# Get Research Paper > Discovers, retrieves, ranks, and summarizes real existing research papers on any topic. Searches arXiv, Google Scholar, PubMed, Semantic Scholar, and reputable open repositories; returns a curated reading list with verified DOIs, key findings, and citation-ready metadata. Activates on slash commands (`/get-research-paper`, `/find-paper`, `/fetch-paper`, `/papers-on`, `/scholar`) and natural-language requests like "get research paper on …", "find papers about …", "what are the top papers on …". Hands off cleanly to the `research-paper` skill for paper writing. Runtime-neutral — works with Claude Code, OpenCode, Cursor, Cline, Codex, Aider, Amp, and 50+ agents. Use when this capability is needed. - Skill: `tomevault-io/get-research-paper` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/get-research-paper` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/get-research-paper/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/get-research-paper --- # Get Research Paper A research-discovery skill. Where the **`research-paper`** skill *writes* papers, this skill **finds** them. Give it a topic, get a ranked, de-duplicated reading list of real existing papers with verified DOIs, key findings, and ready-to-cite metadata. This file is the **entry point**. Heavier guidance (per-source strategies, ranking criteria, summarization prompts) lives in topic folders and is loaded **on demand**. --- ## 1. When to activate ### Slash commands | Command | What it does | | ------------------------------ | ------------------------------------------------- | | `/get-research-paper ` | Curated reading list (default 10 papers) | | `/find-paper ` | Alias for `/get-research-paper` | | `/find-papers ` | Alias for `/get-research-paper` | | `/fetch-paper ` | Alias for `/get-research-paper` | | `/papers-on ` | Alias for `/get-research-paper` | | `/scholar ` | Quick scholarly summary (5 papers, 2-line summaries) | Common options: - `--n ` — number of papers (default 10). - `--years ` — e.g. `2020-2024`, `last-5`, `since-2018`. - `--source ` — `arxiv`, `scholar`, `pubmed`, `semantic-scholar`, `all` (default). - `--depth ` — summary detail. - `--style ` — pre-format the bibliography. - `--audience ` — adjust summary register. - `--handoff` — emit a `bibliography.yaml` ready for the `research-paper` skill. ### Natural-language patterns - "get research paper on / about / for [topic]" - "find research papers on [topic]" - "find papers on / about [topic]" - "what are the top papers on [topic]" - "show me research on [topic]" - "fetch papers about [topic]" - "list papers on [topic]" - "literature on [topic]" (shorter than `/literature-review`) - "scholar [topic]" ### Negative activation Do NOT activate for: - Requests to **write** a paper (route to `research-paper`). - Requests to **review** or **critique** a draft (route to `research-paper`). - Casual questions ("what is X?") that don't need scholarly sources. - Pure code / API documentation lookup. --- ## 2. Output contract Every run produces, at minimum: 1. **Reading list** — N papers with: - Title (full) - Authors (first 3 + "et al." if more) - Year - Venue / journal / preprint server - DOI / arXiv ID / URL - 2–4 sentence **summary** (problem → method → finding → significance) - **Relevance score** (1–5) and **quality score** (per `citation_engine` rubric) - **Cite key** (lowercase author_year_word) ready for use 2. **Field briefing** (optional, default ON for `--depth deep`) — a 1-paragraph synthesis of where the field is and what the dominant approaches are. 3. **`bibliography.yaml`** — canonical-format file ready to drop into the `research-paper` skill. 4. **`Known-gaps.md` block** — every paper that couldn't be verified is surfaced with severity and recommended fix. See `templates/reading-list.md`, `templates/paper-summary.md`, `templates/briefing.md`. --- ## 3. Core principles 0. **Anchor to TODAY's date FIRST.** Before any search, determine today's actual date (via `date -u +%Y-%m-%d`, runtime context, or asking the user). **Never default to training-cutoff dates.** Year ranges like `--years last-3` are computed from today. Full protocol: `instructions/freshness.md`. 1. **Real papers only.** Never invent papers, DOIs, authors, or findings. Use only sources the model can verify (or honestly mark `[UNVERIFIED — offline]`). 2. **De-duplicate aggressively.** Same DOI / arXiv ID / first-author + year + title prefix → one entry. 3. **Rank by relevance and quality.** A bad paper that mentions the topic is less useful than a great paper that's two clicks adjacent. 4. **Cite-ready by default.** Every entry has cite_key + DOI + ready-to-use formatted citation. 5. **Triangulation.** For load-bearing claims, prefer ≥ 2 independent sources. Note when a finding rests on a single source. 6. **Honest about limits.** Without web tools, the model relies on training-data knowledge — flag every entry accordingly. 7. **Hand off cleanly.** Output is consumable by the `research-paper` skill via `--handoff` mode. --- ## 4. Top-level workflow ``` intake → search-strategy → fan-out search → rank+dedupe → verify → summarize → assemble briefing → output (+ optional handoff) ``` Each step has a dedicated playbook. Read the file for the step you're on; persist the artifact; move on. Master pipeline: **`workflows/search.md`**. --- ## 5. Source coverage | Source | When to prefer | Tool | | --------------------- | ----------------------------------------- | -------------------------------------------- | | **arXiv** | CS, ML, AI, physics, math, quant-bio | `toolchains/arxiv_search.py` (works offline-only via API) | | **Google Scholar** | Generic / cross-discipline broad surveys | `WebSearch` with `site:scholar.google.com` | | **Semantic Scholar** | API-friendly, citation graph, summaries | `WebFetch` of api.semanticscholar.org | | **PubMed / PubMed Central** | Biomedical, life sciences | `WebFetch` of eutils.ncbi.nlm.nih.gov | | **DBLP** | CS authors / venues / publication lists | `WebFetch` of dblp.org | | **ACM DL** | HCI, systems, security, networks | `WebSearch` with `site:dl.acm.org` | | **IEEE Xplore** | Engineering, signal, hardware | `WebSearch` with `site:ieeexplore.ieee.org` | | **OpenReview** | NeurIPS, ICLR, ICML reviews + papers | `WebFetch` of openreview.net | | **Crossref** | DOI verification + metadata fill-in | `WebFetch` of api.crossref.org | | **Retraction Watch** | Retraction screening | `WebFetch` of retractionwatch.com / database | Per-source strategy details: `sources/`. --- ## 6. Ranking and quality Each candidate paper is scored on: - **Authority (0–4)** — venue quality (peer-review rigor, impact). - **Methodological rigor (0–3)** — replicability, sample size, sound stats. - **Recency / relevance (0–3)** — fresh + topical, OR foundational + canonical. - **Total (0–10)** — used to rank. Default reading lists keep papers scoring **≥ 5**. Higher floors raise the bar (`--quality-floor 7`). Full rubric: `prompts/ranking.md` (extends the `citation_engine/source-evaluation.md` of the `research-paper` skill). --- ## 7. Handoff to `research-paper` After producing a reading list: ```bash /get-research-paper "graph neural networks for fraud detection" \ --n 25 --handoff --style ieee --years 2020-2024 ``` Produces: ``` gnn-fraud-detection/ ├── reading-list.md # human-readable curated list ├── bibliography.yaml # ← canonical file for research-paper skill ├── briefing.md # 1-paragraph synthesis └── Known-gaps.md # any unverifiable items ``` The user then runs the writer skill with the produced bibliography: ```bash /research "graph neural networks for fraud detection" \ --style ieee --bibliography ./gnn-fraud-detection/bibliography.yaml ``` The writer reads the curated bibliography directly — no re-search needed. --- ## 8. Failure handling - **No web search available** → use model-known papers, mark every entry `[UNVERIFIED — offline]`, lower the recommended `--n` to 5–8, and surface the limitation in the briefing. - **Search returns nothing** → broaden the query (drop adjectives, try synonyms), then return what was found with an honest note. - **Conflicting metadata across sources** → prefer the published (peer-reviewed) version over the preprint; note the relationship. - **Retracted paper detected** → drop from the list; flag in `Known-gaps.md`. - **Out-of-scope topic** → surface a note in the briefing; deliver best-effort results. --- ## 9. Where to look next - **Plan a search** → `workflows/search.md` - **Per-source strategy** → `sources/` - **Ranking rubric** → `prompts/ranking.md` - **Summarization** → `prompts/summarization.md` - **Output templates** → `templates/` - **Hand off to writer** → `workflows/handoff-to-writer.md` - **arXiv search tool** → `toolchains/arxiv_search.py` This skill is intentionally smaller than the writer skill. Its job is discovery and curation; the heavy lifting (writing, methodology, review) lives in `research-paper`. --- > Source: [aniketkrs/research-paper](https://github.com/aniketkrs/research-paper) — distributed by [TomeVault](https://tomevault.io).