chitin-core

Selects the most cost-effective capable model for tasks, handles rate limit failures gracefully, and supports tier overrides and health checks.

Route tasks to the cheapest capable model. Never crash on rate limits.

Activation

When spawning sub-agents or delegating tasks, use ModelRouter to select the optimal model.

Trigger phrases: "route this", "spawn a sub-agent", "delegate", or any time you need to choose a model for a task.

Usage

Route a Task

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js route "task description here"

Returns JSON:

{"tier":"MEDIUM","model":"google-antigravity/gemini-3.1-pro","confidence":0.85,"estimatedCost":0.005,"signals":["codeSignals:2×1.2=2.4"]}

Use the returned model value in sessions_spawn.

Handle Failures

If a spawned session fails with a rate limit or error:

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js fail "provider/model" "error message"

Then re-route — the failed model will be skipped:

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js route "same task"

Check Health

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js health

View Costs

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js costs

Validate Config

node ~/.openclaw/workspace/skills/chitin-core/scripts/router.js validate

Workflow

1. Receive task from user
2. Run router.js route "<task>" to get optimal model
3. sessions_spawn with returned model
4. If spawn fails → router.js fail "<model>" "<error>" → retry route
5. Return result to user

Tiers

Tier	Use Case	Models
LIGHT	Greetings, simple Q&A, status checks	Flash, DeepSeek, gpt-5-mini, Groq, Ollama
MEDIUM	Code, summaries, standard tasks	Gemini Pro, gpt-5.2, DeepSeek Reasoner
HEAVY	Architecture, complex reasoning, agentic	gpt-5.2-pro, o3, Codex

Override Tags

Include in task text to force a tier:

• @light — force cheapest model
• @medium — force mid-tier
• @heavy — force most capable

Graceful Degradation

If all models in a tier are rate-limited, the router automatically:

1. Tries adjacent tiers (escalate or downgrade)
2. Falls back to local Ollama if configured
3. Returns structured error with retry time (never crashes)

Configuration

Edit config.json in the skill directory to:

• Add/remove models per tier
• Adjust cost figures
• Tune classification boundaries