agilab-intent-router

Route terse AGILAB user requests to the correct skill with safety constraints.

19|2|Updated Mar 14, 2025
One-click install
npx skills add https://github.com/ThalesGroup/agilab --skill agilab-intent-router
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agilab-intent-router
Source: https://github.com/ThalesGroup/agilab/tree/main/.claude/skills/agilab-intent-router
Command: npx skills add https://github.com/ThalesGroup/agilab --skill agilab-intent-router

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Route ambiguous or terse user requests to the correct AGILAB workflow, reducing misrouting and ensuring safety and policy adherence.

Core Features & Use Cases

  • Intent routing: classify short user prompts (e.g., "review AGILAB", "update repos", "release it") and map them to the appropriate AGILAB skill with the correct constraints.
  • Session-aware routing: leverage prior turns to maintain scope and continuity across actions.
  • Governance and safety: apply predefined policies to determine target, depth, mode, and output, preventing unsafe or destructive actions.

Quick Start

Provide a terse user request and let the agilab-intent-router map it to the correct AGILAB workflow.

Frequently Asked Questions about agilab-intent-router

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I route terse or ambiguous user requests to the correct workflow?

Intent routing classifies short user prompts and maps them to the appropriate skill with correct constraints. It evaluates terse commands like 'review' or 'update' to determine the target, depth, mode, and output contract automatically.

How does session-aware routing maintain scope across conversation turns?

Session-aware routing leverages prior conversation turns to maintain scope and continuity across actions. It uses existing session context to interpret ambiguous requests, ensuring subsequent commands align with previously established workflow parameters.

What's the best way to apply safety policies to AI conversation intent classification?

Applying predefined governance policies during intent classification ensures safety adherence. The router evaluates target, depth, and mode against safety rules before invoking the underlying skill, preventing unsafe or destructive actions from executing.

Do I need predefined policies to classify intent for short automation commands?

Predefined policies are required to classify intent and set safety modes correctly. The router applies these governance rules to determine validation depth and output contracts, ensuring short commands like 'release it' map to safe workflows.

Can I use conversation design to handle ambiguous commands across different components?

Conversation design with session-aware routing handles ambiguous commands across components by classifying intent and invoking the appropriate underlying skill. It maps terse requests to the correct target with proper validation depth and output constraints.

Why does intent routing prevent misrouting in automated workflows?

Intent routing prevents misrouting by classifying ambiguous user requests before execution. It determines the correct target, safety mode, validation depth, and output contract, ensuring terse commands invoke the appropriate underlying workflow safely.