What is ai-product-strategy?

A guide for deciding what an AI product or AI feature should do, how it should work at a high level, and how its value, risks, and quality will be tested.

Use it to create an AI product thesis, compare use cases, choose between an assistant, copilot, or agent, set autonomy and safety limits, plan evaluations and monitoring, build a roadmap, and define criteria for stopping or changing direction.

AI systems can give variable answers and create new safety or security risks. This guide helps teams turn that uncertainty into defined use cases, boundaries, measurements, experiments, and stop-or-continue decisions.

ai-product-strategy — Skill for Claude Code and Codex by liqiongyu

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add liqiongyu/lenny_skills_plus --skill ai-product-strategy

Clone the repo

git clone --depth 1 https://github.com/liqiongyu/lenny_skills_plus

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/ai-product-strategy/github.svg)](https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/ai-product-strategy)

Your own site

<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/ai-product-strategy"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/ai-product-strategy/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/ai-product-strategy"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/ai-product-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,496 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00026	$0.02496
Opus 5.5	$0.00010	$0.00998
Sonnet 5.5	$0.00005	$0.00499
Haiku 4.5	$0.00003	$0.00250

Measured 2mo ago against content hash 2aa08e8b7798, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ai-product-strategy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ai-product-strategy/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 143 lines

Files

What ships with it

13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 143 lines · 26 tokens per session scan A 2aa08e8b7798

Subscribe to this mod's changes

ai-product-strategy is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (53 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 2,496 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI Planning agent-skills ai-agents automation claude codex prompt-engineering

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