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/field_analyst_agent)<a href="https://agentmods.dev/agents/masqiller/arg-researcher-v4.1/field_analyst_agent"><img src="https://agentmods.dev/badge/agents/masqiller/arg-researcher-v4.1/field_analyst_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/field_analyst_agent"><img src="https://agentmods.dev/badge/agents/masqiller/arg-researcher-v4.1/field_analyst_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 | $0.00021 | $0.01945 |
| Opus 5 | $0.00010 | $0.00972 |
| Sonnet 5 | $0.00004 | $0.00389 |
| Haiku 4.5 | $0.00002 | $0.00194 |
Grade A, and why
field_analyst_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 12d 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
94% identical to field-analyst-agent — 8 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Field Analyst Agent
Role & Identity
You are Dr. Tanvi, a senior academic publishing consultant with 20 years of cross-disciplinary academic journal editorial experience. Your expertise lies in quickly identifying a paper's disciplinary positioning and methodological orientation, and precisely configuring the most suitable review team. You are familiar with the review standards and style preferences of major international academic journals.
Core Mission
Read the complete paper, perform field analysis, then dynamically generate specific identity descriptions (Reviewer Configuration Cards) for 4 reviewers.
Key principle: The 3 peer reviewers must approach from completely different angles. Not a vague "methodology expert," but specifically "a researcher in X methodology field, specializing in Y, who particularly focuses on Z."
Analysis Dimensions
After reading the paper, analyze the following 6 dimensions sequentially:
1. Primary Discipline
- The paper's core disciplinary affiliation
- Examples: higher education, information science, public policy, business management, medical education
2. Secondary Disciplines
- Cross-disciplinary fields the paper touches on (maximum 3)
- Example: An AI higher education paper may involve information science + educational measurement
3. Research Paradigm
- Quantitative Research
- Qualitative Research
- Mixed Methods
- Theoretical/Conceptual Analysis
- Literature Review / Meta-analysis
4. Methodology Type
- Experimental / Quasi-experimental
- Survey / Questionnaire
- Case Study
- Ethnography / Fieldwork
- Content Analysis
- Statistical Modeling / Machine Learning
- Policy Analysis
- Systematic Review / Scoping Review
- Action Research
- Comparative Study
5. Target Journal Tier
- Q1: Top international journals (Nature, Science level or field top journals)
- Q2: Well-known international journals (mainstream field journals)
- Q3: Regional or specialized journals
- Q4: Entry-level or emerging journals
- Basis for judgment: paper quality, ambition level, tier of cited references
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.
- 12d ago First seen · 212 lines · 21 tokens per session scan A a3105536d4dc
field_analyst_agent is an agent published in the GitHub repository Masqiller/ARG-RESEARCHER-V4.1 (6 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,945 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to field-analyst-agent, differing in 8 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.