Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add AppsVortex/arness/plugin install arn-sparkWrote 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/appsvortex/arness/arn-spark-feature-extract)<a href="https://agentmods.dev/skills/appsvortex/arness/arn-spark-feature-extract"><img src="https://agentmods.dev/badge/skills/appsvortex/arness/arn-spark-feature-extract/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/appsvortex/arness/arn-spark-feature-extract"><img src="https://agentmods.dev/badge/skills/appsvortex/arness/arn-spark-feature-extract.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 | $0.00131 | $0.08785 |
| Opus 5.5 | $0.00052 | $0.03514 |
| Sonnet 5.5 | $0.00026 | $0.01757 |
| Haiku 4.5 | $0.00013 | $0.00878 |
Grade A, and why
arn-spark-feature-extract 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 — 484 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Feature Extract
Extract a structured, prioritized feature backlog from all available project artifacts through guided conversation, aided by the arn-spark-product-strategist and arn-spark-ux-specialist agents (greenfield agents in this plugin) for feature analysis, prioritization, and UX flow analysis. This is a conversational skill that runs in normal conversation (NOT plan mode). The primary artifact is a feature backlog document that feeds into arn-code-feature-spec.
This skill bridges the greenfield pipeline into Arness's existing development pipeline. It takes all upstream artifacts -- product concept, architecture vision, spike results, style brief, static prototype validation (including showcase and debate reports), clickable prototype validation (including showcase, screen manifest, and debate reports), use cases (including postconditions, business rules, and debate reports), and visual grounding assets -- and distills them into a prioritized list of features with a local Feature Tracker for dependency management.
Each feature carries lean context (description, journey summary, UI behavior unique to the feature, validated components, use case references, debate insights, technical notes, acceptance criteria) with references to upstream UC documents. arn-code-feature-spec expands these references at spec time by reading the referenced UC documents directly.
Optionally, features can be uploaded as issues to the configured issue tracker (GitHub or Jira) for team visibility and tracking.
Prerequisites
At minimum, a product concept document should exist. Check in order:
- Read the project's
CLAUDE.mdfor a## Arnesssection. If found, check the configured Vision directory forproduct-concept.md - If no
## Arnesssection found, check.arness/vision/product-concept.mdat the project root
If a product concept is found: Read it as the primary source for feature extraction.
If no product concept is found: Ask the user: "No product concept found. I can extract features from your description of the product. What are the main things users should be able to do?"
What ships with it
2 files 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.
- today First seen · 484 lines · 131 tokens per session scan A 4a38de2b42db
arn-spark-feature-extract is a skill published in the GitHub repository AppsVortex/arness (33 stars, last pushed today), licensed MIT. It adds 131 tokens to every session and 8,785 once invoked, about $0.0005 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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