imbad0202__academic-research-skills/POSITIONING.md

Positioning

What this is

Academic Research Skills (ARS) is a source-available academic research copilot framework for noncommercial scholarly use. The reference distribution is a suite of Claude Code skills that assists human researchers through the full research-to-publication pipeline. Sibling distributions for other agent platforms (e.g. Codex) follow the same workflow content, the same human-in-the-loop design philosophy, and the same license terms; see CONTRIBUTING.md § Platform ports.

It is licensed under CC BY-NC 4.0. This is not an open source license — it restricts commercial use by design, to keep the tool free for academic communities.

What this is not

ARS is not an autonomous paper-writing system. It is not a replacement for the researcher. It does not claim authorship, and its outputs are not submission-ready without human review.

Rejected mechanisms (autonomous-research anti-patterns)

These are not "out of scope" footnotes. They are the load-bearing boundary that defines what ARS does NOT do, and would not do even if a future system made them feasible. Each is the kind of autonomous mechanism catalogued by Kong et al. (2026), AI for Auto-Research: Roadmap & User Guide (arXiv:2605.18661), and rejected against the human-led positioning above. The recorded review test for all five — "who controls the next research-state transition?" — lives in the L1 design lesson.

• End-to-end autonomous research pipeline (Kong §7.4.8). A system that carries a project from question to manuscript without scholar confirmation at each state transition. Rejected: the scholar would become a reviewer of AI output, not the author. The pipeline's mandatory checkpoints exist precisely to prevent this.
• Idea-generation agent (Kong §3.1). An agent that proposes research hypotheses or questions for the scholar. Rejected — and distinct from the shipped wording-pattern advisory (#257): ARS may flag surface-level wording / framing patterns in a scholar-supplied research question and ask a Socratic follow-up, but it must not propose, substitute, rank, expand, or select research hypotheses or questions for the scholar. The boundary is recorded in the L2 design lesson.
• Paper2X auto-generation (Kong §6). Autonomous generation of slides / posters / video from a manuscript. Rejected — and distinct from a fidelity audit: ARS may audit an already-authored or externally generated dissemination artifact against the manuscript for fidelity, but it must not transform a manuscript into a dissemination artifact by choosing the content, narrative, layout, or output medium itself. (Dissemination design is handled by separate, non-ARS skill chains; the fidelity-audit suggestion itself is out of this repo's scope.)
• Autonomous experiment execution / coding (Kong §3.3). An LLM that runs experiments or code without scholar oversight. Rejected — and distinct from the shipped Experiment Provenance Intake (#260): ARS may ingest scholar-declared external experiment provenance and check manuscript claims against the declared results, but it must not initiate, run, modify, iterate, or treat tool-executed experiment / code outputs as evidence inside the pipeline.
• Physical wet-lab automation API (Kong §7.4.6). An interface that drives liquid handlers or automated labs. Rejected: even with safeguards, this extends beyond a research copilot's scope into laboratory infrastructure, and conflicts with the copilot-not-pilot positioning.

These are first-party scope boundaries and review criteria for future changes, not runtime guarantees. First-party ARS treats each as out of scope; adding one would require changing this recorded boundary, not merely adding a feature.

Recorded non-goals (scope boundaries without a mechanism)

Unlike the Rejected mechanisms above — capabilities ARS refuses on principle — these are lifecycle stages and state layers ARS deliberately does not enter. They were adjudicated out of scope in the 2026-06-10 researcher-blindspot audit and are recorded here so the boundary is reviewable, not improvised (the same recording discipline as the Rejected mechanisms; boundary + review criterion, not a runtime guarantee).

• Post-publication lifecycle. Tracking citation contexts of the scholar's own published papers, errata/corrigenda workflows, and OA self-archiving compliance are out of scope. ARS's front is research-to-publication; what happens to a paper after it ships belongs to the scholar and their institutional tooling. The existing monitoring_agent is unaffected — it alerts on developments in the cited literature (an input to current work), not on the scholar's own published output. Review criterion: a proposed feature whose value begins after the manuscript is accepted extends the front, and requires changing this recorded boundary first.
• Research-program-level state. ARS keeps no memory across papers: no registry of the scholar's prior claims, no carried-forward limitations list, no reviewer-history profile. The per-paper Material Passport remains the only state carrier, and every run starts from what the scholar explicitly feeds it. This is a deliberate consequence of the anti-leakage philosophy — gates that trusted an ambient cross-paper memory would be evaluating state nobody declared this run. The supported way for a returning author to carry their own prior work forward without any new mechanism is the Cross-paper workflow guide. Review criterion: a proposed feature that reads or writes scholar state outside the current run's passport crosses this boundary.

Allowed uses

• Research assistance: literature search, source verification, citation checking
• Teaching: demonstrating research methodology, peer review processes, academic writing standards
• Method training: using Socratic modes to develop research question formulation and argumentation skills
• Noncommercial academic collaboration: research groups, labs, departments using the tool for shared workflows

Discouraged uses

• Submitting AI-generated papers as solely human-authored without disclosing AI assistance
• Using the tool to produce papers without engaging with the content (the pipeline has mandatory checkpoints specifically to prevent this)
• Treating AI-generated review feedback as a substitute for actual peer review

Prohibited uses (per license)

• Commercial SaaS or hosted services built on ARS
• Consulting or freelance services that package ARS as a paid product
• Enterprise or institutional paid deployments without separate licensing
• Commercial API wrappers or resale of ARS functionality

These reflect our policy intent. See the CC BY-NC 4.0 license for the precise legal terms. For commercial licensing inquiries, contact the maintainer.

Design philosophy

Assistive, not deceptive. ARS helps you write better, not hide that you used AI.

• Style Calibration learns your voice from past papers — so the output sounds like you, not like a machine
• Writing Quality Check catches AI-typical patterns — to improve prose quality, not evade detection
• Disclosure Mode generates venue-specific or policy-anchor AI usage statements — because transparency is the standard

Human-in-the-loop, always. The pipeline's checkpoint system is mandatory by design:

• FULL checkpoints present all deliverables and require explicit user confirmation
• MANDATORY checkpoints at integrity gates and review decisions cannot be skipped
• "Full mode" means full-pipeline execution, not full autonomy — the human decides at every gate
• Max 2 revision loops, after which remaining issues become "Acknowledged Limitations" rather than being silently resolved

Failure modes are made visible, not hidden. The 7-mode AI Research Failure Mode Checklist (v3.2) and Reviewer Calibration Mode exist so that users can see where the AI might be wrong — not so that the AI can claim it's always right. The v3.7.3 + v3.8 L3 claim-faithfulness gate adds per-citation locator anchors and an opt-in audit pass that verifies whether each cited source actually supports the claim made of it.

Boundaries are recorded, not improvised. When adopting a capability from a published system would touch a load-bearing boundary — who ranks, what propagates, who writes state — the decision of whether and how to adopt it is written down as a design-lesson doc, so the same boundary is applied consistently later. The Co-Scientist (Gottweis et al. 2026) analysis is recorded in four such docs: hidden-ranking vs. advisory ranking (L1), unapproved feedback propagation (L2), which mechanisms transfer to ARS and which do not (L3), and control-plane ownership — who may write, rank, or route (L4). The Kong (2026) auto-research analysis adds two: copilot vs. auto-research as a research-state-authority line (L1) and advisory-on-wording vs. idea-generation (L2); the autonomous mechanisms they reject are enumerated in Rejected mechanisms above.

Citing this tool

If you use ARS in your research, please cite it:

Wu, C.-I. (2026). Academic Research Skills for Claude Code (Version 3.8) [Computer software]. https://github.com/Imbad0202/academic-research-skills