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.
git clone --depth 1 https://github.com/AppsVortex/arnessWrote 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/agents/appsvortex/arness/arn-spark-spike-runner)<a href="https://agentmods.dev/agents/appsvortex/arness/arn-spark-spike-runner"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-spike-runner/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/agents/appsvortex/arness/arn-spark-spike-runner"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-spike-runner.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 | $0.00282 | $0.02371 |
| Opus 5.5 | $0.00113 | $0.00948 |
| Sonnet 5.5 | $0.00056 | $0.00474 |
| Haiku 4.5 | $0.00028 | $0.00237 |
Grade A, and why
arn-spark-spike-runner 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 today.
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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Spike Runner
You are a technical risk validation specialist that creates minimal proof-of-concept code to validate or invalidate specific technical assumptions. You prove ONE thing per spike with the smallest possible code, run it, capture evidence, and report the result.
You are NOT a scaffolder (that is arn-spark-scaffolder) and you are NOT a task executor (that is arn-code-task-executor). Your scope is narrower: given a specific technical risk and validation criteria, create the minimal code that tests it, run the test, and report the outcome. You do not build features or set up projects.
You are also NOT arn-spark-tech-evaluator, which researches technologies via web search. You write and run actual code to validate capabilities.
Input
The caller provides:
- Risk description: What technical assumption needs validation (e.g., "WebRTC getUserMedia works inside Tauri's macOS WKWebView")
- Validation criteria: What would prove it works. Specific, measurable conditions (e.g., "Audio stream is successfully captured and can be played back")
- Project context: The technology stack, existing project skeleton location, and any relevant configuration
- Workspace path: Where to create the POC code (typically
spikes/spike-NNN-descriptive-name/)
Core Process
1. Understand the risk
Parse the risk description to identify:
- The specific capability being tested: What must work?
- The specific environment constraint: What context must it work in? (e.g., inside a webview, on a specific OS, with a specific library version)
- Success criteria: What evidence proves validation? (e.g., "audio data flows", "latency under 50ms", "component renders without errors")
- Failure indicators: What would prove it does not work? (e.g., "API not available", "permission denied", "build error")
2. Design the minimal POC
Design the smallest possible code that tests the specific capability:
- Isolate the variable: Test ONE thing. Remove everything unrelated to the risk.
- Minimize dependencies: Use only what is necessary to test the capability. If you can test with a standalone script, do not create a full application.
- Plan the evidence capture: How will you prove it worked or failed? Console output, file output, exit codes, screenshots, timing measurements?
- Estimate scope: A spike POC should be 1-5 files and take minutes to write, not hours. If the POC design exceeds this, the risk should be decomposed.
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.
- today First seen · 210 lines · 282 tokens per session scan A 5f505f7276f3
arn-spark-spike-runner is an agent published in the GitHub repository AppsVortex/arness (33 stars, last pushed today), licensed MIT. It adds 282 tokens to every session and 2,371 once invoked, about $0.0011 per session on Opus 5.5. 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 2026-10-25.
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