCompare its persisted fixture with the provider contract
Temptation to add global MCPScope and data boundary were skippedKeep it opt-in until a frozen canary and benchmark pass

Sources

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 Deepseek Provider Contract AI skill do?

Validate a proposed DeepSeek API integration before any key or project context is sent: check thinking-mode tool-call history, strict-schema assumptions, bounded output, and provider data boundaries. Use when integrating DeepSeek, adding DeepSeek tool calls or streaming, debugging DeepSeek 400 after a tool call, or evaluating a DeepSeek harness/MCP. Do NOT use for a generic model comparison, ordinary local coding, or to send a repository transcript to a provider by default.

Why use Deepseek Provider Contract on TypingMind?

Because you install it once and use it with any model. Deepseek Provider Contract 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 Deepseek Provider Contract in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/operational/deepseek-provider-contract. 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 Deepseek Provider Contract?

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 Deepseek Provider Contract?

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

Is the Deepseek Provider Contract AI skill free?

Yes. It is published on GitHub by AnastasiyaW 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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