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    Literature Discovery

    2

    Systematic discovery and landscape mapping of scientific literature with citation analysis and venue quality filtering.

    Free

    12 installsSecurity scanned
    literature-discovery

    literature-discovery

    Example session with this skill installed

    Use the literature-discovery skill to map recent research on retrieval-augmented generation (RAG) for enterprise knowledge bases, focusing on the last 5 years and Q1/Q2 venues.

    • Read your context and instructions
    • Compiled the literature-discovery

    Research clusters

    ClusterFocusLeading venuesMaturity
    Retrieval architecturesDense/sparse hybrid, late interactionSIGIR, ACL, EMNLPMature
    Enterprise groundingDomain adaptation, access controlKDD, VLDB, ICMLGrowing
    Evaluation & benchmarkingRAG-specific metrics, human evalACL, TACLEmerging
    Long-context vs RAGContext windows replacing retrievalICLR, NeurIPSHot

    Seminal papers

    1. Lewis et al. (2020) — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (foundational)
    2. Karpukhin et al. (2020) — Dense Passage Retrieval
    3. Izacard & Grave (2021) — Fusion-in-Decoder
    4. Gao et al. (2023) — RAG survey + benchmarks (ACL)
    5. Asai et al. (2024) — Self-RAG / adaptive retrieval

    Leading authors, datasets & metrics

    • Institutions: Meta AI, Stanford, U. Waterloo, Berkeley, Tsinghua
    • Datasets: Natural Questions, MS MARCO, KILT, HotpotQA, FinanceBench
    • Metrics: EM/F1, recall@k, faithfulness, citation precision

    Trend

    Shift from "bigger retriever" toward

    adaptive retrieval + factual-grounding evaluation, especially for private enterprise corpora.

    Next steps

    • Run state-of-the-art on the 8 seminal papers for a narrative review.
    • Run research-gap to confirm the evaluation gap before committing.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Starting a new research project and don't know where the field stands? This skill discovers and maps the scientific literature for any topic before you commit.

    What it does:

    • Searches recent publications (configurable 5–10 year window)
    • Prioritizes Q1/Q2 journals and top conferences
    • Identifies seminal papers and groups results into research clusters
    • Detects trends and maps leading authors, institutions, datasets, tools, and metrics

    What you get:

    A structured literature map instead of an unstructured pile of PDFs, so you can see who's working on what, what's been done, and where the field is heading.

    Why buy it:

    It replaces hours of manual searching and reading with a ready-made expert workflow. Pair it with "state-of-the-art" and "research gap" to go from "I have a topic" to "I have a defensible contribution."

    • Conducting a systematic literature review for a thesis or grant proposal.
    • Onboarding a research team to a new technical domain or subfield.
    • Identifying potential collaborators and high-impact venues for publication.
    • Benchmarking new experimental results against established community datasets and metrics.

    Known limitations

    Initial search accuracy depends on the precision of the provided research topic and time window. The skill may have a language bias toward English-language publications, but I have tried with Spanish, and it worked very well too.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Listed15 days ago
    Updated12 days ago

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