context-engineering-advisor

SKILL.md

Diagnose whether your AI workflows are context stuffing or context engineering, then implement structured practices to improve output quality.

Distinguishes between context stuffing (volume-based) and context engineering (structure-based), with five diagnostic questions to identify Context Hoarding Disorder

Guides two-layer memory architecture: short-term conversational memory plus long-term persistent memory via vector databases for semantic retrieval

Implements the Research→Plan→Reset→Implement cycle to eliminate context rot and prevent goal drift during multi-phase AI workflows

Provides falsification tests and ownership frameworks to define context boundaries, prevent unbounded growth, and establish accountability for what persists vs. retrieves

Purpose

Guide product managers through diagnosing whether they're doing context stuffing (jamming volume without intent) or context engineering (shaping structure for attention). Use this to identify context boundaries, fix "Context Hoarding Disorder," and implement tactical practices like bounded domains, episodic retrieval, and the Research→Plan→Reset→Implement cycle.

Key Distinction: Context stuffing assumes volume = quality ("paste the entire PRD"). Context engineering treats AI attention as a scarce resource and allocates it deliberately.

This is not about prompt writing—it's about designing the information architecture that grounds AI in reality without overwhelming it with noise.

Key Concepts

The Paradigm Shift: Parametric → Contextual Intelligence

The Fundamental Problem:

LLMs have parametric knowledge (encoded during training) = static, outdated, non-attributable

When asked about proprietary data, real-time info, or user preferences → forced to hallucinate or admit ignorance

Context engineering bridges the gap between static training and dynamic reality

PM's Role Shift: From feature builder → architect of informational ecosystems that ground AI in reality