Collect user signals with PSON5 and project redaction-aware agent context.
npx skills add https://github.com/fredabila/pson5 --skill pson-agent
Please help me install this Agent Skill. Skill: pson-agent Source: https://github.com/fredabila/pson5/tree/main/skills/pson-agent Command: npx skills add https://github.com/fredabila/pson5 --skill pson-agent
Collects user signals, stores them in a structured profile, and projects an agent-safe context so AI agents can personalize behavior with privacy and consent safeguards.
Install the SDK, create a user profile, fetch agent context for a target intent, and then use the projection to tailor the response.
High-intent search queries and answers about installing and using this skill.
AI agent personalization collects user signals into a structured profile and projects an agent-safe context. This projected context is then passed to the AI agent to tailor its behavior across diverse interfaces while maintaining privacy safeguards.
Agent-safe projection prevents sensitive data leakage by applying consent rules, redaction, and trust boundaries during context projection. This ensures only safe, filtered personalization data is exposed to downstream AI components.
Multi-interface integration allows you to deploy agent personalization across SDK, HTTP API, CLI, or MCP environments. This enables end-to-end personalization workflows in diverse technical stacks without requiring custom adapters for each interface.
PSON5 structured data manages AI agent memory and consent rules by storing user signals in a structured profile. The system applies consent and privacy rules during projection to ensure compliant behavior modeling and safe context exposure.
Agent personalization supports registry workflows, generative workflows, and provider adapters. It utilizes seven tools with atomic writes to ensure reliable state management and seamless integration across different AI provider environments.
The end-to-end personalization pipeline covers acquisition, modeling, state, graph, simulation, and projection. This structured process ensures user signals are safely transformed into agent-safe context for tailored AI behavior.
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