# Context Engineering Advisor

Diagnose context stuffing vs.

- Repository: https://github.com/deanpeters/Product-Manager-Skills (7825 stars)
- Category: AI and agents
- License: see repository
- Last updated: 2026-10-16
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add deanpeters/Product-Manager-Skills --skill context-engineering-advisor -g -a claude-code -y`
Codex: `npx skills add deanpeters/Product-Manager-Skills --skill context-engineering-advisor -g -a codex -y`
Cursor: `npx skills add deanpeters/Product-Manager-Skills --skill context-engineering-advisor -g -a cursor -y`
Gemini CLI: `npx skills add deanpeters/Product-Manager-Skills --skill context-engineering-advisor -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add deanpeters/Product-Manager-Skills`

## Description

Diagnose context stuffing vs. context engineering. Use when an AI workflow feels bloated, brittle, or hard to steer reliably.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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.

## Input

**Works best with:** A description of the AI workflow, agent, or prompt setup that feels bloated, brittle, or hard to steer.
**Also useful:** What you've already stuffed into context (docs, transcripts, schemas) and where outputs go wrong.

Anything supplied with the invocation itself, text after the skill name, a pasted context dump, or an appended `ARGUMENTS:` line, counts as answers already given. Use it and skip whatever it covers; don't re-ask.

**Arriving empty-handed? That works too.** The advisor opens by asking what you're feeding the model today and what breaks.

**Example invocation:** `Diagnose my setup: our support-triage agent gets the full 40-page policy manual per ticket and still misroutes edge cases.`

## 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

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The rest: https://github.com/deanpeters/Product-Manager-Skills/blob/HEAD/skills/context-engineering-advisor/SKILL.md