aeo-optimization
AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs
$ npx -y skills add alinaqi/maggy --skill external-model-delegation --agent claude-codeHow it fires
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UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs
name: external-model-delegation description: UserPromptSubmit hook pattern that classifies prompts into six cost tiers and delegates to external model CLIs when-to-use: When configuring or debugging prompt-based delegation to qwen3, deepseek, kimi, or codex user-invocable: false effort: medium
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 | Delegation command | Cost | |------|-------------------|------| | QWEN | `qwen3 "prompt"` | $0 (local Ollama) | | DEEPSEEK_FLASH | `deepseek --flash "prompt"` | $0.14 / $0.28 per M tokens | | DEEPSEEK_PRO | `deepseek --pro "prompt"` | $0.44 / $0.87 per M tokens | | KIMI | `kimi --quiet -p "prompt"` | $0.60 / $2.50 per M tokens | | CODEX | `codex exec` | varies | | CLAUDE | handle natively | $3-5 / $15-25 per M tokens |
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
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
#!/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'User types prompt
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UserPromptSubmit hook fires
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qwen3 classifies into tier (QWEN|DEEPSEEK_FLASH|DEEPSEEK_PRO|KIMI|CODEX|CLAUDE)
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Hook injects additionalContext: "Run: <delegation-command>"
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Claude reads context, spawns delegation script, returns output
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User sees response from the delegated model| Tier | Task types | |------|-----------| | QWEN | grep, find, regex, shell, syntax lookups, log reading, short summaries | | DEEPSEEK_FLASH | Simple code, boilerplate, CRUD, test writing, small fixes, config | | DEEPSEEK_PRO | Multi-file features, refactors, debugging, medium coding, docs | | KIMI | Single-file review, medium reasoning, commit messages, diff summaries | | CODEX | Bulk generation, mechanical changes across many files | | CLAUDE | Architecture, security, complex debugging, system design, quality-critical |
# 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
Turn Claude Code into a self-reviewing, test-enforced engineering system that remembers context across sessions — then route work across 13 models from a single dashboard.
Repo: alinaqi/maggy
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