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 san-npm/skills-ws --skill pricing-optimizationgit clone --depth 1 https://github.com/san-npm/skills-wsWrote 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/san-npm/skills-ws/pricing-optimization)<a href="https://agentmods.dev/skills/san-npm/skills-ws/pricing-optimization"><img src="https://agentmods.dev/badge/skills/san-npm/skills-ws/pricing-optimization.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 | $0.00074 | $0.05802 |
| Opus 5 | $0.00037 | $0.02901 |
| Sonnet 5 | $0.00015 | $0.01160 |
| Haiku 4.5 | $0.00007 | $0.00580 |
Grade A, and why
pricing-optimization 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 8d 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 — 455 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pricing Optimization
Workflow
1. Value Metric Selection
The value metric is what you charge for. Get this wrong and everything else fails.
Good value metric criteria:
- Scales with value delivered to customer
- Easy for customer to understand
- Predictable for customer to budget
- Grows as customer succeeds
| Metric type | Examples | Best fit | Watch out for |
|---|---|---|---|
| Per seat | $X/user/month | Collaboration tools where every user gets value | Customers share logins; AI agents replace seats (seat counts can shrink) |
| Per usage / metered | $X/API call, $X/GB, $X/1k tokens | Infra, APIs, AI products where cost tracks consumption | Unpredictable bills hurt buyer trust → add caps, alerts, or prepaid credits |
| Hybrid (platform fee + usage) | $X/mo base + overage | Usage products needing revenue floor & expansion | Two dials to explain; keep the base meaningful, not a tax |
| Per feature / tier | Tier-gated access | Horizontal SaaS with distinct segments | Feature gates feel arbitrary if not value-aligned |
| Per outcome | $X/lead, $X/transaction, % of GMV | Performance tools that can attribute results | Attribution disputes; revenue swings with customer's business |
| Committed spend | Annual $ commitment drawn down by usage | Enterprise usage products, procurement-friendly | Requires forecasting; overage/rollover policy must be explicit |
| Flat rate | $X/month | Simple, single-persona products | Leaves expansion revenue on the table |
Decision framework (guidelines, not laws — validate against your buyer):
- Value scales ~linearly with active users, and seats aren't easily shared → per seat (but stress-test against AI/automation eroding seat counts).
- Cost-to-serve and value both track consumption → usage / metered; pair with a platform fee (hybrid) when you need a predictable revenue floor and land-and-expand.
- Features cleanly separate segments by their jobs-to-be-done → tier-based.
- You can credibly attribute a business outcome → outcome-based.
- Selling to procurement-led enterprises → committed spend with usage drawdown.
- There is rarely one "right" metric: many durable companies run hybrid (e.g. seats + usage, or platform fee + transaction %). Prefer the metric the buyer already uses to measure success internally.
What ships with it
1 file 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.
- 8d ago First seen · 455 lines · 74 tokens per session scan A ddb2ae26268a
pricing-optimization is a skill published in the GitHub repository san-npm/skills-ws (2 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 5,802 once invoked, about $0.0004 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 2026-08-31.
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