intent-classifier

name	intent-classifier
version	0.2.10
category	agent
description	Classify user intents and route to appropriate skills, commands, or workflows. Use when determining which skill to invoke, routing requests to specialized agents, or building skill selection logic. Trigger on 'which skill should I use', 'route this to', 'classify this request', 'skill selection', or when multiple skills could handle a task. Not for skill-creator, skill-evaluator.
license	MIT

Intent Classifier

Classify user requests and route them to the most appropriate skill, command, or specialized agent.

When to Use

• User asks which skill to use or how to route a request
• Need to determine which skill handles a given task
• Multiple skills could handle a request and disambiguation is needed
• Even if they just say "which skill should I use" or "route this"

Classification Workflow

1. Intent Extraction

• Parse the user's natural language request
• Identify primary action (create, update, delete, analyze, etc.)
• Extract entities (file paths, technologies, specific terms)

2. Skill Matching

• Compare against available skills catalog
• Score match confidence for each candidate
• Consider skill descriptions and keywords

3. Route Decision

• Select highest-confidence match
• Handle ties and ambiguous cases
• Fall back to general assistance if no match

Classification Rules

Priority Order

1. Exact keyword match - Skill description contains exact phrase
2. Domain match - Technology stack alignment
3. Action match - Verb/action alignment
4. Context match - File paths, extensions, surrounding context

Confidence Scoring

High (0.8-1.0):   Exact description match, multiple keyword hits
Medium (0.5-0.7): Partial match, related domain
Low (0.3-0.4):    Weak match, shared keywords only
None (<0.3):      No relevant match

Multi-Intent Handling

When a request contains multiple intents:

1. Sequential - Chain skills in dependency order
2. Parallel - Execute independent skills simultaneously
3. Composite - Use parent skill that orchestrates sub-skills

Fallback Strategies

Confidence	Action
> 0.7	Execute primary skill
0.5 - 0.7	Execute skill with confirmation
0.3 - 0.5	Present top 2-3 options to user
< 0.3	Use general assistance

Dynamic Catalog

Update the skill catalog using:

./scripts/dynamic-catalog.sh

This scans .agents/skills/ and regenerates the skill registry with:

• Skill names and descriptions
• Keywords extracted from descriptions
• Compatibility requirements

Skill Selection Examples

User Request	Primary Intent	Routed Skill
"Create a new skill for Docker"	Skill creation	skill-creator
"Audit my code for security issues"	Security audit	security-code-auditor
"Design a REST API for users"	API design	api-design-first
"Fix this bug in my Python script"	Code debugging	(general)

See Also

• skill-creator — Create and improve skills
• skill-evaluator — Evaluate and score skills
• goap-agent — Orchestrator for complex multi-step tasks

Rationalizations

Rationalization	Reality
"I can just pick a skill manually"	Systematic classification ensures the best-matched skill is used and prevents overlooking specialized alternatives.
"Close enough is fine, I'll just pick the first match"	Low-confidence matches lead to wrong workflows and wasted tokens; present options when uncertain.

Red Flags

• Routing to a skill without checking confidence score
• Ignoring multi-intent requests that require chaining skills
• Failing to update the dynamic catalog after adding new skills

References

• references/classification-rules.md - Detailed classification algorithm
• references/skill-catalog.md - Auto-generated skill registry
• references/workflow-patterns.md - Multi-skill orchestration patterns

Voice & Context

• Default: professional + blog
• Reference: voice-profiles skill for definitions and auto-detection.