ARS Skill Library v3.9.4.2 Integration Complete
Four Major Research Skills Officially Launched
ResearchLinkAI announces the completion of the internal integration of Academic Research Skills (ARS) v3.9.4.2. Featuring 4 major skills · 23 operating modes · 33 built-in Agents, covering deep research, paper writing, simulated peer review, and end-to-end pipelines, it has officially become the "standard arsenal" of the company's AI-native research system. This marks a critical upgrade to the company's AI research capabilities in 2026.
On May 23, 2026, ResearchLinkAI completed a critical integration of its internal AI research capability stack—ARS (Academic Research Skills) v3.9.4.2 has been officially connected to the company's role system. ARS is not a new role, but rather a cross-role "shared toolkit": research workflows previously scattered across bioinformatics, pharmacy, medicine, CS-AI, control systems, and data science have been consolidated into four standardized, reusable, mode-driven skills.
The completion of this integration means: all research AIs can now invoke the same high-quality research pipeline on demand—clients will no longer receive the "personal improvisation" of a single AI researcher, but rather a strictly orchestrated, reproducible, and auditable research workflow.
What is ARS
ARS (Academic Research Skills) is an academic research skill library designed for AI Agents. It does not change "who conducts the research," but it transforms "how research is conducted"— encapsulating tasks such as "conducting reviews," "writing papers," "reviewing manuscripts," and "running end-to-end pipelines" into standardized skills that can be invoked on demand by researcher AIs in corresponding fields.
The core feature of ARS is being "mode-driven": each skill incorporates multiple modes, ranging from 30-minute quick summaries to systematic reviews exceeding 5,000 words, and from rapid assessments by a single reviewer to full simulated peer reviews involving five reviewers plus editorial decisions. The CEO or researchers simply need to select the appropriate mode to trigger the corresponding Agent collaboration flow.
Four Skills in Detail
deep-research
13-Agent Deep Research Team · 7 Modes
Designed for systematic reviews, in-depth literature analysis, and Socratic topic selection, covering all intensity levels from 30-minute quick summaries to PRISMA 2020 systematic reviews.
academic-paper
12-Agent Paper Writing Team · 10 Modes
A high-quality paper writing pipeline covering scenarios such as complete IMRaD drafts, section-level planning, revisions and point-by-point responses based on reviewer comments, citation error checking, and format conversion.
academic-paper-reviewer
7-Agent Peer Review Team · 6 Modes
Multi-perspective simulated peer review, ranging from editor-in-chief quick assessments to independent reviews by five reviewers plus editorial decisions and revision roadmaps. A "stress test" tool for pre-submission preparation.
academic-pipeline
Full-process Scheduler · 10-stage End-to-End
An end-to-end pipeline from research to paper. The CEO proposes the direction, and the system automatically coordinates Agents from the three major skills above to output a complete chain from project initiation to submission-ready manuscript.
How It Is Invoked
ARS does not replace any role but exists as a "copilot for researchers." It is triggered by explicit instructions from the CEO—for example, "Use ARS to conduct a simulated peer review on this paper." The corresponding researcher AI will then read the skill definition according to integration instructions, select a mode, schedule built-in Agents, and ultimately write the output to the project directory.
Scheduling keywords include:
ARS Deep Research · Systematic ReviewARS Paper Writing · ARS draftARS Simulated Review · peer reviewARS Full Pipeline · End-to-EndIron Rules & Boundaries
To prevent misuse of the skill library, the integration documentation establishes six inviolable iron rules for ARS:
Iron Rule 01 · Explicit CEO Trigger
ARS must not be activated automatically without explicit designation by the CEO. Routine lightweight tasks follow existing SOPs.
Iron Rule 02 · Strategy Routing Priority
Topic selection must first go through strategy routing (A–E); ARS only assists in execution after the strategy is determined.
Iron Rule 03 · No Replacement of Human Judgment
ARS is a copilot, not the captain; critical decisions remain with the CEO.
Iron Rule 04 · Unchanged Output Paths
All files are written to the standard shared/projects/ directory for traceability and auditing.
Iron Rule 05 · Citation Authenticity
No fabrication of references; all citations must be verifiable. This is the strictest built-in constraint of ARS.
Iron Rule 06 · Academic Integrity
Using ARS must comply with the AI assistance disclosure requirements of target journals; no disclosure obligations may be bypassed.
Business Impact
Internal: Researchers Upgrade from "Solo Act" to "Orchestra"
The content a single AI researcher can produce at one time is limited. ARS enables a single researcher to coordinate 33 specialized Agents working collaboratively— effectively upgrading past "individual writing" to an ensemble performance featuring an "editorial board + review panel + editor-in-chief." Paper quality, citation accuracy, and revision efficiency are all simultaneously improved.
For Clients: From "Per-instance Service" to "Process-based Delivery"
High-ticket categories in our Taobao store such as "Bioinformatics Analysis," "Medical Statistics," "Molecular Docking," and "Algorithm Development" will all be driven by standard ARS workflows at their core. Clients are no longer purchasing "a single instance of AI generation," but rather a versioned, auditable, and reproducible research pipeline.
For the Industry: A Complete Panorama of Research Skills
ARS will serve as the "reference baseline" for ResearchLinkAI in the ARA evaluation channel—other tools can be scored against ARS, providing a complete and open capability coordinate system for the AI research skills ecosystem.
Next Steps
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Practical Application: The peer review for the CAI-Ethics project and the final revisions for the OA osteoarthritis paper were both successfully executed using ARS modes, verifying integration stability.
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Client Productization: Package "Running a full ARS pipeline for a paper" as a high-ticket standardized service and list it on the official website and Taobao store product pages.
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Content Output: A dedicated episode in the ARA evaluation series will present a horizontal comparison between ARS and other Skills, allowing external users to clearly see "what the tools we sell look like."
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Continuous Upgrades: The upstream ARS repository remains updatable via git pull, and the company will regularly incorporate new modes and Agents into the internal skill library.