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/cosmix/loom/loom-performance-testingnpx skills add cosmix/loom --skill loom-performance-testinggit clone --depth 1 https://github.com/cosmix/loomWhat 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 | $0.00058 | $0.07032 |
| Opus 5 | $0.00029 | $0.03516 |
| Sonnet 5 | $0.00012 | $0.01406 |
| Haiku 4.5 | $0.00006 | $0.00703 |
Grade A, and why
loom-performance-testing 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 — 517 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Testing
Overview
Validate that a system meets latency, throughput, and stability targets under load, and locate the bottleneck when it doesn't. This file assumes you can write the test code; it focuses on the measurement traps that make load-test numbers lie and the decision criteria for tool/shape selection.
Core Concepts (read first — these are where numbers go wrong)
Open vs. closed workload models
The single most consequential choice. It determines what your numbers mean.
| Model | Load driver | Throughput is… | Overload behavior | Use for |
|---|---|---|---|---|
| Closed | Fixed VUs, each loops request→wait | Emergent (backpressure) | Self-throttles: slow server → fewer requests sent | Modeling a fixed client pool |
| Open | Fixed arrival rate (req/s) | Controlled (you set it) | Queue grows unbounded; latency explodes | Web traffic, finding breaking pt |
⚠ Closed models hide overload. With fixed VUs, when the server slows down each VU sends fewer requests, so offered load silently drops. You can't overwhelm the server past what its own latency allows — you measure a moving target, not capacity. Real internet traffic is open (users arrive independently of server health), so use an arrival-rate executor to find true breaking points.
- k6: closed =
constant-vus/ramping-vus; open =constant-arrival-rate/ramping-arrival-rate. - Locust is closed-model (users); JMeter thread groups are closed; Gatling
injectOpen/injectClosed; artilleryarrivalRateis open. - Open executors need
preAllocatedVUs/maxVUsheadroom; if k6 warns "insufficient VUs," it dropped iterations and your rate was never achieved.
Coordinated omission — why naive latency numbers lie
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 · 517 lines · 58 tokens per session scan A 691c0219f7d3
loom-performance-testing is a skill published in the GitHub repository cosmix/loom (54 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 7,032 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 2026-08-30.
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