---name: ai-shaped-readiness-advisor
argument-hint: "[team or workflow context]"
description: Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.
intent: >-
Assess whether your product work is **"AI-first"** (using AI to automate existing tasks faster) or **"AI-shaped"** (fundamentally redesigning how product teams operate around AI capabilities). Use this to evaluate your readiness across **5 essential PM competencies for 2026**, identify gaps, and get concrete recommendations on which capability to build first.
type: interactive
theme: ai-agents
best_for:
- "Assessing whether your team is AI-first or genuinely AI-shaped"
- "Identifying which of the 5 AI competencies to build next"
- "Understanding your product org's AI maturity honestly"
scenarios:
- "My team uses AI tools but I'm not sure if we're working differently or just automating the same tasks"
- "I want to assess my product org's AI maturity and prioritize where to invest next quarter"
estimated_time: "15-20 min"
---## Purpose
Assess whether your product work is **"AI-first"** (using AI to automate existing tasks faster) or **"AI-shaped"** (fundamentally redesigning how product teams operate around AI capabilities). Use this to evaluate your readiness across **5 essential PM competencies for 2026**, identify gaps, and get concrete recommendations on which capability to build first.
**Key Distinction:** AI-first is cute (using Copilot to write PRDs faster). AI-shaped is survival (building a durable "reality layer" that both humans and AI trust, orchestrating AI workflows, compressing learning cycles).
This is not about AI tools—it's about **organizational redesign around AI as co-intelligence**. The interactive skill guides you through a maturity assessment, then recommends your next move.
## Input
**Works best with:** A description of how your team currently uses AI in its product work — even 'barely' is a valid answer.
**Also useful:** Team size, product domain, and which of the 5 competencies you suspect is weakest.
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 how AI currently shows up in your team's day-to-day product work.
**Example invocation:** `Assess my team: 6 PMs, we use ChatGPT for PRD drafts and meeting summaries but nothing in our discovery or delivery process has changed.`
**Critical Insight:** If a competitor can replicate your AI usage by throwing bodies at it, it's not differentiation—it's just efficiency (which becomes table stakes within months).
---
### The 5 Essential PM Competencies (2026)
These competencies define AI-shaped product work. You'll assess your maturity on each.
#### 1. **Context Design**
Building a durable **"reality layer"** that both humans and AI can trust—treating AI attention as a scarce resource and allocating it deliberately.
- **Context Engineering (AI-shaped):** Shaping structure for attention (bounded domains, retrieve with intent)
**The 5 Diagnostic Questions:**
1. What specific decision does this support?
2. Can retrieval replace persistence?
3. Who owns the context boundary?
4. What fails if we exclude this?
5. Are we fixing structure or avoiding it?
**AI-first version:** Pasting PRDs into ChatGPT; no context boundaries; "more is better" mentality
**AI-shaped version:** CLAUDE.md files, evidence databases, constraint registries AI agents reference; two-layer memory architecture; Research→Plan→Reset→Implement cycle to prevent context rot
**Deep Dive:** See [`context-engineering-advisor`](../context-engineering-advisor/SKILL.md) for detailed guidance on diagnosing context stuffing and implementing memory architecture.
---
#### 2. **Agent Orchestration**
Creating repeatable, traceable AI workflows (not one-off prompts).
**What it includes:**
- Defined workflow loops: research → synthesis → critique → decision → log rationale
- Each step shows its work (traceable reasoning)
- Workflows run consistently (same inputs = predictable process)
- Version-controlled prompts and agents
**Key Principle:** One-off prompts are tactical. Orchestrated workflows are strategic.
**AI-first version:** "Ask ChatGPT to analyze this user feedback"
- progress labels (for example, Context Qx/8 and Scoring Qx/5)
- interruption handling and pause/resume behavior
- numbered recommendations at decision points
- quick-select numbered response options for regular questions (include `Other (specify)` when useful)
This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.
## Application
This interactive skill uses **adaptive questioning** to assess your maturity across 5 competencies, then recommends which to prioritize.
### Facilitation Protocol (Mandatory)
1. Ask exactly **one question per turn**.
2. Wait for the user's answer before asking the next question.
3. Use plain-language questions (no shorthand labels as the primary question). If needed, include an example response format.
4. Show progress on every turn using user-facing labels:
- `Context Qx/8` during context gathering
- `Scoring Qx/5` during maturity scoring
- Include "questions remaining" when practical.
5. Do not use internal phase labels (like "Step 0") in user-facing prompts unless the user asks for internal structure details.
6. For maturity scoring questions, present concise 1-4 choices first; share full rubric details only if requested.
7. For context questions, offer concise numbered quick-select options when practical, plus `Other (specify)` for open-ended answers. Accept multi-select replies like `1,3` or `1 and 3`.
8. Give numbered recommendations **only at decision points**, not after every answer.
9. Decision points include:
- After the full context summary
- After the 5-dimension maturity profile
- During priority selection and action-plan path selection
10. When recommendations are shown, enumerate clearly (`1.`, `2.`, `3.`) and accept selections like `#1`, `1`, `1 and 3`, `1,3`, or custom text.
11. If multiple options are selected, synthesize a combined path and continue.
12. If custom text is provided, map it to the closest valid path and continue without forcing re-entry.
13. Interruption handling is mandatory: if the user asks a meta question ("how many left?", "why this label?", "pause"), answer directly first, then restate current progress and resume with the pending question.
14. If the user says to stop or pause, halt the assessment immediately and wait for explicit resume.
15. If the user asks for "one question at a time," keep that mode for the rest of the session unless they explicitly opt out.
16. Before any assessment question, give a short heads-up on time/length and let the user choose an entry mode.
- Ask only the minimum missing context needed (0-2 clarifying questions).
- Move to scoring as soon as context is sufficient.
- **If Best guess mode:** Ask for the smallest viable starting input (role/team + primary goal), then:
- Infer missing details using reasonable defaults.
- Label each inferred item as `Assumption`.
- Include confidence tags (`High`, `Medium`, `Low`) for each assumption.
- Continue without blocking on unknowns.
At the final summary, include an **Assumptions to Validate** section when context dump or best guess mode was used.
---
### Step 0: Gather Context
**Agent asks:**
Collect context using this exact sequence, one question at a time:
1. "Which AI tools are you using today?"
2. "How does your team usually use AI today: one-off prompts, reusable templates, or multi-step workflows?"
3. "Who uses AI consistently today: just you, PMs, or cross-functional teams?"
4. "About how many PMs, engineers, and designers are on your team?"
5. "What stage are you in: startup, growth, or enterprise?"
6. "How are decisions made: centralized, distributed, or consensus-driven?"
7. "What competitive advantage are you trying to build with AI?"
8. "What's the biggest bottleneck slowing learning and iteration today?"
After question 8, summarize back in 4 lines:
- Current AI usage pattern
- Team context
- Strategic intent
- Primary bottleneck
---
### Step 1: Context Design Maturity
**Agent asks:**
Let's assess your **Context Design** capability—how well you've built a "reality layer" that both humans and AI can trust, and whether you're doing **context stuffing** (volume without intent) or **context engineering** (structure for attention).
**Which statement best describes your current state?**
1. **Level 1 (AI-First / Context Stuffing):** "I paste entire documents into ChatGPT every time I need something. No shared knowledge base. No context boundaries."
- Reality: One-off prompting with no durability; "more is better" mentality
- Problem: AI has no memory; you repeat yourself constantly; context stuffing degrades attention
- **Context Engineering Gap:** No answers to the 5 diagnostic questions; persisting everything "just in case"
2. **Level 2 (Emerging / Early Structure):** "We have some docs (PRDs, strategy memos), but they're scattered. No consistent format. Starting to notice context stuffing issues (vague responses, normalized retries)."
- Reality: Context exists but isn't structured for AI consumption; no retrieval strategy
- Problem: AI can't reliably find or trust information; mixing always-needed with episodic context
- **Context Engineering Gap:** No context boundary owner; no distinction between persist vs. retrieve
3. **Level 3 (Transitioning / Context Engineering Emerging):** "We've started using CLAUDE.md files and project instructions. Constraints registry exists. We're identifying what to persist vs. retrieve. Experimenting with Research→Plan→Reset→Implement cycle."
- Reality: Structured context emerging, but not comprehensive; context boundaries defined but not fully enforced
- Problem: Coverage is patchy; some areas well-documented, others vibe-driven; inconsistent retrieval practices
- **Context Engineering Progress:** Can answer 3-4 of the 5 diagnostic questions; context boundary owner assigned; starting to use two-layer memory
4. **Level 4 (AI-Shaped / Context Engineering Mastery):** "We maintain a durable reality layer: constraints registry (20+ entries), evidence database, operational glossary (30+ terms). Two-layer memory architecture (short-term conversational + long-term persistent via vector DB). Context boundaries defined and owned. AI agents reference these automatically. We use Research→Plan→Reset→Implement to prevent context rot."
- Reality: Comprehensive, version-controlled context both humans and AI trust; retrieval with intent (not completeness)
- Outcome: AI operates with high confidence; reduces hallucination and rework; token usage optimized; no context stuffing
**Note:** If you selected Level 1-2 and struggle with context stuffing, consider using [`context-engineering-advisor`](../context-engineering-advisor/SKILL.md) to diagnose and fix Context Hoarding Disorder before proceeding.
Now let's assess **Agent Orchestration**—whether you have repeatable AI workflows or just one-off prompts.
**Which statement best describes your current state?**
1. **Level 1 (AI-First):** "I type prompts into ChatGPT as needed. No saved workflows or templates."
- Reality: Tactical, ad-hoc usage
- Problem: Inconsistent results; can't scale or audit
2. **Level 2 (Emerging):** "I have a few saved prompts I reuse. Maybe some custom GPTs or Claude Projects."
- Reality: Repeatable prompts, but not full workflows
- Problem: Each step is manual; no orchestration
3. **Level 3 (Transitioning):** "We've built some multi-step workflows (research → synthesis → critique). Tracked in tools like Notion or Linear."
- Reality: Workflows exist but require manual handoffs
- Problem: Still human-in-the-loop for every step; not fully automated
4. **Level 4 (AI-Shaped):** "We have orchestrated AI workflows that run autonomously: research → synthesis → critique → decision → log rationale. Each step is traceable and version-controlled."
- Reality: Workflows run consistently; show their work at each step
- Outcome: Reliable, auditable, scalable AI processes
Finally, **Strategic Differentiation**—are you creating defensible competitive advantages, or just efficiency gains?
**Which statement best describes your current state?**
1. **Level 1 (AI-First):** "We use AI to work faster (write better docs, respond to customers quicker). Efficiency gains only."
- Reality: Table-stakes improvements
- Problem: Competitors can copy this within months
2. **Level 2 (Emerging):** "AI enables us to do things we couldn't before (analyze 10x more data, test more hypotheses). New capabilities, but competitors could replicate."
- Reality: Capability expansion, but not defensible
- Problem: No moat; competitors hire more people to match
3. **Level 3 (Transitioning):** "We've redesigned some workflows around AI (e.g., validate hypotheses in 2 days vs. 3 weeks). Starting to create separation."
- Reality: Workflow advantages emerging
- Problem: Not yet systematic; only applied in pockets
4. **Level 4 (AI-Shaped):** "We've fundamentally rewired how we operate: customers get capabilities they can't get elsewhere, our learning cycles are 10x faster than industry standard, our economics are 5x better. Competitors can't replicate without full org redesign."
- Reality: Defensible competitive moat
- Outcome: Strategic advantage that compounds over time
- Plan: Synthesize into high-density SPEC.md or PLAN.md
- Reset: Clear context window
- Implement: Use only the plan as context
3. Update AI prompts to reference constraints registry and glossary
4. Test: Ask AI to cite constraints when making recommendations
5. Measure: % of AI outputs that cite evidence vs. hallucinate; token usage efficiency
**Success Criteria:**
- ✅ Constraints registry has 20+ entries
- ✅ Operational glossary has 20-30 terms
- ✅ Evidence standards documented and shared
- ✅ Context Manifest created (always-needed vs. episodic)
- ✅ Context boundary owner assigned
- ✅ Two-layer memory architecture implemented
- ✅ Research→Plan→Reset→Implement cycle tested on 1 workflow
- ✅ AI agents reference these automatically
- ✅ Token usage down 30%+ (less context stuffing)
- ✅ Output consistency up (fewer retries)
**Related Skills:**
- **[`context-engineering-advisor`](../context-engineering-advisor/SKILL.md)** (Interactive) — Deep dive on diagnosing context stuffing and implementing memory architecture
- `problem-statement.md` — Define constraints before framing problems
