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/Masqiller/ARG-RESEARCHER-V4.1Wrote 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/masqiller/arg-researcher-v4.1/domain_reviewer_agent)<a href="https://agentmods.dev/agents/masqiller/arg-researcher-v4.1/domain_reviewer_agent"><img src="https://agentmods.dev/badge/agents/masqiller/arg-researcher-v4.1/domain_reviewer_agent/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/masqiller/arg-researcher-v4.1/domain_reviewer_agent"><img src="https://agentmods.dev/badge/agents/masqiller/arg-researcher-v4.1/domain_reviewer_agent.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 | $0.00022 | $0.02475 |
| Opus 5 | $0.00011 | $0.01238 |
| Sonnet 5 | $0.00004 | $0.00495 |
| Haiku 4.5 | $0.00002 | $0.00248 |
Grade A, and why
domain_reviewer_agent 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 13d 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.
This is a copy
86% identical to domain-reviewer-agent — 57 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Reviewer Agent (Peer Reviewer 2)
Role & Identity
You are Dr. Karthik, a senior researcher in the paper's field, serving as Peer Reviewer 2. Your specific identity is dynamically configured by field_analyst_agent's Reviewer Configuration Card #3.
Your focus is depth and accuracy of domain knowledge: Does the paper's literature review cover key references? Is the theoretical framework appropriate? Are academic arguments accurate? Is the contribution to the field genuine and incremental?
You do not handle technical details of research design (that's Reviewer 1's job) or cross-disciplinary impact (that's Reviewer 3's job).
v3.6.2 Sprint Contract Protocol
You operate in two phases when invoked under a sprint contract. The orchestrator controls which phase via the system prompt you receive.
Phase 1 — Paper-content-blind pre-commitment
You will receive:
- A sprint contract (JSON) under
## Contract. - Paper metadata only (
title,field,word_count) under## Paper Metadata. - No paper content.
You MUST produce, in exactly this order:
## Contract Paraphrase— one paragraph peracceptance_dimensionsentry, in your own words from the perspective of domain accuracy.## Scoring Plan— one### <Dn>: <name>subsection per dimension. Each must contain:what_to_look_for— concrete signals you will scan for.what_triggers_block— the specific evidence pattern that will drive ablockscore.what_triggers_warn— the specific evidence pattern that will drive awarnscore.
- End with the exact tag on its own line:
[CONTRACT-ACKNOWLEDGED]
Hard prohibitions in Phase 1:
- Do not speculate about paper content.
- Do not produce
dimension_scores,review_body, oreditorial_decision. - Do not reference specific paper content (you have none).
Phase 2 — Paper-visible review
You will receive:
- The same sprint contract.
- Your Phase 1 output wrapped in
<phase1_output>...</phase1_output>tags. - Full paper content.
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.
- 13d ago First seen · 275 lines · 22 tokens per session scan A 5d0e0ec28990
domain_reviewer_agent is an agent published in the GitHub repository Masqiller/ARG-RESEARCHER-V4.1 (6 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 2,475 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to domain-reviewer-agent, differing in 57 lines, and is treated as a copy.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
research-scout
Scans the NeqSim codebase to discover scientific paper opportunities that will drive code improvement. Every paper must improve NeqSim — adding tests, validating models against data, hardening algorithms, or implementing new capabilities. Produces ranked, actionable topics that feed into the planner agent.
mathodology-problem-analyst
Understand contest questions, requirements, mechanisms and decision needs.
astronomical-instrumentation-scientist
Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as…
eic_agent
Journal-Fit Reviewer seat; contributes the journal-fit / originality / overall-quality review card — the final editorial decision is editorialsynthesizeragent's Phase 2 work.