Which frameworks are supported by the semantic mapping?

It currently supports Autogen, Langroid, Model Context Protocol (MCP), UTCP, FastAPI, Git/Gitflow, SRE, and Memory Graphs.

Does this skill look at my existing code?

Yes, it follows a multi-step workflow that includes querying your codebase via LSP and Grep to ensure translations align with your project's specific conventions.

How does this skill prevent AI hallucinations?

By identifying ambiguous terms and mapping them to a verified ontology of technical concepts, it forces clarification before the AI assumes an incorrect implementation path.

When should I use the semantic-translation skill?

Use it when requirements are vague, when mixing domain-specific language, or when you are a non-technical user wanting to ensure your intent is understood correctly.

Semantic Translation & Technical Mapping

Name: Semantic Translation & Technical Mapping
Author: jcmrs

byjcmrs

Herramientas para Desarrolladores

Translates ambiguous human intent and non-technical language into precise technical concepts across multi-agent frameworks, API protocols, and DevOps domains.

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The Semantic Translation skill acts as a cognitive bridge between high-level natural language and precise technical implementation, specifically targeting the semantic drift that occurs when vague requests interact with complex technical frameworks. By mapping ambiguous terms like 'make it talk' or 'check for gaps' to specific entities in Autogen, Langroid, MCP, UTCP, FastAPI, and SRE ecosystems, it ensures that Claude understands the exact architectural intent. This skill minimizes hallucinations and implementation errors by proactively identifying ambiguity and providing ontological disambiguation before code is generated.

Características Principales

Multi-stage knowledge querying across static ontologies, external docs, and local codebase.
Ontological mapping for 8 major domains including Autogen, Langroid, MCP, and UTCP.
Conversational clarification workflows to verify technical intent before execution.
0 GitHub stars
Ambiguity detection for vague action verbs and meta-questions like 'am I making sense?'
Domain-specific validation for Agent orchestration, API protocols, and SRE observability.

Casos de Uso

Bridging the gap when a non-technical stakeholder provides vague or idiomatic project requirements.
Disambiguating framework-specific terminology when working across multiple agentic ecosystems like Langroid and Autogen.
Validating architectural intent when implementing complex protocols like the Model Context Protocol (MCP) or UTCP.

Acerca de

Características Principales

Multi-stage knowledge querying across static ontologies, external docs, and local codebase.
Ontological mapping for 8 major domains including Autogen, Langroid, MCP, and UTCP.
Conversational clarification workflows to verify technical intent before execution.
0 GitHub stars
Ambiguity detection for vague action verbs and meta-questions like 'am I making sense?'
Domain-specific validation for Agent orchestration, API protocols, and SRE observability.

Casos de Uso

Bridging the gap when a non-technical stakeholder provides vague or idiomatic project requirements.
Disambiguating framework-specific terminology when working across multiple agentic ecosystems like Langroid and Autogen.
Validating architectural intent when implementing complex protocols like the Model Context Protocol (MCP) or UTCP.