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 jeet129/praxis --skill capacity-resource-estimationgit clone --depth 1 https://github.com/jeet129/praxisWrote 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/jeet129/praxis/capacity-resource-estimation)<a href="https://agentmods.dev/skills/jeet129/praxis/capacity-resource-estimation"><img src="https://agentmods.dev/badge/skills/jeet129/praxis/capacity-resource-estimation.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 8 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00119 | $0.03222 |
| Opus 5 | $0.00060 | $0.01611 |
| Sonnet 5 | $0.00024 | $0.00644 |
| Haiku 4.5 | $0.00012 | $0.00322 |
Grade A, and why
capacity-resource-estimation 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 — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capacity & Resource Estimation
capability: build-and-deploy
domain: infra
state: active
dependencies:
- nfr-definition
- architecture-pattern-selection
- iac
triggers:
- "sizing infrastructure for a new project"
- "sizing a new service before initial deploy"
- "revisiting capacity after load data invalidates initial assumptions"
- "preparing the production_go_live evidence package"
- "growth-event planning (campaign, expansion, expected surge)"
outputs:
- sizing model per service (compute / memory / storage / IOPS / network)
- autoscaling policy (min / max / triggers)
- capacity assumptions (workload model, dependency calls, growth curve)
- headroom buffer per service
- environment cost envelope (monthly $)
- cost-vs-NFR tradeoff documentation
consumers:
- platform-sre (primary author)
- iac (consumes sizing for resource definitions)
- deploy-release (consumes for pod/container resource requests + limits)
- cost-finops (consumes for budgets and attribution)
- performance-testing (load test scenarios match the sizing assumptions)
- reliability-dr (DR sizing follows from this)
references: []
The discipline that turns NFR targets into provisioned infrastructure. Without it, projects either over-provision (paying for idle capacity) or under-provision (paging at peak load). With it, sizing is evidenced — explicit workload model, explicit assumptions, explicit growth curve — so revisions are honest and reproducible.
Capacity sizing isn't an exact science but it should be visibly approximate — when sizes are wrong, the assumptions that drove them should be visible enough to revise.
When this skill fires
- A new project's infrastructure is being sized for the first time.
- A new service is being added; its size needs estimation before initial deploy.
- Mid-project, load data reveals the initial sizing assumptions were wrong; resize.
- The
production_go_livegate needs evidence of capacity sufficiency. - A growth event is being planned (marketing campaign, geographic expansion, expected surge).
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 · 296 lines · 119 tokens per session scan A 5f7d86daae0a
capacity-resource-estimation is a skill published in the GitHub repository jeet129/praxis (7 stars, last pushed 4d ago), licensed MIT. It adds 119 tokens to every session and 3,222 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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