arn-spark-spike-runner

arn-spark-spike-runner is an agent for Claude Code from AppsVortex/arness. It costs 282 tokens per session (2,371 once invoked), scanned A, original, MIT.

A technical proof-of-concept agent that writes and runs the smallest code needed to test one specific technical assumption. A proof of concept is a small experiment used to see whether an approach works before building the full feature.

In plain words
What is it for?
Use it to test a single technical risk, such as whether a library works in a particular environment, and report the observed result.
Why use it?
It reduces the risk of committing to an untested technology or integration. It produces evidence showing whether the stated validation conditions passed, partly passed, or failed.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the arn-spark plugin — 28 skills, 20 agents shipped together

Good fit Use it to test a single technical risk, such as whether a library works in a particular environment, and report the observed result.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/appsvortex/arness/arn-spark-spike-runner
Install

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.

Clone the repo
git clone --depth 1 https://github.com/AppsVortex/arness

Made for: Claude Code.

Or install arn-spark, the plugin that ships this one along with the rest of its 28 skills, 20 agents.

Wrote 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.

agentmods badge for arn-spark-spike-runner

README.md
[![agentmods](https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-spike-runner/github.svg)](https://agentmods.dev/agents/appsvortex/arness/arn-spark-spike-runner)
Your own site
<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.

agentmods 80×15 button for arn-spark-spike-runner

Your own site · 80×15
<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>
Per session 282 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,371 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured today against content hash 5f505f7276f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

plugins/arn-spark/agents/arn-spark-spike-runner.md · 210 lines

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.

Read the full file on GitHub · 210 lines

Changes

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.

  1. today First seen · 210 lines · 282 tokens per session scan A 5f505f7276f3

Subscribe to this mod's changes

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.

Related

Other agents, from other repositories

quality-fixer

Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.

shinpr/claude-code-workflows · 44 tokens

evidence-based-investigator

Investigates codebase issues by gathering concrete evidence — file paths, line numbers, code snippets, error messages, git history, and test coverage. Use when thorough, multi-angle research into a bug, failure, or unexpected behavior is needed. Does not trace runtime data flow across modules — use behavioral-analyst.…

testdouble/han · 85 tokens

debugger

Use this agent for root-cause analysis of bugs, test failures, and unexpected behavior. Follows the 5 Whys protocol and produces ROOT-CAUSE.md with evidence chain and remediation plan. Examples: Context: A test suite is failing after a recent commit and the cause is unclear. user: "Figure out why the tests are…

lgbarn/shipyard · 305 tokens

shipyard:debugger

Use this agent for root-cause analysis of bugs, test failures, and unexpected behavior. Follows the 5 Whys protocol and systematic debugging methodology.

lgbarn/shipyard · 37 tokens

ia-bug-reproduction-validator

Validates, reproduces, and root-cause analyzes bug reports (does not fix). Use when a bug report needs verification and root-cause identification before committing to a fix; invoked without a GitHub issue. For issue-linked reproduction use /ia-reproduce-bug.

iliaal/whetstone · 62 tokens

bulwark-fix-validator

Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.

QBall-Inc/the-bulwark · 54 tokens