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 commands/thelobbi/claude/auto-scalegit clone --depth 1 https://github.com/TheLobbi/claudeWrote 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/commands/thelobbi/claude/auto-scale)<a href="https://agentmods.dev/commands/thelobbi/claude/auto-scale"><img src="https://agentmods.dev/badge/commands/thelobbi/claude/auto-scale.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 | $0.00000 | $0.04135 |
| Opus 5 | $0.00000 | $0.02067 |
| Sonnet 5 | $0.00000 | $0.00827 |
| Haiku 4.5 | $0.00000 | $0.00413 |
Grade A, and why
auto-scale 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 3d 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 — 467 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Scaling Optimization
Dynamic scaling operations with auto-scaling policies, cost optimization, performance tuning, and intelligent resource management for cloud infrastructure.
Purpose
Design, implement, and optimize auto-scaling policies for cloud infrastructure to ensure optimal performance, cost efficiency, and reliability through intelligent resource management, predictive scaling, and automated cost optimization.
Multi-Agent Coordination Strategy
Uses adaptive optimization pattern combining workload analysis, predictive modeling, policy optimization, and continuous tuning for intelligent auto-scaling.
Auto-Scaling Architecture
``` ┌──────────────────────────────────────────────────┐ │ Auto-Scaling Orchestrator │ │ (scaling-orchestrator) │ └────────────┬─────────────────────────────────────┘ │ ┌────────┼────────┬────────┬────────┬─────────┐ ▼ ▼ ▼ ▼ ▼ ▼ Analyze Predict Optimize Monitor Tune Cost ```
Execution Flow
Phase 1: Workload Analysis (0-25 mins)
- workload-analyzer - Analyze historical workload patterns
- traffic-pattern-detector - Identify traffic patterns (daily, weekly, seasonal)
- peak-identifier - Identify peak usage periods
- baseline-calculator - Calculate baseline resource needs
- variability-assessor - Assess workload variability
- anomaly-detector - Detect workload anomalies
Phase 2: Resource Analysis (25-50 mins)
- resource-profiler - Profile current resource utilization
- bottleneck-identifier - Identify resource bottlenecks
- capacity-planner - Calculate capacity requirements
- rightsizing-analyzer - Identify oversized/undersized instances
- waste-detector - Detect resource waste
- reservation-optimizer - Optimize reserved instances
Phase 3: Predictive Modeling (50-80 mins)
- demand-forecaster - Forecast future demand
- time-series-analyst - Time series analysis of metrics
- ml-predictor - Machine learning-based prediction
- seasonality-modeler - Model seasonal patterns
- event-anticipator - Anticipate special events (Black Friday, etc.)
- confidence-interval-calculator - Calculate prediction confidence
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.
- 3d ago First seen · 467 lines · 0 tokens per session scan A 2d739296adac
auto-scale is a command published in the GitHub repository TheLobbi/claude (21 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,135 tokens. 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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