/model-routing
9-tier model routing system with cascading classifier fallback and result auto-evaluation
Claude Code
$ npx -y skills add alinaqi/maggy --skill model-routing --agent claude-code
Codex
$ npx skills add alinaqi/maggy --skill model-routing -a codex -y
Cursor
$ npx skills add alinaqi/maggy --skill model-routing -a cursor -y
Gemini CLI
$ npx skills add alinaqi/maggy --skill model-routing -a gemini-cli -y
GitHub Copilot
$ npx skills add alinaqi/maggy --skill model-routing -a github-copilot -y
OpenCode
$ npx skills add alinaqi/maggy --skill model-routing -a opencode -y
Windsurf
$ npx skills add alinaqi/maggy --skill model-routing -a windsurf -y
SKILL.md
model-routing.SKILL.mdname: model-routing
description: 9-tier model routing system with cascading classifier fallback and result auto-evaluation
when-to-use: When configuring or debugging how prompts are classified and routed to the cheapest capable model
user-invocable: false
effort: medium
Model Routing System
How Routing Decisions Are Made
Every user prompt goes through a 9-tier classification pipeline before any AI model processes it. The system answers three questions:
1. **Which model should handle this?** — 9-tier cost/complexity classification 2. **Is the classifier itself working?** — Cascading fallback (qwen3 → kimi → deepseek → cache) 3. **Can we verify the result?** — Tool-level fallback + auto-evaluation
The Pipeline
User types prompt
↓
UserPromptSubmit hook fires (~/.claude/hooks/route-task-hook)
↓
Classifier: qwen3 (local, free) classifies into tier
↓ (fails?)
Classifier: kimi (local, free) retries
↓ (fails?)
Classifier: deepseek-flash (~$0.0001) retries
↓ (fails?)
Classifier: cached tier from last success
↓
Hook injects routing decision into Claude's context
↓
Claude delegates to the right model or handles directly9-Tier Routing Table
| Tier | Model | Input (per M) | Output (per M) | Handles | |------|-------|---------------|----------------|---------| | 1 | **Gemini 2.5 Flash-Lite** | $0.10 | $0.40 | Bulk extraction, classification, CIG pipelines | | 2 | **DeepSeek V4 Flash** | $0.14 | $0.28 | Simple code, CRUD, test writing, small fixes | | 3 | **DeepSeek V4 Pro** | $0.44 | $0.87 | Multi-file features, refactors, debugging (~80% of work) | | 4 | **Gemini 2.5 Flash** | $0.15 | $0.60 | Multimodal (images, video, audio), brand analysis | | 5 | **Kimi K2.6** | $0.60 | $2.50 | Code review, commit messages, diff summaries | | 6 | **Gemini 3.1 Pro + Search** | $1.25 | $10.00 | Deep research, Google grounding, 2M context | | 7 | **Codex** | varies | varies | Bulk generation, code review | | 8 | **Claude Sonnet/Opus** | $3-5 | $15-25 | Architecture, security, quality-critical |
Delegation Commands
When the hook says "delegate to X", run the matching command and return its output:
# Tier 1 — Gemini Flash-Lite
~/bin/gemini --flash-lite "prompt"
# Tier 2 — DeepSeek Flash
~/bin/deepseek --flash "prompt"
# Tier 3 — DeepSeek Pro
~/bin/deepseek --pro "prompt"
# Tier 4 — Gemini Flash
~/bin/gemini --flash "prompt"
# Tier 5 — Kimi
~/bin/kimi --quiet -p "prompt"
# Tier 6 — Gemini Pro Search
~/bin/gemini --pro-search "prompt"
# Tier 7 — Codex
codex exec "prompt"
# Tier 8 — Claude
# Handle directly (no delegation)
Delegation Script Contract
Every `~/bin/` script follows the same pattern:
1. **Accepts prompt as argument**: `script "what is 2+2"` 2. **Model flags**: `--flash`, `--pro`, `--flash-lite`, `--pro-search` 3. **Quiet mode**: `--quiet` (where applicable) 4. **Output**: writes response to stdout, errors to stderr 5. **Exit codes**: 0 on success, non-zero on failure
Available Scripts
~/bin/
├── qwen3 # Shell: curl to local Ollama API
├── kimi # Shell: execs Kimi CLI binary
├── deepseek # Python: httpx to DeepSeek Anthropic-compat API
├── gemini # Python: httpx to Gemini OpenAI-compat API
├── research # Python: multi-backend research with auto-evaluation
└── route-task # Shell: qwen3-powered task classification
Classifier Fallback Chain
The classifier itself can fail. When it does, cascading fallback kicks in:
| Level | Classifier | Cost | Threshold | |-------|-----------|------|-----------| | 1 | **qwen3** (Ollama) | $0 | 2s connect, 8s classify | | 2 | **kimi** CLI | $0 | Local process | | 3 | **deepseek-flash** | ~$0.0001 | API call | | 4 | **Cached tier** | $0 | From `~/.claude/routing-cache.json` |
The cache (`~/.claude/routing-cache.json`) saves the last successful tier and timestamp. After compaction, when Ollama may be briefly unreachable, the cache ensures routing continues without dropping to CLAUDE by default.
Missing Wrapper Fallback
Before the hook emits a delegation instruction it checks that the tier's wrapper exists (the resolved flash/pro binary, `~/bin/gemini-api`, `~/bin/gemini-cli`, `~/bin/kimi`, `~/bin/grok`, or `codex` on PATH). If it is missing, the decision becomes `ROUTING DECISION: CLAUDE (fallback from <tier>)` and Claude handles the task directly, without the PLAN FIRST step. A cached tier the hook no longer knows is discarded the same way. There is no qwen3 delegation tier any more.
Tool Fallback Protocol
When Claude's built-in tools fail, external backends take over:
| Failed Tool | Fallback 1 | Fallback 2 | |-------------|------------|------------| | **WebSearch** / **WebFetch** | `~/bin/research "query"` | `~/bin/deepseek --pro "query"` | | **Read** / file access | `cat` via Bash | — | | **Grep** | `grep -r` via Bash | — |
Research Tool (`~/bin/research`)
Multi-backend research with auto-evaluation:
- Tries **deepseek-flash → deepseek-pro** in sequence
- Scores results 0-10 on content quality, structure, length
- Auto-adjusts preferred backend based on evaluation scores
- View stats: `~/bin/research --eval`
- Score log: `~/.claude/research-eval.jsonl`
Maggy Integration
Maggy's `model_router.py` mirrors the same 9-tier structure in `DEFAULT_TIERS`. The `PiAdapter` uses the same delegation scripts for execution. Task type overrides in `routing_rules_defaults.py` ensure:
- `research`, `competitor` → **Gemini Pro Search** (Google grounding)
- `bulk` → **Gemini Flash-Lite** (cheapest)
- `security`, `architecture`, `planning` → **Claude** (quality-critical)
- `docs`, `tests` → **DeepSeek Pro** (cost-efficient)
- `review` → **Claude** (security + architecture depth)
Environment
# Required for delegation scripts (in ~/.zshrc)
export DEEPSEEK_API_KEY="sk-..."
export GEMINI_API_KEY="..." # For gemini delegator
export OPENAI_API_KEY="sk-..." # For codex CLI
# Ollama must be running locally for qwen3
Read more
name: model-routing description: 9-tier model routing system with cascading classifier fallback and result auto-evaluation when-to-use: When configuring or debugging how prompts are classified and routed to the cheapest capable model user-invocable: false effort: medium
Model Routing System
How Routing Decisions Are Made
Every user prompt goes through a 9-tier classification pipeline before any AI model processes it. The system answers three questions:
1. **Which model should handle this?** — 9-tier cost/complexity classification 2. **Is the classifier itself working?** — Cascading fallback (qwen3 → kimi → deepseek → cache) 3. **Can we verify the result?** — Tool-level fallback + auto-evaluation
The Pipeline
User types prompt
↓
UserPromptSubmit hook fires (~/.claude/hooks/route-task-hook)
↓
Classifier: qwen3 (local, free) classifies into tier
↓ (fails?)
Classifier: kimi (local, free) retries
↓ (fails?)
Classifier: deepseek-flash (~$0.0001) retries
↓ (fails?)
Classifier: cached tier from last success
↓
Hook injects routing decision into Claude's context
↓
Claude delegates to the right model or handles directly9-Tier Routing Table
| Tier | Model | Input (per M) | Output (per M) | Handles | |------|-------|---------------|----------------|---------| | 1 | **Gemini 2.5 Flash-Lite** | $0.10 | $0.40 | Bulk extraction, classification, CIG pipelines | | 2 | **DeepSeek V4 Flash** | $0.14 | $0.28 | Simple code, CRUD, test writing, small fixes | | 3 | **DeepSeek V4 Pro** | $0.44 | $0.87 | Multi-file features, refactors, debugging (~80% of work) | | 4 | **Gemini 2.5 Flash** | $0.15 | $0.60 | Multimodal (images, video, audio), brand analysis | | 5 | **Kimi K2.6** | $0.60 | $2.50 | Code review, commit messages, diff summaries | | 6 | **Gemini 3.1 Pro + Search** | $1.25 | $10.00 | Deep research, Google grounding, 2M context | | 7 | **Codex** | varies | varies | Bulk generation, code review | | 8 | **Claude Sonnet/Opus** | $3-5 | $15-25 | Architecture, security, quality-critical |
Delegation Commands
When the hook says "delegate to X", run the matching command and return its output:
# Tier 1 — Gemini Flash-Lite ~/bin/gemini --flash-lite "prompt" # Tier 2 — DeepSeek Flash ~/bin/deepseek --flash "prompt" # Tier 3 — DeepSeek Pro ~/bin/deepseek --pro "prompt" # Tier 4 — Gemini Flash ~/bin/gemini --flash "prompt" # Tier 5 — Kimi ~/bin/kimi --quiet -p "prompt" # Tier 6 — Gemini Pro Search ~/bin/gemini --pro-search "prompt" # Tier 7 — Codex codex exec "prompt" # Tier 8 — Claude # Handle directly (no delegation)
Delegation Script Contract
Every `~/bin/` script follows the same pattern:
1. **Accepts prompt as argument**: `script "what is 2+2"` 2. **Model flags**: `--flash`, `--pro`, `--flash-lite`, `--pro-search` 3. **Quiet mode**: `--quiet` (where applicable) 4. **Output**: writes response to stdout, errors to stderr 5. **Exit codes**: 0 on success, non-zero on failure
Available Scripts
~/bin/ ├── qwen3 # Shell: curl to local Ollama API ├── kimi # Shell: execs Kimi CLI binary ├── deepseek # Python: httpx to DeepSeek Anthropic-compat API ├── gemini # Python: httpx to Gemini OpenAI-compat API ├── research # Python: multi-backend research with auto-evaluation └── route-task # Shell: qwen3-powered task classification
Classifier Fallback Chain
The classifier itself can fail. When it does, cascading fallback kicks in:
| Level | Classifier | Cost | Threshold | |-------|-----------|------|-----------| | 1 | **qwen3** (Ollama) | $0 | 2s connect, 8s classify | | 2 | **kimi** CLI | $0 | Local process | | 3 | **deepseek-flash** | ~$0.0001 | API call | | 4 | **Cached tier** | $0 | From `~/.claude/routing-cache.json` |
The cache (`~/.claude/routing-cache.json`) saves the last successful tier and timestamp. After compaction, when Ollama may be briefly unreachable, the cache ensures routing continues without dropping to CLAUDE by default.
Missing Wrapper Fallback
Before the hook emits a delegation instruction it checks that the tier's wrapper exists (the resolved flash/pro binary, `~/bin/gemini-api`, `~/bin/gemini-cli`, `~/bin/kimi`, `~/bin/grok`, or `codex` on PATH). If it is missing, the decision becomes `ROUTING DECISION: CLAUDE (fallback from <tier>)` and Claude handles the task directly, without the PLAN FIRST step. A cached tier the hook no longer knows is discarded the same way. There is no qwen3 delegation tier any more.
Tool Fallback Protocol
When Claude's built-in tools fail, external backends take over:
| Failed Tool | Fallback 1 | Fallback 2 | |-------------|------------|------------| | **WebSearch** / **WebFetch** | `~/bin/research "query"` | `~/bin/deepseek --pro "query"` | | **Read** / file access | `cat` via Bash | — | | **Grep** | `grep -r` via Bash | — |
Research Tool (`~/bin/research`)
Multi-backend research with auto-evaluation:
- Tries **deepseek-flash → deepseek-pro** in sequence
- Scores results 0-10 on content quality, structure, length
- Auto-adjusts preferred backend based on evaluation scores
- View stats: `~/bin/research --eval`
- Score log: `~/.claude/research-eval.jsonl`
Maggy Integration
Maggy's `model_router.py` mirrors the same 9-tier structure in `DEFAULT_TIERS`. The `PiAdapter` uses the same delegation scripts for execution. Task type overrides in `routing_rules_defaults.py` ensure:
- `research`, `competitor` → **Gemini Pro Search** (Google grounding)
- `bulk` → **Gemini Flash-Lite** (cheapest)
- `security`, `architecture`, `planning` → **Claude** (quality-critical)
- `docs`, `tests` → **DeepSeek Pro** (cost-efficient)
- `review` → **Claude** (security + architecture depth)
Environment
# Required for delegation scripts (in ~/.zshrc) export DEEPSEEK_API_KEY="sk-..." export GEMINI_API_KEY="..." # For gemini delegator export OPENAI_API_KEY="sk-..." # For codex CLI # Ollama must be running locally for qwen3
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.
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Questions about model-routing.
- Which coding agents can use model-routing?
- model-routing installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

