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External Model Delegation

Community
alinaqi
external-model-delegation

UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs

Overview

Publisheralinaqi
Repositorymaggy
Skill nameexternal-model-delegation
Stars
707
Forks
57
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the External Model Delegation 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/alinaqi/maggy/tree/main/skills/external-model-delegation
  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/alinaqi/maggy.git /tmp/maggy
mkdir -p .claude/skills
cp -r /tmp/maggy/skills/external-model-delegation .claude/skills/external-model-delegation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable External Model Delegation 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 External Model Delegation 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 External Model Delegation 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.

External Model Delegation Pattern

A UserPromptSubmit hook classifies every user prompt into one of six cost/performance tiers. The hook injects additionalContext instructing Claude to run a specific delegation script and return the output.

Tier routing table

TierDelegation commandCost
QWENqwen3 "prompt"$0 (local Ollama)
DEEPSEEK_FLASHdeepseek --flash "prompt"$0.14 / $0.28 per M tokens
DEEPSEEK_PROdeepseek --pro "prompt"$0.44 / $0.87 per M tokens
KIMIkimi --quiet -p "prompt"$0.60 / $2.50 per M tokens
CODEXcodex execvaries
CLAUDEhandle natively$3-5 / $15-25 per M tokens

Delegation script pattern

Each script is a self-contained executable in ~/bin/ that accepts a prompt and writes the response to stdout:

~/bin/
├── qwen3      # Shell: curl to local Ollama API
├── kimi       # Shell: execs Kimi CLI binary
├── deepseek   # Python: httpx to DeepSeek Anthropic-compat API
└── route-task # Shell + qwen3: classifies prompt into tier

Script contract

  1. Accept prompt as first argument: qwen3 "what is 2+2"
  2. Support --flash / --pro model flags (deepseek)
  3. Support --quiet mode flag (kimi)
  4. Write response to stdout, errors to stderr
  5. Exit 0 on success, non-zero on error

Writing a new delegation script

bash
#!/bin/bash
# Minimal delegator template
PROMPT="$1"
API_KEY="${EXTERNAL_API_KEY:-}"
# Call external API, write result to stdout
curl -s https://api.example.com/chat \
  -H "Authorization: Bearer $API_KEY" \
  -d "$(jq -n --arg p "$PROMPT" '{prompt: $p}')" \
  | jq -r '.response'

Routing hook flow

User types prompt
    ↓
UserPromptSubmit hook fires
    ↓
qwen3 classifies into tier (QWEN|DEEPSEEK_FLASH|DEEPSEEK_PRO|KIMI|CODEX|CLAUDE)
    ↓
Hook injects additionalContext: "Run: <delegation-command>"
    ↓
Claude reads context, spawns delegation script, returns output
    ↓
User sees response from the delegated model

Classification tiers

TierTask types
QWENgrep, find, regex, shell, syntax lookups, log reading, short summaries
DEEPSEEK_FLASHSimple code, boilerplate, CRUD, test writing, small fixes, config
DEEPSEEK_PROMulti-file features, refactors, debugging, medium coding, docs
KIMISingle-file review, medium reasoning, commit messages, diff summaries
CODEXBulk generation, mechanical changes across many files
CLAUDEArchitecture, security, complex debugging, system design, quality-critical

Environment

bash
# Required env vars (set in ~/.zshrc)
export DEEPSEEK_API_KEY="sk-..."      # For deepseek delegator
export OPENAI_API_KEY="sk-..."         # For codex CLI
# Ollama must be running locally for qwen3 classification + delegation

Frequently asked questions

What does the External Model Delegation AI skill do?

UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs

Why use External Model Delegation on TypingMind?

Because you install it once and use it with any model. External Model Delegation 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 External Model Delegation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/alinaqi/maggy/tree/main/skills/external-model-delegation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use External Model Delegation?

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 External Model Delegation?

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

Is the External Model Delegation AI skill free?

Yes. It is published on GitHub by alinaqi 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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