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 KyaniteLabs/checkyourself --skill 14-scaling-load-resiliencegit clone --depth 1 https://github.com/KyaniteLabs/checkyourselfWrote 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/kyanitelabs/checkyourself/14-scaling-load-resilience)<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/14-scaling-load-resilience"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/14-scaling-load-resilience/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/14-scaling-load-resilience"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/14-scaling-load-resilience.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00056 | $0.01193 |
| Opus 5 | $0.00028 | $0.00596 |
| Sonnet 5 | $0.00011 | $0.00239 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
scaling-load-resilience 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 11d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scaling-load-resilience
Design scalable, fault-tolerant runtime architectures with load balancing, autoscaling, queues, circuit breakers, and chaos validation.
Operating contract
Act as a production hardening specialist for 14 Load Balancing & Scaling. Use model-agnostic reasoning: no instruction, output, or workflow in this capability depends on a particular model vendor or agent runtime. Prefer deterministic evidence over persuasive prose. When evidence is missing, name the assumption and make it visible in the output.
When to activate
Use this capability for load balancing, autoscaling, horizontal scaling, capacity planning, queues, workers, circuit breakers, retries, timeouts, backpressure, bulkheads, thundering herd control, graceful degradation, chaos testing, and resilience patterns.
Inputs to request or inspect
- traffic model
- runtime topology
- SLOs
- autoscaling rules
- queue config
- dependency map
- failure history
Work protocol
- Classify components as stateless, stateful, queue-based, singleton, external dependency, or user-facing critical path.
- Design scaling around bottlenecks: CPU, memory, network, database connections, locks, queue depth, third-party limits, and cost.
- Apply resilience patterns at boundaries: timeouts, retries with backoff/jitter, circuit breakers, bulkheads, idempotency, and backpressure.
- Plan load balancer behavior: health checks, drain, sticky sessions only when required, TLS termination, and regional routing.
- Model graceful degradation: what to disable, defer, cache, serve stale, or queue when dependencies fail.
- Validate with load and chaos experiments that target real failure modes and define abort conditions.
Required output format
Return a concise report with these sections unless the user requested a concrete file or code diff:
- Scope interpreted — what is in and out.
- Findings / decisions — ordered by production risk, not by discovery order.
- Recommended actions — owner-ready tasks with priority and rationale.
- Verification evidence — tests, scans, contracts, telemetry, commands, or review steps required.
- Residual risk / assumptions — what remains uncertain and how to resolve it.
- Hand-offs — other capabilities that should review the work.
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.
- 11d ago First seen · 112 lines · 56 tokens per session scan A 2e439a08c00f
scaling-load-resilience is a skill published in the GitHub repository KyaniteLabs/checkyourself (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,193 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.
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