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 agentmods add skills/getcrew44/crew44/finance-based-pricing-advisornpx skills add getcrew44/crew44 --skill finance-based-pricing-advisorgit clone --depth 1 https://github.com/getcrew44/crew44Wrote 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/getcrew44/crew44/finance-based-pricing-advisor)<a href="https://agentmods.dev/skills/getcrew44/crew44/finance-based-pricing-advisor"><img src="https://agentmods.dev/badge/skills/getcrew44/crew44/finance-based-pricing-advisor.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.05512 |
| Opus 5 | $0.00018 | $0.02756 |
| Sonnet 5 | $0.00007 | $0.01102 |
| Haiku 4.5 | $0.00004 | $0.00551 |
Grade A, and why
finance-based-pricing-advisor 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 6d 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 — 764 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Evaluate the financial impact of pricing changes (price increases, new tiers, add-ons, discounts) using ARPU/ARPA analysis, conversion impact, churn risk, NRR effects, and CAC payback implications. Use this to make data-driven go/no-go decisions on proposed pricing changes with supporting math and risk assessment.
What this is: Financial impact evaluation for pricing decisions you're already considering.
What this is NOT: Comprehensive pricing strategy design, value-based pricing frameworks, willingness-to-pay research, competitive positioning, psychological pricing, packaging architecture, or monetization model selection. For those topics, see the future pricing-strategy-suite skills.
This skill assumes you have a specific pricing change in mind and need to evaluate its financial viability.
Key Concepts
The Pricing Impact Framework
A systematic approach to evaluate pricing changes financially:
-
Revenue Impact — How does this change ARPU/ARPA?
- Direct revenue lift from price increase
- Revenue loss from reduced conversion or increased churn
- Net revenue impact
-
Conversion Impact — How does this affect trial-to-paid or sales conversion?
- Higher prices may reduce conversion rate
- Better packaging may improve conversion
- Test assumptions
-
Churn Risk — Will existing customers leave due to price change?
- Grandfathering strategy (protect existing customers)
- Churn risk by segment (SMB vs. enterprise)
- Churn elasticity (how sensitive are customers to price?)
-
Expansion Impact — Does this create or block expansion opportunities?
- New premium tier = upsell path
- Usage-based pricing = expansion as customers grow
- Add-ons = cross-sell opportunities
-
CAC Payback Impact — Does pricing change affect unit economics?
- Higher ARPU = faster payback
- Lower conversion = higher effective CAC
- Net effect on LTV:CAC ratio
Pricing Change Types
Direct monetization changes:
- Price increase (raise prices for all customers or new customers only)
- New premium tier (create upsell path)
- Paid add-on (monetize previously free feature)
- Usage-based pricing (charge for consumption)
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.
- 6d ago First seen · 764 lines · 36 tokens per session scan A 0fa65a065502
finance-based-pricing-advisor is a skill published in the GitHub repository getcrew44/crew44 (359 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 5,512 once invoked, about $0.0002 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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