| name | academic-research |
| description | Full academic research pipeline from discovery to publication — 4 reference pipelines, 27 modes, 39-agent ensemble (deep-research 8 modes, academic-paper 11 modes, academic-paper-reviewer 6 modes, academic-pipeline 10-stage orchestrator). Routes each request to the right pipeline and mode. Human-in-the-loop throughout. Plugin (upstream): claude plugin marketplace add Imbad0202/academic-research-skills |
| allowed-tools | Bash Read Write Edit Glob Grep WebFetch |
| metadata | {"tags":"academic-research, deep-research, paper-writing, peer-review, literature-review, systematic-review, citation-check, research-pipeline, scholarly-publishing, ars","version":"1.0.0","source":"https://github.com/Imbad0202/academic-research-skills","upstream_version":"3.13.0","license":"CC-BY-NC-4.0"} |
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:
Plugin Installation
claude plugin marketplace add Imbad0202/academic-research-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:
- Pipeline — which of the 4 pipelines fits (deep-research / academic-paper / academic-paper-reviewer / academic-pipeline)
- Mode — which specific mode within that pipeline (see routing table below)
- Topic or input — the research topic, paper text, or reviewer comments to work from
- Oversight preference — Very High (guided/Socratic) → High (key decisions) → Medium (structured) → Low (template)
- Format / venue — APA 7.0, IEEE, LaTeX, NeurIPS, ICLR, CVPR, or journal target
Pipeline & Mode Routing Table
| What the user says | Pipeline | Mode | Oversight |
|---|
| "research [topic]", "deep research", "academic analysis" | deep-research | full | High |
| "quick brief", "30 minute summary" | deep-research | quick | Medium |
| "review this paper's research quality" | deep-research | review | High |
| "literature review", "annotated bibliography" | deep-research | lit-review | Medium |
| "WHY HOW WHAT papers", "3W scan", "compare papers" | deep-research | three-way-scan | Low |
| "verify claims", "fact-check", "evidence verification" | deep-research | fact-check | Medium |
| "guide my research", "help me think through" | deep-research | socratic | Very High |
| "systematic review", "meta-analysis", "PRISMA" | deep-research | systematic-review | Medium |
| "write a paper on X", "academic paper", "research paper" | academic-paper | full | High |
| "guide me through writing", "help me plan my paper" | academic-paper | plan | Very High |
| "build a paper outline" | academic-paper | outline-only | High |
| "I have reviewer comments", "revise my paper" | academic-paper | revision | High |
| "parse reviewer comments into a roadmap" | academic-paper | revision-coach | Medium |
| "write an abstract" | academic-paper | abstract-only | Medium |
| "turn this into a literature review paper" | academic-paper | lit-review | Medium |
| "convert to LaTeX", "convert citations to IEEE" | academic-paper | format-convert | Medium |
| "check citations" | academic-paper | citation-check | Medium |
| "generate AI disclosure statement for NeurIPS" | academic-paper | disclosure | Medium |
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:
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 outputstask_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
| If the user needs… | Route to |
|---|
| ML training experiment loop | autoresearch |
| Prompt / skill eval pipeline | skill-autoresearch |
| Engineering design doc or ADR | technical-writing |
| API portal or SDK reference | api-documentation |
| Karpathy-style ML research | autoresearch |
| General content / blog / newsletter | marketing-automation |
| Code documentation | technical-writing |
