agent-contracts-routing-tuning

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Actualizado17 de enero de 2026 a las 10:31

Tune routing with TriggerCondition priorities, LLM hints, context_builder, and traceable decisions.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Fuente

yatarousan0227

yatarousan0227/agent-contracts

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SKILL.md

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name	agent-contracts-routing-tuning
description	Tune routing with TriggerCondition priorities, LLM hints, context_builder, and traceable decisions.
metadata	{"short-description":"Routing design and debugging"}

agent-contracts Routing Tuning

Use this skill when you are designing or debugging routing behavior (rule matches vs LLM selection).

Routing Model (what to optimize)

Rule filtering via TriggerCondition (when / when_not, priority)
Candidate selection (top matches + ties)
Optional LLM decision among candidates (with llm_hint)
Fallbacks and terminal states (response.response_type)

Practical Tuning Steps

Make rule-based selection deterministic first (LLM off).
Use priorities to express business rules (100+ = critical, 50-99 = main, 1-49 = fallback).
Add llm_hint only where ambiguity remains after rules.
Use context_builder only when needed; keep default minimal slices.
Debug with decide_with_trace() and inspect matched rules.

Guardrails

Prefer rules for safety/constraints; use LLM for ambiguous intent.
Keep candidate sets small and explainable.
Treat response.response_type terminal values as part of routing design.

References (load only when needed)

docs/core_concepts.md (Traceable Routing / Context Builder)
examples/02_routing_explain.py
docs/skills/official/agent-contracts-routing-tuning/references/debug_playbook.md

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name	agent-contracts-routing-tuning
description	Tune routing with TriggerCondition priorities, LLM hints, context_builder, and traceable decisions.
metadata	{"short-description":"Routing design and debugging"}

agent-contracts Routing Tuning

Use this skill when you are designing or debugging routing behavior (rule matches vs LLM selection).

Routing Model (what to optimize)

Rule filtering via TriggerCondition (when / when_not, priority)
Candidate selection (top matches + ties)
Optional LLM decision among candidates (with llm_hint)
Fallbacks and terminal states (response.response_type)

Practical Tuning Steps

Make rule-based selection deterministic first (LLM off).
Use priorities to express business rules (100+ = critical, 50-99 = main, 1-49 = fallback).
Add llm_hint only where ambiguity remains after rules.
Use context_builder only when needed; keep default minimal slices.
Debug with decide_with_trace() and inspect matched rules.

Guardrails

Prefer rules for safety/constraints; use LLM for ambiguous intent.
Keep candidate sets small and explainable.
Treat response.response_type terminal values as part of routing design.

References (load only when needed)

docs/core_concepts.md (Traceable Routing / Context Builder)
examples/02_routing_explain.py
docs/skills/official/agent-contracts-routing-tuning/references/debug_playbook.md