Academic Research

academic-research

A routing-first front door for the full Academic Research Skills (ARS) suite — 4 pipelines, 27 modes, spanning the complete research-to-publication lifecycle.

AI is copilot, not pilot. ARS handles the grunt work (hunting references, verifying citations, checking logical consistency, formatting). You handle the parts that require your brain: defining the question, choosing the method, interpreting results, and writing the sentence after "I argue that."

Read these pipeline references before executing:

• references/deep-research-pipeline.md
• references/academic-paper-pipeline.md
• references/academic-paper-reviewer-pipeline.md
• references/academic-pipeline-stages.md

Plugin Installation

# Full upstream suite (all 4 skills, 27 modes — recommended for heavy use)
claude plugin marketplace add Imbad0202/academic-research-skills

# Or install this routing skill only via jeo-skills
npx skills add https://github.com/akillness/jeo-skills --skill academic-research

When to use this skill

• Conducting rigorous literature research on a topic (→ deep-research pipeline)
• Writing a new academic paper from scratch or guided outline (→ academic-paper pipeline)
• Reviewing or evaluating an existing paper (→ academic-paper-reviewer pipeline)
• Orchestrating the full research-to-publication workflow end-to-end (→ academic-pipeline)
• Fact-checking claims in a paper or source (→ deep-research / fact-check mode)
• Preparing for peer review, rebuttal, or journal revision (→ academic-paper / revision modes)
• Running a PRISMA 2020 systematic review (→ deep-research / systematic-review mode)
• Generating a bilingual abstract, converting citations, checking AI disclosure requirements

When not to use this skill

• The request is a code documentation or API reference → use technical-writing
• The request is an ML experiment loop (training/eval) → use autoresearch
• The request is a general writing task (blogs, marketing, newsletters) → use marketing-automation
• The request needs autonomous agent ML training → use autoresearch
• The user already has a paper draft and just needs copy editing → use technical-writing or technical-writing

Required intake packet

Before routing, identify:

1. Pipeline — which of the 4 pipelines fits (deep-research / academic-paper / academic-paper-reviewer / academic-pipeline)
2. Mode — which specific mode within that pipeline (see routing table below)
3. Topic or input — the research topic, paper text, or reviewer comments to work from
4. Oversight preference — Very High (guided/Socratic) → High (key decisions) → Medium (structured) → Low (template)
5. Format / venue — APA 7.0, IEEE, LaTeX, NeurIPS, ICLR, CVPR, or journal target

Pipeline & Mode Routing Table

Instructions

Step 1: Pick the pipeline

deep-research        → topic investigation, literature synthesis, fact-checking, PRISMA
academic-paper       → write, outline, revise, abstract, format, rebuttal, disclosure
academic-paper-reviewer → evaluate, review, calibrate reviewer quality
academic-pipeline    → full orchestrated 10-stage research → paper → review → revise → finalize

If ambiguous between deep-research and academic-paper: the user doing discovery → deep-research; the user writing or revising a document → academic-paper.

Step 2: Confirm the mode and input

• State the chosen pipeline and mode explicitly before proceeding
• For socratic or plan modes: begin Socratic dialogue immediately (one question at a time)
• For full or revision modes: confirm the topic/paper and output format first
• For citation-check or fact-check: confirm which claims or sections to target

Step 3: Execute with human-in-the-loop

Follow the pipeline reference for the chosen mode:

• deep-research: Read references/deep-research-pipeline.md
• academic-paper: Read references/academic-paper-pipeline.md
• academic-paper-reviewer: Read references/academic-paper-reviewer-pipeline.md
• academic-pipeline (10-stage): Read references/academic-pipeline-stages.md

Checkpoints marked [USER CHECKPOINT] require explicit user confirmation before continuing. Never skip them.

Step 4: Return a structured output packet

Every completed mode should return:

Pipeline:     <which pipeline>
Mode:         <which mode>
Topic/Input:  <research topic or paper title>
Output:       <the primary artifact — report / draft / review / outline>
Citations:    <APA 7.0 / IEEE / inline — as applicable>
Integrity:    <claim verification status if fact-check or citation-check was run>
Next step:    <recommended follow-up mode or pipeline>

Data access and integrity principles

• data_access_level: verified_only — ARS never uses unverified claims in outputs
• task_type: open-ended — all modes are open-ended; ARS does not run experiments
• Citation hallucination guard: three-index triangulation (Semantic Scholar + OpenAlex + Crossref) in academic-pipeline Stage 6/7
• Experiment provenance: claims backed by experiments must be declared in the Material Passport; ARS audits alignment (ALIGNED / OVERSTATED / NOT_SUPPORTED_BY_PROVENANCE)
• Style Calibration: academic-paper plan mode can learn your voice from past work samples

Route-out map