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Strategic Build

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strategic-build

Distinguishes strategic vs tactical work during development using Shreyas Doshi's frameworks and Marty Cagan's empowered teams principles. Use when making architectural decisions, choosing what to build, preventing "product theater", or applying the LNO framework (Leverage, Neutral, Overhead). Helps avoid feature factory and build work that compounds.

Overview

Publishermenkesu
Repositoryawesome-pm-skills
Skill namestrategic-build
Stars
406
Forks
120
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by menkesu on GitHub. Read the source before you install it.

Installation

Install the Strategic Build AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/menkesu/awesome-pm-skills.git /tmp/awesome-pm-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-pm-skills/strategic-build .claude/skills/strategic-build
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Strategic Build in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Strategic Build on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Strategic Build is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Strategic Prototyping: Build What Matters

When This Skill Activates

Claude uses this skill when:

  • User asks "should I build this?" or "is this worth it?"
  • Making architectural decisions (reusable component, abstraction, etc.)
  • Evaluating feature requests
  • Distinguishing busy-work from high-impact work
  • Preventing premature optimization or "product theater"

Core Frameworks

1. The LNO Framework (Source: Shreyas Doshi, ex-Stripe/Twitter/Google PM)

Leverage, Neutral, Overhead:

"Not all tasks are created equal. Some tasks compound (Leverage), some maintain (Neutral), and some drain without return (Overhead)."

Use when: Prioritizing what to build or evaluating if work is strategic

The Three Categories:

🚀 Leverage Work (10x multipliers)

  • Compounds over time
  • Enables future work
  • Used repeatedly
  • Examples: Design systems, core infrastructure, strategic features

➡️ Neutral Work (1x necessary)

  • Maintains current state
  • Necessary but doesn't compound
  • One-time impact
  • Examples: Bug fixes, minor UX tweaks, maintenance

⚓ Overhead Work (0x drain)

  • Busy-work that doesn't move needle
  • "Product theater" - looks like PM work but isn't
  • Process for process's sake
  • Examples: Excessive docs, meetings about meetings, vanity features

How to Apply:

For any work request, ask:
1. Will this be used 10+ times? (Leverage candidate)
2. Does this unblock future work? (Leverage candidate)
3. Is this maintaining vs building? (Likely Neutral)
4. Is this just "looking busy"? (Overhead - avoid)

BUILD: Leverage > Neutral > Overhead
RATIO: Aim for 70% Leverage, 20% Neutral, 10% Overhead max

Example:

Request: "Make this component reusable across the app"

LNO Analysis:
- Will we reuse it 10+ times? 
  YES → Leverage work, do it
  NO (only 2-3 uses) → Neutral/Overhead, build specific first

- Does it unblock future teams?
  YES → Leverage (design system contribution)
  NO → Wait until 3rd use case, then refactor

2. The Three Levels of Product Work (Source: Shreyas Doshi)

Impact, Execution, Optics:

"Most execution problems are actually strategy problems. Most failed launches had unclear strategy, not bad execution."

The Hierarchy:

Level 1: IMPACT (Why)

  • What outcome matters?
  • Why does this move the business?
  • What problem are we solving?
  • Failure mode: Building without clear success criteria

Level 2: EXECUTION (How)

  • How do we build this well?
  • Technical decisions
  • Quality standards
  • Failure mode: Great execution on wrong problem

Level 3: OPTICS (Perception)

  • How does this look to stakeholders?
  • Updating status, demos, comms
  • Failure mode: Theater - looks good but no impact

Critical Insight:

"Teams often confuse Level 2 (execution) for Level 1 (impact). You ship fast but to nowhere. Always validate Level 1 before optimizing Level 2."

How to Apply:

Before building anything:
1. Level 1: What's the outcome? (Impact)
   - Define success metric
   - Know why this matters
   
2. Level 2: How to build it? (Execution)
   - Technical approach
   - Quality bar
   
3. Level 3: How to communicate? (Optics)
   - Stakeholder updates
   - Launch narrative

Most teams skip Level 1 and jump to Level 2.

3. Pre-Mortems for Strategic Decisions (Source: Shreyas Doshi)

Imagine Failure Before Building:

"Six months from now, this failed completely. Why did it fail?"

Use when: Making big architectural decisions or starting major features

How:

  1. Assume Failure: Imagine it's 6 months later, project failed
  2. Work Backwards: List all reasons it could have failed
  3. Address Top Risks: Mitigate the most likely failure modes
  4. Decide: Build with eyes open or don't build at all

Example:

Feature: "Build AI-powered recommendations"

Pre-Mortem (Imagine it failed):
- Model wasn't accurate enough (users ignored recommendations)
- Too slow (users left before results loaded)
- Cold start problem (no data for new users)
- Team underestimated ML expertise needed

Mitigations:
✅ Start with hybrid: AI + rule-based fallback
✅ Set latency budget: <500ms or use cached results
✅ Plan cold start: Popular items + collaborative filtering
✅ Hire ML advisor or partner with ML team

4. Empowered vs Feature Teams (Source: Marty Cagan)

Product Theater Warning:

"Too many companies overhired. Product owners, product ops, agile coaches - dramatically overpaid for value they provide. It's project management, not product management."

Feature Team (Avoid This):

  • Given features to build
  • Success = shipped on time
  • PM is project manager
  • Result: Feature factory, no ownership

Empowered Team (Aim for This):

  • Given problems to solve
  • Success = business outcome
  • PM discovers solutions
  • Result: Innovation, ownership

How to Identify:

Feature Team Signs:
❌ "We need to ship X by Q2"
❌ Roadmap is list of features
❌ Success = velocity, not outcomes
❌ PM writes tickets, not strategies

Empowered Team Signs:
✅ "We need to increase retention by 20%"
✅ Roadmap is list of outcomes
✅ Success = metrics moved
✅ PM discovers solutions

Action: If you're in a feature team, shift your framing:

  • Feature request → User problem
  • Ship date → Outcome timeline
  • Velocity → Impact

Decision Tree: Strategic vs Tactical

DECISION: Should I build this?
├─ LNO Check ──────────────────────────┐
│  Is this Leverage work? ──YES──────→ PRIORITIZE
│  Is this Overhead? ───YES──────────→ SKIP
│  Is this Neutral? ────YES──────────↓
├─ Three Levels Check ─────────────────┤
│  Clear Level 1 (Impact)? ──NO──────→ PAUSE & DEFINE
│  Clear Level 2 (How)? ─────YES─────↓
├─ Pre-Mortem ─────────────────────────┤
│  Top failure risks mitigated? NO───→ REDESIGN
│  Confident in approach? ────YES────↓
├─ Feature vs Empowered ───────────────┤
│  Solving problem or building feature?
│  Problem-focused ──────────────────→ BUILD
│  Feature-focused ──────────────────→ REFRAME AS PROBLEM
└─ BUILD WITH STRATEGY ←──────────────┘

Action Templates

Template 1: LNO Assessment

markdown
# Work Item: [Name]

## LNO Classification
- [ ] **Leverage** - Compounds over time, enables future work
- [ ] **Neutral** - Maintains, necessary but doesn't compound
- [ ] **Overhead** - Busy-work, no meaningful impact

## Evidence
- Times this will be used: [number]
- Future work it enables: [list]
- Business impact if skipped: [high/medium/low]

## Decision
- Priority: [High/Medium/Low]
- Rationale: [explain using LNO framework]

Template 2: Three Levels Check

markdown
# Feature: [Name]

## Level 1: IMPACT (Why)
- Problem we're solving: [describe]
- Success metric: [specific number]
- Business outcome: [revenue/retention/acquisition/etc.]
- **✓ Clear?** [yes/no - if no, stop and define]

## Level 2: EXECUTION (How)
- Technical approach: [describe]
- Quality bar: [standards]
- Timeline: [estimate]
- **Only proceed if Level 1 is clear**

## Level 3: OPTICS (Communication)
- Stakeholders: [list]
- Update cadence: [frequency]
- Launch story: [how we'll talk about it]

Template 3: Pre-Mortem

markdown
# Pre-Mortem: [Feature Name]

**Scenario:** It's 6 months from now. This completely failed.

## Why It Failed (Brainstorm)
1. [failure reason]
2. [failure reason]
3. [failure reason]
4. [failure reason]
5. [failure reason]

## Top 3 Risks
1. **Risk:** [most likely failure]
   **Mitigation:** [how to prevent]

2. **Risk:** [second most likely]
   **Mitigation:** [how to prevent]

3. **Risk:** [third most likely]
   **Mitigation:** [how to prevent]

## Go/No-Go Decision
- [ ] Risks acceptable and mitigated
- [ ] Still believe in the approach
- [ ] **Decision:** [Build / Redesign / Skip]

Quick Reference Card

🎯 Strategic Build Checklist

Before Committing:

  • Classified as Leverage/Neutral/Overhead (LNO)
  • If not Leverage, questioning if worth building
  • Level 1 (Impact) crystal clear
  • Success metric defined
  • Pre-mortem completed
  • Top risks mitigated

During Build:

  • Avoiding "product theater" - building real value
  • Focusing on outcomes, not just output
  • Making decisions with strategy in mind
  • Building for reuse only when justified (3+ use cases)

Red Flags (Stop & Reassess):

  • Can't articulate the "why" (Level 1 unclear)
  • Building because "stakeholder wants it"
  • No clear success metric
  • Feels like busy-work

Real-World Examples

Example 1: Stripe's API Design (Shreyas Doshi at Stripe)

Challenge: Should we build this new API endpoint?

LNO Analysis:

  • Used by 1000+ developers → Leverage
  • Enables ecosystem growth → High leverage
  • Alternative: Developers build workarounds → Tech debt

Decision: Build (high Leverage work)


Example 2: Feature Factory Trap (Marty Cagan)

Challenge: Product team shipping fast but metrics flat

Diagnosis: Feature Team, not Empowered Team

  • Roadmap = list of features
  • PM = project manager
  • No ownership of outcomes

Fix:

  • Shifted to outcome-based roadmap
  • PM discovers solutions, not just builds features
  • Measured impact, not velocity

Result: Fewer features, better outcomes


Example 3: Premature Abstraction

Request: "Make this component super reusable"

LNO Assessment:

  • Currently used: 2 times
  • Abstraction adds complexity
  • Not clear if we'll need it again

Decision: Build specific, wait for 3rd use case

  • First 2 uses: Copy-paste (acceptable tech debt)
  • 3rd use: Refactor into shared component (data-driven)

Common Pitfalls

❌ Mistake 1: Optimizing Execution Without Strategy

Problem: Building fast but in wrong direction Fix: Always validate Level 1 (Impact) before Level 2 (Execution)

❌ Mistake 2: Overhead Disguised as Work

Problem: Updating stakeholders, writing docs, tweaking process Fix: Audit your week - what % was Leverage vs Overhead?

❌ Mistake 3: Building for "Someday"

Problem: "We might need this later" → premature abstraction Fix: YAGNI (You Aren't Gonna Need It) - build when needed

❌ Mistake 4: Feature Team Mentality

Problem: Success = shipped, not impact Fix: Reframe every feature as a problem to solve


Related Skills

  • zero-to-launch - For scoping MVP strategically
  • decision-frameworks - For structured decision-making
  • prioritization-craft - For choosing between options
  • strategic-pm - For moving from tactical to strategic thinking
  • ship-decisions - For when to ship vs iterate

Key Quotes

Shreyas Doshi:

"The hardest thing about product management isn't the frameworks - it's distinguishing Leverage work from Overhead disguised as work."

Marty Cagan:

"If you're just delivering output, you're a project manager. Product managers deliver outcomes."

Shreyas on Pre-Mortems:

"Most teams do post-mortems when it's too late. Do pre-mortems when you can still change course."


Further Learning

  • references/shreyas-doshi-frameworks.md - Deep dive on LNO and Three Levels
  • references/marty-cagan-empowered.md - Feature teams vs empowered teams
  • references/strategic-vs-tactical.md - Case studies and examples

Frequently asked questions

What does the Strategic Build AI skill do?

Distinguishes strategic vs tactical work during development using Shreyas Doshi's frameworks and Marty Cagan's empowered teams principles. Use when making architectural decisions, choosing what to build, preventing "product theater", or applying the LNO framework (Leverage, Neutral, Overhead). Helps avoid feature factory and build work that compounds.

Why use Strategic Build on TypingMind?

Because you install it once and use it with any model. Strategic Build is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Strategic Build in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/menkesu/awesome-pm-skills/tree/main/strategic-build. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Strategic Build?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Strategic Build?

As many as you like. As long as a model supports skills, you can use Strategic Build with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Strategic Build AI skill free?

It is published on GitHub by menkesu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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