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 product-led-salesgit 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/product-led-sales)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/product-led-sales"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/product-led-sales/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/product-led-sales"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/product-led-sales.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 | $0.00027 | $0.02466 |
| Opus 5 | $0.00014 | $0.01233 |
| Sonnet 5 | $0.00005 | $0.00493 |
| Haiku 4.5 | $0.00003 | $0.00247 |
Grade A, and why
product-led-sales 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 9d 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.
Product-Led Sales
Scope
Covers
- Designing a product-led sales (PLS) motion: converting self-serve usage into a sales opportunity that can close larger contracts
- Defining product-qualified entities (PQL/PQA), signals, thresholds, scoring, and routing rules
- Designing the sales handoff workflow (alerts, SLAs, dispositions) and a Product↔Sales feedback loop
- Creating a usage-triggered outreach kit (helpful, compliant, not “creepy”)
- Planning instrumentation, reporting, and a pilot-to-scale rollout
When to use
- “We’re PLG/self-serve, but we want to add sales without breaking the low-touch funnel.”
- “Define PQLs/PQAs and the product signals that should trigger outreach.”
- “Create a product-led sales playbook for sales to act on usage signals.”
- “Sales says MQLs are low quality—build a product-qualified pipeline instead.”
- “Design a PLS pilot (routing + SLAs + measurement) before scaling.”
When NOT to use
- You don’t have meaningful activation or self-serve usage yet (fix onboarding/activation first) -> use
user-onboardingorretention-engagement - You’re pre-product-market-fit and need your first 10 customers via founder-led outreach -> use
founder-sales - You want a purely enterprise, relationship-led motion with buying committees and procurement -> use
enterprise-sales - You need a lead qualification or scoring framework without product usage data -> use
sales-qualification - You need ICP/positioning or pricing/packaging from scratch (do that first, then return)
- You want spammy outreach, deception, or dark patterns (not supported)
- You need legal/privacy/security advice or production data/CRM implementation (coordinate with qualified experts)
Inputs
Minimum required
- Product + model: freemium/trial, typical onboarding path, who uses vs who buys
- ICP/segments: target roles + company types + ACV bands (and which segment is in scope for PLS)
- Objective: conversion to paid, expansion, ACV lift, pipeline creation (pick 1 primary)
- Current funnel baseline: activation rate, trial-to-paid, expansion rate (even rough)
- Usage data reality: what events/attributes exist, and whether you can map users → accounts
- Sales capacity + workflow: SDR/AE/CS roles, SLAs, and where activity is logged (CRM)
- Constraints: regions/compliance, “don’t use these signals,” messaging tone/brand rules
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 1015 B
- eval/SHOWCASE.md 4.9 KB
- eval/with_skill.md 40 KB
- eval/without_skill.md 20 KB
- README.md 1.7 KB
- references/CHECKLISTS.md 2.5 KB
- references/EXAMPLES.md 1.9 KB
- references/INTAKE.md 2.1 KB
- references/RUBRIC.md 5.2 KB
- references/SOURCE_SUMMARY.md 1.9 KB
- references/TEMPLATES.md 4.3 KB
- references/WORKFLOW.md 3.6 KB
- skillpack.json 368 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.
- 9d ago First seen · 143 lines · 27 tokens per session scan A 7a33c027ef1e
product-led-sales is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 2,466 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 2026-09-03.
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