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 LeadMagic/gtm-skills --skill pricing-strategygit clone --depth 1 https://github.com/LeadMagic/gtm-skillsWrote 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/leadmagic/gtm-skills/pricing-strategy)<a href="https://agentmods.dev/skills/leadmagic/gtm-skills/pricing-strategy"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/pricing-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/leadmagic/gtm-skills/pricing-strategy"><img src="https://agentmods.dev/badge/skills/leadmagic/gtm-skills/pricing-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 366 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00061 | $0.05161 |
| Opus 5 | $0.00030 | $0.02580 |
| Sonnet 5 | $0.00012 | $0.01032 |
| Haiku 4.5 | $0.00006 | $0.00516 |
Grade A, and why
pricing-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 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pricing Strategy
Overview
The most expensive pricing mistake in B2B SaaS is pricing to cost-plus or competitor-minus rather than pricing to value. When pricing is disconnected from willingness-to-pay, companies either leave revenue on the table (pricing too low, the most common error) or kill their pipeline (pricing too high for the perceived value). The non-obvious rule is that the pricing model — the unit of value you charge for — is more important than the price point itself. A well-designed model that aligns with how customers perceive value can support aggressive price points. A misaligned model makes even discount prices feel expensive.
This skill produces a complete pricing strategy grounded in Madhavan Ramanujam's "Monetizing Innovation" 2x2 framework (willingness-to-pay vs pricing model), the Price Intelligently/ProfitWell methodology for value-based pricing research, and OpenView's SaaS benchmarks for stage-appropriate pricing patterns. The deliverables include a Pricing Model Recommendation (which model to use and why), a Tier Structure Design (feature differentiation, pricing anchors, expansion paths), a Willingness-to-Pay Analysis (based on customer research), a Competitive Pricing Comparison, and Discount Authority Guidelines (who can discount, by how much, and when).
The pricing strategy is designed to be a living document — pricing should be reviewed quarterly in early-stage companies and at least annually in scaled companies. This skill provides the initial design and the framework for ongoing optimization.
When to Use
- User says "design our pricing" or "create pricing strategy" → activate this skill
- User asks "how should we price this" or "what pricing model" → use this skill
- User mentions "monetization," "tier design," "packaging," "price increase" → this skill applies
- User is launching a new product or entering a new market and needs pricing
- Current pricing feels arbitrary or was set without research
- User needs discount authority guidelines for the sales team
- After gtm-context and competitive-intel are complete
What ships with it
3 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.
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 · 405 lines · 61 tokens per session scan A 29bc243538e3
pricing-strategy is a skill published in the GitHub repository LeadMagic/gtm-skills (50 stars, last pushed 5d ago), licensed MIT. It adds 61 tokens to every session and 5,161 once invoked, about $0.0003 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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