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/controlplane-com/ai-plugin/autoscaling-capacitynpx skills add controlplane-com/ai-plugin --skill autoscaling-capacitygit clone --depth 1 https://github.com/controlplane-com/ai-pluginWrote 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/controlplane-com/ai-plugin/autoscaling-capacity)<a href="https://agentmods.dev/skills/controlplane-com/ai-plugin/autoscaling-capacity"><img src="https://agentmods.dev/badge/skills/controlplane-com/ai-plugin/autoscaling-capacity.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 | $0.00057 | $0.03449 |
| Opus 5 | $0.00028 | $0.01724 |
| Sonnet 5 | $0.00011 | $0.00690 |
| Haiku 4.5 | $0.00006 | $0.00345 |
Grade A, and why
autoscaling-capacity 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 5d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoscaling & Capacity AI
Tool availability: some MCP tools named here live in the
fulltoolset profile — if one is not advertised on this connection, tell the user to reconnect the MCP server with?toolsets=full(or use thecplnCLI fallback). Reads work on every profile via the genericlist_resources/get_resourcetools;delete_resourceis on every profile exceptreadonly.
Deep skill for scaling and resource optimization. Everything scaling lives in one block — spec.defaultOptions.autoscaling (with capacityAI beside it); spec.localOptions[] overrides it per location. The platform keeps the chosen metric near but below target. For workload types, production defaults, and the spec shape, start with the workload skill.
Picking a metric
| Metric | Scales on | Types | Notes |
|---|---|---|---|
concurrency |
avg in-flight requests per replica | serverless only (its default) | pair with maxConcurrency for a hard per-replica cap |
rps |
requests per second per replica | all three | consistent-response-time HTTP |
cpu |
% of allocated CPU | all three (stateful default) | target ≤ 100; conflicts with Capacity AI (below) |
memory |
% of allocated memory | all three | target ≤ 100 |
latency |
response time in ms at metricPercentile |
standard / stateful | p50 (default) / p75 / p99; target is ms, not % |
multi[] |
several metrics; highest replica count wins | standard / stateful | entries from cpu / memory / rps only, each at most once; replaces metric and top-level target |
keda |
external / event-driven triggers | standard / stateful | GVC must enable KEDA first; target is rejected |
disabled |
nothing — fixed at minScale |
all | realized as min = max |
If metric is omitted, serverless defaults to concurrency and stateful to cpu. Standard is the trap: with Capacity AI on (its default) an omitted metric resolves to disabled — min = max, no autoscaling — so always name the metric on a standard workload that must scale. A metric invalid for the workload type is rejected (e.g. concurrency on standard).
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.
- 5d ago First seen · 183 lines · 57 tokens per session scan A 8ba21891733c
autoscaling-capacity is a skill published in the GitHub repository controlplane-com/ai-plugin (10 stars, last pushed 11d ago), licensed MIT. It adds 57 tokens to every session and 3,449 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 2026-08-31.
Other skills, from other repositories
network-rca
Kubernetes network root cause analysis skill powered by Kubeshark MCP. Use this skill whenever the user wants to investigate past incidents, perform retrospective traffic analysis, take or manage traffic snapshots, extract PCAPs, dissect L7 API calls from historical captures, compare traffic patterns over time, detect…
nginx-to-higress-migration
Migrate from ingress-nginx to Higress in Kubernetes environments. Use when (1) analyzing existing ingress-nginx setup (2) reading nginx Ingress resources and ConfigMaps (3) installing Higress via helm with proper ingressClass (4) identifying unsupported nginx annotations (5) generating WASM plugins for nginx…
higress-openclaw-integration
Deploy and configure Higress AI Gateway for OpenClaw integration. Use when: (1) User wants to deploy Higress AI Gateway, (2) User wants to configure OpenClaw to use more model providers, (3) User mentions 'higress', 'ai gateway', 'model gateway', 'AI网关', (4) User wants to set up model routing or auto-routing, (5) User…
install
Kubeshark installation and deployment skill. Use this skill whenever the user wants to install Kubeshark, deploy Kubeshark to a Kubernetes cluster, set up Kubeshark, configure Kubeshark helm values, generate a Kubeshark config file, customize Kubeshark deployment, troubleshoot Kubeshark installation, upgrade…
ak-cloud-deploy
Deploy an Agent Kernel project to AWS, Azure, or GCP using Terraform modules, or to any Kubernetes cluster (on-prem, baremetal, EKS) using the official Helm chart. Supports serverless and containerized modes for all three clouds. AWS supports execution modes (restsync, restasync, async, stream), queue-based scalable…
huawei-cloud-cce-env-assessment
A skill for huawei cloud container(CCE) assessment. It automatically collects metrics and configurations from containerized application environments on Huawei Cloud to generate a comprehensive assessment report. Use this when users want to evaluate if their Huawei Cloud applications align with cloud-native best…