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 sananthanarayan/skilldrop --skill capacity-cost-modelgit clone --depth 1 https://github.com/sananthanarayan/skilldropWrote 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/sananthanarayan/skilldrop/capacity-cost-model)<a href="https://agentmods.dev/skills/sananthanarayan/skilldrop/capacity-cost-model"><img src="https://agentmods.dev/badge/skills/sananthanarayan/skilldrop/capacity-cost-model.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 | $0.00120 | $0.01737 |
| Opus 5 | $0.00060 | $0.00869 |
| Sonnet 5 | $0.00024 | $0.00347 |
| Haiku 4.5 | $0.00012 | $0.00174 |
Grade A, and why
capacity-cost-model 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 7d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
capacity-cost-model
Turns "how much will this cost to run?" into a model a reviewer can re-run and finance can trust — sized from demand, expressed in cost per unit, and honest about the line items everyone forgets until the invoice. Distinct from business-case (which decides whether to invest, across options) and nfr-spec (which sets the throughput/availability targets); this models the resources and dollars to meet a target you've already chosen.
How to respond
-
Start from the demand model, never from instance types. The first question is "how much of what?" — the driver (requests/sec, tenants, GB stored, events/day, concurrent users), its current value, its growth curve, and the peak-to-average ratio. Ask at most 2 questions, spent on the driver's current volume + source and the peak ratio (the spikiness that decides static-vs-autoscale). Sizing to a round instance count instead of to demand is how clusters end up 80% idle or 200% over.
-
Express everything as unit economics. Cost per request / per tenant / per 1k events / per GB-month — because that's what scales predictably, what finance budgets in, and what reveals whether the architecture gets cheaper or more expensive per unit as it grows. ✅ "$0.012 per active tenant per day, dominated by the per-tenant search index" — ❌ "about $4k/month" (a number with no denominator can't be reasoned about at 10×).
-
Model the components against the cost catalog (
reference.md) — compute, storage, network egress (the forgotten heavyweight), managed-service tiers, logging/observability (routinely a top-3 surprise), backups/DR, data transfer cross-AZ/region, and non-production environments (dev/staging/idle often 30–50% of the bill). Each line: the quantity from the demand model × the unit price, with the price's source/date tagged. Missing the egress and observability lines is the single most common way a model lands 40% low. -
Make peak-vs-average and headroom explicit decisions, not defaults. State both the average load (what you bill for) and the peak (what you must serve), and the chosen target utilization + buffer with its reasoning: ✅ "size to peak × 1.3 for failover + spike; accept ~55% average utilization because the spike is revenue-critical". Over-provisioning burns money; under-provisioning is an incident — the headroom number is where that tradeoff is decided, so it's named, not buried in a rounded-up instance count.
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.
- 7d ago First seen · 65 lines · 120 tokens per session scan A c135313d3c42
capacity-cost-model is a skill published in the GitHub repository sananthanarayan/skilldrop (2 stars, last pushed 24d ago), licensed MIT. It adds 120 tokens to every session and 1,737 once invoked, about $0.0006 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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