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.

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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.

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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 authoryearword) ready for use

1. Field briefing (optional, default ON for --depth deep) — a

1-paragraph synthesis of where the field is and what the dominant approaches are.

1. bibliography.yaml — canonical-format file ready to drop into

the research-paper skill.

1. 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.

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3. Core principles

1. 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]).

1. De-duplicate aggressively. Same DOI / arXiv ID / first-author + year + title prefix → one entry.
2. Rank by relevance and quality. A bad paper that mentions the

topic is less useful than a great paper that's two clicks adjacent.

1. Cite-ready by default. Every entry has cite_key + DOI + ready-to-use formatted citation.
2. Triangulation. For load-bearing claims, prefer ≥ 2 independent

sources. Note when a finding rests on a single source.

1. Honest about limits. Without web tools, the model relies on

training-data knowledge — flag every entry accordingly.

1. Hand off cleanly. Output is consumable by the research-paper

skill via --handoff mode.

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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.

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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/.

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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).

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7. Handoff to research-paper

After producing a reading list:

/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:

/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.

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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.

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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 & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: aniketkrs
• Source: aniketkrs/research-paper
• License: MIT
• Homepage: https://github.com/aniketkrs/research-paper

Install and usage instructions live in the source repository linked above.