What happens if the profile file is missing or invalid?

The skill includes robust error handling that defaults to a neutral relevance score of 0.5 and logs a warning, ensuring the workflow continues without failure.

Where is the user profile file stored?

The skill specifically looks for a file named user-profile.json located in the ~/.looplia/ directory.

Is this skill part of a larger framework?

Yes, it is part of the Looplia Core repository, which is a universal agentic workflow CLI for composing AI agents.

How is the relevance score calculated?

The skill uses a weighted algorithm that compares content tags against user topics, where the score is the sum of matched topic weights divided by the total possible weight.

Can I define my own writing style in the profile?

Yes, the schema allows you to set preferences for tone (beginner to expert), voice (first or third person), and target word counts.

User Profile Personalization

Name: User Profile Personalization
Author: memorysaver

bymemorysaver

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Content Management

Personalizes AI agent outputs by reading user interests and calculating content relevance scores from local profile data.

About

The User Profile Reader skill enables Claude to tailor its behavior and output by accessing a standardized user profile stored within the workspace. By interpreting specific interests, expertise levels, and stylistic preferences, it calculates a numerical relevance score for any given content against the user's documented topics. This ensures that generated documentation, summaries, or reports are perfectly aligned with the user’s unique focus areas and preferred communication style, making agentic workflows significantly more context-aware.

Key Features

Calculates weighted relevance scores (0-1) based on topic interest levels
Integrates seamlessly with other skills to filter content by relevance
Reads local user-profile.json for persistent personalization
Provides automated fallback logic for missing or invalid profile data
Supports custom tone, voice, and target word count preferences
11 GitHub stars

Use Cases

Tailoring technical documentation based on the user's expertise level (beginner vs. expert)
Filtering large data sets or logs to highlight topics most relevant to a specific developer
Standardizing the voice and tone of AI-generated content across a workspace

About

Key Features

Calculates weighted relevance scores (0-1) based on topic interest levels
Integrates seamlessly with other skills to filter content by relevance
Reads local user-profile.json for persistent personalization
Provides automated fallback logic for missing or invalid profile data
Supports custom tone, voice, and target word count preferences
11 GitHub stars

Use Cases

Tailoring technical documentation based on the user's expertise level (beginner vs. expert)
Filtering large data sets or logs to highlight topics most relevant to a specific developer
Standardizing the voice and tone of AI-generated content across a workspace