# Ai Product Strategy

> Create an AI Product Strategy Pack (thesis, use cases, system plan, eval plan, roadmap).

- **Type:** Skill
- **Install:** `agentstack add skill-liqiongyu-lenny-skills-plus-ai-product-strategy`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [liqiongyu](https://agentstack.voostack.com/s/liqiongyu)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [liqiongyu](https://github.com/liqiongyu)
- **Source:** https://github.com/liqiongyu/lenny_skills_plus/tree/main/skills/ai-product-strategy

## Install

```sh
agentstack add skill-liqiongyu-lenny-skills-plus-ai-product-strategy
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AI Product Strategy

## Scope

**Covers**
- Defining an executable product strategy for an AI/LLM/agent product or AI feature portfolio
- Translating AI uncertainty (non-determinism, emergent risks) into an empirical plan with evals + instrumentation
- Choosing product form factor (assistant vs copilot vs agent), autonomy boundaries, and a safety/security posture
- Setting kill criteria so you know when to pivot or stop investing
- Producing a strategy pack leaders and teams can use to align and execute

**When to use**
- "Define our AI product strategy / LLM strategy / agent strategy."
- "Prioritize AI use cases and turn them into an AI roadmap."
- "We're adding AI to an existing product—what should we build and how do we measure it?"
- "We want to ship an agent; define autonomy, security, and rollout."
- "Should we keep investing in our AI feature, or kill it?"

**When NOT to use**
- You need to **build/implement** an LLM system (RAG pipeline, prompt engineering, tool use) → use `building-with-llms`.
- You need to **evaluate a specific AI vendor or tool** (Claude vs GPT, build vs buy for one tool) → use `evaluating-new-technology`.
- You need to **design an AI platform for third-party developers** (APIs, ecosystem, marketplace) → use `platform-strategy`.
- You need to **rapidly prototype** an AI demo/proof-of-concept → use `vibe-coding`.
- You need a long-term product/company **vision** → use `defining-product-vision` first.
- You need deep **competitor research**, battlecards, or win/loss → use `competitive-analysis`.
- You need a feature-level **PRD/spec/design doc** → use `writing-prds` / `writing-specs-designs` after strategy.
- You don't yet have a clear **problem/ICP hypothesis** → use `problem-definition` / `conducting-user-interviews`.

## Inputs

**Minimum required**
- Product context (what exists today) + target customer/user + their job/pain
- Strategy horizon (default: 3–12 months) + constraints (budget, latency, policy/legal, data access, platform)
- Intended AI surface and scope: assistant / copilot / agent; where it lives in the workflow
- Success metrics (1–3) and guardrails (2–5), including safety/trust, cost, and latency

**Missing-info strategy**
- Ask up to 5 questions from [references/INTAKE.md](references/INTAKE.md) (3–5 at a time).
- If details remain missing, proceed with clearly labeled assumptions and provide 2–3 options (use-case focus, autonomy level, build/buy).

## Outputs (deliverables)

Produce an **AI Product Strategy Pack** in Markdown (in-chat; or as files if requested), in this order:

1) **Context snapshot** (decision, users, constraints, why now)
2) **Strategy thesis** (value prop, why-now, differentiation, non-goals)
3) **Use-case portfolio** (prioritized opportunities with feasibility + risk)
4) **Autonomy policy** (assistant→copilot→agent boundaries + human control points)
5) **System plan** (build/buy, data plan, eval plan, cost/latency budgets)
6) **Empirical learning plan** (experiments, instrumentation, iteration cadence)
7) **Roadmap** (phases, milestones, exit criteria, owners)
8) **Kill criteria** (when to pivot or stop; sunk-cost guardrails)
9) **Risks / Open questions / Next steps** (always included)

Templates: [references/TEMPLATES.md](references/TEMPLATES.md)

**Quick mode:** If the user needs a lightweight strategy (single feature, early exploration, or time-boxed to  hasn't reached X after Y weeks, we stop/pivot."
  3. Run [references/CHECKLISTS.md](references/CHECKLISTS.md) and score with [references/RUBRIC.md](references/RUBRIC.md).
  4. Always add **Risks / Open questions / Next steps**.
- **Outputs:** Final **AI Product Strategy Pack**.
- **Checks:** A stakeholder can act on the pack without a meeting; trade-offs and unknowns are explicit; there's a clear "what would make us stop."

## Quality gate (required)
- Use [references/CHECKLISTS.md](references/CHECKLISTS.md) and [references/RUBRIC.md](references/RUBRIC.md).
- Always include: **Risks**, **Open questions**, **Next steps**.

## Examples

**Example 1 (AI-first product):** "Use `ai-product-strategy` to define strategy for an AI coding assistant for mid-market engineering teams. Constraints: ship a beta in 8 weeks; must not leak proprietary code; budget capped at $X/month."
Expected: strategy thesis + prioritized use cases + autonomy policy + system/eval plan + roadmap + kill criteria.

**Example 2 (AI feature portfolio):** "Use `ai-product-strategy` to prioritize AI opportunities for a customer support platform. Decide copilot vs agent, include safety posture, and propose a 2-quarter roadmap."
Expected: use-case portfolio with 1–3 bets, a clear agency-control policy, empirical plan, and phased roadmap with exit criteria.

**Example 3 (Quick mode):** "I have 30 minutes—give me a lightweight AI strategy for adding summarization to our internal wiki."
Expected: context snapshot + 3–5 use cases scored in a table + phased roadmap + risks/next steps. Note that autonomy policy and system plan were skipped and should be done before build.

**Boundary example:** "Pick the best LLM provider."
Response: this is a vendor evaluation, not a product strategy—use `evaluating-new-technology`. If the broader product decision is unclear, offer to run this strategy workflow first to define what you need from a provider.

## Common anti-patterns (avoid these)

1. **"We use AI" as differentiation.** If your only moat is "we added an LLM," anyone can copy you in weeks. Push for data, distribution, or workflow advantages.
2. **Agent-first without permissions.** Defaulting to maximum autonomy before validating with copilot mode. Start with suggest, graduate to act.
3. **No evals = no strategy.** A strategy without measurable quality targets and offline evals is a wish list. Non-deterministic systems require empirical validation.
4. **Ignoring the cost curve.** AI inference costs are real. A strategy that doesn't model cost-per-user or cost-per-task will hit a wall at scale.
5. **"We'll iterate" without a plan.** Iteration requires instrumentation, review cadence, and decision rules—not just good intentions.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [liqiongyu](https://github.com/liqiongyu)
- **Source:** [liqiongyu/lenny_skills_plus](https://github.com/liqiongyu/lenny_skills_plus)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-liqiongyu-lenny-skills-plus-ai-product-strategy
- Seller: https://agentstack.voostack.com/s/liqiongyu
- Browse the marketplace: https://agentstack.voostack.com/browse

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