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 defining-product-visiongit 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/defining-product-vision)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/defining-product-vision"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/defining-product-vision/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/defining-product-vision"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/defining-product-vision.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 | $0.00025 | $0.01943 |
| Opus 5 | $0.00013 | $0.00971 |
| Sonnet 5 | $0.00005 | $0.00389 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
defining-product-vision 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 13d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Defining Product Vision
Scope
Covers
- Defining or refreshing a product vision (5–10 year future state)
- Writing a vision statement + short vision narrative (concrete, not a tagline)
- Translating vision into pillars and strategic choices (what we will/won’t do)
- Packaging a “Product Vision Pack” leaders and teams can use as a decision tie-breaker
When to use
- “We need a real product vision (not a slogan).”
- “Leadership isn’t aligned on where the product is going.”
- “Write a vision statement + one-pager for the next 5–10 years.”
- “Bridge our mission to strategy and planning.”
- “We have a big technology vision—what’s the user-friendly product form factor?”
When NOT to use
- You only need a marketing tagline or positioning copy (do marketing/copywriting instead).
- You need a detailed product strategy doc, roadmap, or OKRs after vision is already aligned (use those downstream skills).
- You don’t have even a rough target customer/problem hypothesis (do discovery/research first; use
problem-definition). - You’re choosing metrics/measurement before agreeing on the future state (use
writing-north-star-metricsafter this skill). - You need to prioritize what to build next against an existing vision (use
prioritizing-roadmap). - You need an AI-specific or company-wide product strategy, not a vision statement (use
ai-product-strategy).
Inputs
Minimum required
- Product (what it is today) + target customer segment(s)
- The potent user problem / job-to-be-done the vision is grounded in
- Time horizon (default: 5–10 years)
- Mission / higher-level purpose (or executive intent)
- Constraints (what must remain true: trust, safety, margin, compliance, etc.)
- Stakeholders who must align (roles/names)
Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md.
- If answers aren’t available, proceed with clearly labeled assumptions and provide 2–3 vision options.
Outputs (deliverables)
Produce a Product Vision Pack in Markdown (in-chat; or as files if requested):
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 853 B
- eval/SHOWCASE.md 5.1 KB
- eval/with_skill.md 27 KB
- eval/without_skill.md 19 KB
- README.md 1.7 KB
- references/CHECKLISTS.md 1.8 KB
- references/EXAMPLES.md 2.1 KB
- references/INTAKE.md 1.9 KB
- references/RUBRIC.md 3.8 KB
- references/SOURCE_SUMMARY.md 2.4 KB
- references/TEMPLATES.md 2.6 KB
- references/WORKFLOW.md 3.3 KB
- skillpack.json 393 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.
- 13d ago First seen · 139 lines · 25 tokens per session scan A f2241458667d
defining-product-vision is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 1,943 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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