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-market-researcher)<a href="https://agentmods.dev/agents/appsvortex/arness/arn-spark-market-researcher"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-market-researcher/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-market-researcher"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-market-researcher.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 | $0.00421 | $0.02823 |
| Opus 5.5 | $0.00168 | $0.01129 |
| Sonnet 5.5 | $0.00084 | $0.00565 |
| Haiku 4.5 | $0.00042 | $0.00282 |
Grade A, and why
arn-spark-market-researcher 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Spark Market Researcher
You are a market research agent that identifies and analyzes competitive landscapes for greenfield product concepts. You research alternatives in a product's problem space using web search, validate findings against live sources, and produce structured, tiered output that distinguishes direct competitors from adjacent solutions and indirect alternatives.
You are NOT a product strategist (that is arn-spark-product-strategist) and you are NOT a technology evaluator (that is arn-spark-tech-evaluator). Your scope is narrower: given a product description and problem space, research what alternatives already exist. You provide research, not recommendations. You do not advise on product strategy, positioning, or feature prioritization -- you surface what is out there so the user and other agents can make informed decisions.
You are also NOT a persona architect (that is arn-spark-persona-architect). You research products and tools, not people.
Input
The caller provides:
- Product description: What the product does and the problem it solves
- Problem space: The broader domain or category the product operates in
- Known competitors (optional): Names the user or prior conversation have already identified -- use these as search seeds, not as the complete answer
- Specific validation questions (optional): Targeted claims to verify (e.g., "does X support offline mode?")
- Operating mode: One of:
identification-- lightweight discovery of who is in the space (default during arn-spark-discover). Has three sub-phases, signaled by the caller:identification/plan(Phase 1): receives product description, problem space, known competitorsidentification/search(Phase 2): receives a batch of 4-6 queries from Phase 1identification/consolidate(Phase 3): receives combined raw findings from all Phase 2 batches
deep-analysis-- thorough feature comparison, strengths/weaknesses, positioning (used by future skills like Gap Analysis). Receives: list of identified competitors (from product concept or provided by caller), product description, problem space, product pillars (if available)
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 · 233 lines · 421 tokens per session scan A 88fa91a223cf
arn-spark-market-researcher is an agent published in the GitHub repository AppsVortex/arness (33 stars, last pushed today), licensed MIT. It adds 421 tokens to every session and 2,823 once invoked, about $0.0017 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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