llm_select_agent
Classify a task prompt to recommend the optimal agent CLI and model for session-level routing, based on complexity and budget profile.
Instructions
Classify a task prompt and return the recommended agent CLI + model for session-level routing.
Use this BEFORE starting a Claude Code / Codex / Gemini CLI session to pick the right agent runtime for the task. This is session-level routing — it selects which agent to invoke, not which model to call mid-session.
Decision tree (profile × complexity): budget + simple/moderate → codex + gpt-4o-mini budget + complex → codex + gpt-4o (Codex handles most coding; escalate if needed) balanced + simple → codex + gpt-4o-mini balanced + moderate → claude_code + sonnet balanced + complex → claude_code + opus premium + any → claude_code + opus
Returns JSON with: primary — agent binary name: "claude_code" | "codex" | "gemini_cli" primary_model — model flag value (pass via -m or --model) fallback — fallback agent if primary unavailable fallback_model — model for fallback task_type — classified task type (code / analyze / generate / research / query) complexity — simple | moderate | complex confidence — classifier confidence 0–1 reason — one-line classification rationale env_check — dict of required env vars and whether they're set
Args: prompt: The task description to classify (same text you'd pass to the agent). profile: Routing profile — "budget", "balanced", or "premium" (default: "balanced").
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| profile | No | balanced |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |