/academic-research-mapper

name: academic-research-mapper
description: Map the research landscape for any technical or academic topic by searching arXiv, Semantic Scholar, and Google Scholar in parallel. Use when a developer, researcher, or engineer wants to understand what has been published, who the key authors are, which subtopics are active, and where the gaps still live. Runs parallel TinyFish agents across all three sources, deduplicates results, and synthesizes findings into a structured landscape report with a gap analysis. Trigger this skill whenever someone wants to survey a field, prepare a literature review, find underexplored research directions, or understand the state of the art before building something new.

Research Landscape Mapper — Understand a Field Before You Build or Write

You have access to the TinyFish CLI (`tinyfish`), a tool that runs browser automations from the terminal using natural language goals. This skill uses it to search arXiv, Semantic Scholar, and Google Scholar in parallel, then synthesizes results into a structured landscape report with identified gaps.

Pre-flight Check (REQUIRED)

Before making any TinyFish call, always run BOTH checks:

**1. CLI installed?**

bash/zsh:

which tinyfish && tinyfish --version || echo "TINYFISH_CLI_NOT_INSTALLED"

PowerShell:

Get-Command tinyfish; tinyfish --version

If not installed, stop and tell the user: > Install the TinyFish CLI: `npm install -g @tiny-fish/cli`

**2. Authenticated?**

tinyfish auth status

If not authenticated, stop and tell the user:

> You need a TinyFish API key. Get one at: https://agent.tinyfish.ai/api-keys > > Then authenticate: > > **Option 1 — CLI login (interactive):** > ``` > tinyfish auth login > ``` > > **Option 2 — bash/zsh (Mac/Linux, current session):** > ```bash > export TINYFISH_API_KEY="your-api-key-here" > ``` > > **Option 3 — bash/zsh (persist across sessions, add to ~/.bashrc or ~/.zshrc):** > ```bash > echo 'export TINYFISH_API_KEY="your-api-key-here"' >> ~/.zshrc > source ~/.zshrc > ``` > > **Option 4 — PowerShell (current session only):** > ```powershell > $env:TINYFISH_API_KEY="your-api-key-here" > ``` > > **Option 5 — Claude Code settings:** Add to `~/.claude/settings.local.json`: > ```json > { > "env": { > "TINYFISH_API_KEY": "your-api-key-here" > } > } > ```

Do NOT proceed until both checks pass.

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What This Skill Does

Given a research topic (e.g. *"retrieval-augmented generation"* or *"protein structure prediction"*), this skill:

1. Searches **arXiv** for preprints sorted by most recent — capturing what is being worked on right now 2. Searches **Semantic Scholar** for papers ranked by relevance with citation counts — identifying what the field considers important 3. Searches **Google Scholar** for broad coverage including published venues not yet on arXiv

It then deduplicates across all three sources by title similarity, clusters papers into subtopics, and synthesizes findings into a structured landscape report: what is well-studied, what is emerging, and where the gaps are.

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Core Command

tinyfish agent run --url <url> "<goal>"

Flags

| Flag | Purpose | |------|---------| | `--url <url>` | Target website URL for the agent to navigate | | `--sync` | Wait for the full result before returning (required when you need output before next step) | | `--async` | Submit and return a run ID immediately — use when firing parallel agents | | `--pretty` | Human-readable formatted output for debugging |

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Keyword Strategy

The quality of results depends entirely on your search terms. Before running anything, derive 2–3 keyword variants from the topic. Each source has different vocabulary norms — academic terms work best on Semantic Scholar, shorter compressed terms work best on arXiv.

| Topic | Primary keywords | Variant A | Variant B | |-------|-----------------|-----------|-----------| | Retrieval-augmented generation | `retrieval augmented generation` | `RAG language model` | `dense retrieval QA` | | Protein structure prediction | `protein structure prediction` | `AlphaFold protein folding` | `ab initio structure biology` | | Neural architecture search | `neural architecture search` | `NAS automated machine learning` | `hyperparameter optimization deep learning` | | Federated learning privacy | `federated learning` | `federated learning differential privacy` | `distributed training privacy` |

Use the primary keywords for the first parallel pass. If any source returns fewer than 5 results, run a second pass with the variant keywords on that source only.

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Step-by-Step Workflow

Step 1 — Derive keywords and build URLs

Before running any agents, construct all three search URLs. Do this in your head or in a scratch note — do not make TinyFish calls yet.

**arXiv URL pattern:**

https://arxiv.org/search/?query=<keywords>&searchtype=all&order=-announced_date_first

**Semantic Scholar URL pattern:**

https://www.semanticscholar.org/search?q=<keywords>&sort=Relevance

**Google Scholar URL pattern:**

https://scholar.google.com/scholar?q=<keywords>&as_sdt=0%2C5&hl=en

Replace `<keywords>` with URL-encoded primary keywords (spaces become `+`).

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Step 2 — Search all three sources in parallel

Fire all three agents simultaneously. Do NOT wait for one to finish before starting the next.

**arXiv — sorted by most recent:**

tinyfish agent run --sync \
  --url "https://arxiv.org/search/?query=retrieval+augmented+generation&searchtype=all&order=-announced_date_first" \
  "Extract the top 15 search results as JSON: [{\"title\": str, \"authors\": [str], \"year\": str, \"abstract_snippet\": str (first 150 chars of abstract), \"arxiv_id\": str, \"url\": str}]. If a result has no year visible, use the submission date year."

**Semantic Scholar — sorted by relevance with citation counts:**

tinyfish agent run --sync \
  --url "https://www.semanticscholar.org/