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.
npx skills add liqiongyu/lenny_skills_plus --skill ai-product-strategygit clone --depth 1 https://github.com/liqiongyu/lenny_skills_plusWrote 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.
[](https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/ai-product-strategy)<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.
<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>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 | $0.00013 | $0.01248 |
| Sonnet 5 | $0.00005 | $0.00499 |
| Haiku 4.5 | $0.00003 | $0.00250 |
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 12d 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.
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-visionfirst. - 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-designsafter 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
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.
- eval/eval_config.json 946 B
- eval/SHOWCASE.md 5.4 KB
- eval/with_skill.md 47 KB
- eval/without_skill.md 26 KB
- README.md 1.8 KB
- references/CHECKLISTS.md 2.6 KB
- references/EXAMPLES.md 2.1 KB
- references/INTAKE.md 2.2 KB
- references/RUBRIC.md 3.1 KB
- references/SOURCE_SUMMARY.md 2.5 KB
- references/TEMPLATES.md 4.3 KB
- references/WORKFLOW.md 3.7 KB
- skillpack.json 378 B
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.
- 12d ago First seen · 143 lines · 26 tokens per session scan A 2aa08e8b7798
ai-product-strategy is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo 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. 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.
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