# Research > Find scholarly research papers and works via the OpenAlex API and read their full text. Use when the user wants to find academic papers, survey the literature on a topic, look up works by an author, check citations, or read/analyze a specific paper or its full text. Triggers include "/research", "find papers on", "research the literature", "academic paper", "openalex", "what does the research say… - **Type:** Skill - **Install:** `agentstack add skill-bednarjosef-claude-research-skill-claude-research-skill` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [bednarjosef](https://agentstack.voostack.com/s/bednarjosef) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [bednarjosef](https://github.com/bednarjosef) - **Source:** https://github.com/bednarjosef/claude-research-skill ## Install ```sh agentstack add skill-bednarjosef-claude-research-skill-claude-research-skill ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # research — OpenAlex search + full-text reading Find scholarly works on **OpenAlex** (free, no API key) and convert their open-access full text to Markdown with **markitdown**, then read only the parts you need. ## Tooling Run the bundled CLI with the skill's own virtualenv python: ``` ~/.claude/skills/research/.venv/bin/python ~/.claude/skills/research/oa.py ... ``` The venv is required (markitdown lives there). If `import markitdown` fails, install it: `~/.claude/skills/research/.venv/bin/pip install 'markitdown[pdf]'`. **No email or other identifier is ever sent to OpenAlex.** The normal API is free and keyless. If the user has an **OpenAlex Premium** key, save it once with `oa.py set-key ` (stored at `~/.claude/skills/research/.api_key`, mode 600); it is then sent only as the `api_key` query parameter. It can also come from the `OPENALEX_API_KEY` env var or a per-call `--api-key` flag. The skill works fully without any key — the key only matters for Premium rate limits. If the user mentions an OpenAlex API key, run `set-key` for them. ## Workflow 1. **Search** to find candidate works: ``` oa.py search "graph neural networks for drug discovery" --year-from 2021 --sort cited_by_count:desc --limit 10 --abstracts ``` Each result shows an `id=W…`, year, citation count, and an `[OA+PDF]` / `[OA]` / `[closed]` flag. Only `[OA+PDF]` works can be fetched in full; for others you still get the abstract. Useful options: `--filter` passes raw OpenAlex filters (repeatable), e.g. `--filter authorships.author.id:A5023888391` for an author's works, or `--filter type:review`. `--oa-only` restricts to open access. `--recent N` keeps only the last N years. `--json` emits machine-readable output. 2. **Expand the citation graph** from any known work — the fastest way to the research frontier: ``` oa.py cites W2566079294 --recent 2 --limit 20 # newer work building on it oa.py refs W2566079294 # the work's own foundations oa.py related W2566079294 # concept neighbours ``` `cites` defaults to newest-first, so it surfaces the cutting edge that grew out of a seminal paper. (`refs`/`related` depend on OpenAlex having parsed the work — often sparse for raw arXiv preprints; published versions are richer.) 3. **Fetch** the full text of a chosen work to disk: ``` oa.py fetch W2741809807 ``` Accepts an OpenAlex id, a DOI (`10.7717/peerj.4375` or `doi:…`), or a url. It downloads the OA PDF, converts it to Markdown, saves it to `./research-papers/.md` (override with `--out-dir`), and prints metadata plus an **outline with line numbers** and an approximate token count. 4. **Read only what you need.** Do **not** read the whole file by default — that defeats the point. Use the outline to `Read` the file with an `offset` and `limit` for a specific section, or `Grep` the file for a term. Pull the Methods or one Results subsection, not the entire 40-page paper. ## Guidance - For a literature survey, run a few searches with different phrasings/sorts, collect the strongest `[OA+PDF]` works, fetch them, and read the relevant sections before synthesizing. - **Cite properly.** Use the front-matter in each saved `.md` (title, authors, year, DOI, OpenAlex id) when you reference a paper. Don't invent findings — quote or paraphrase from the fetched text and point to the section. - If full text isn't open access (`[closed]` or no PDF), say so and work from the abstract; offer to look for an alternative open version. - markitdown extracts the PDF text layer (no OCR), so scanned papers come out empty/garbled — flag that if the text looks broken. It also mangles dense **two-column** layouts (words run together, abstracts become broken tables); running prose sections usually survive, so prefer reading those, and when a passage looks corrupted say so rather than quoting noise. For a cleaner source, prefer an HTML version when one exists (arXiv HTML, PubMed Central) by passing its url to `fetch`. - Saved papers persist in `research-papers/`; reuse an existing `.md` instead of re-fetching the same work. ## Use in an autoresearch / agent loop Designed to plug into autonomous research loops (e.g. the `program.md` swarm in `vast-autoresearch`) where agents need fresh, *implementable* ideas — not prose. - **Harvest ideas from abstracts, not PDFs.** In a loop the idea-dense signal is the abstract. `search "" --recent 2 --abstracts --json --limit 15` returns ~15 recent techniques in parsable JSON for the cost of one call — no PDF downloads. Only `fetch` a full paper when you've decided to *implement* a specific method and need its exact recipe (the saved `.md` then becomes the reference while you write code). - **`--json` everywhere** so a subagent can parse `results[].{id,title,abstract, date}` deterministically and dedupe by `id` across rounds (track seen ids in `findings.md`). - **Anchor + expand for the frontier.** Find one strong paper for a focus area, then `cites --recent 1 --json` to pull the newest work building on it. This is how you reach the cutting edge instead of re-finding canon. - **Map each loop focus to a query.** For the slot focuses in `vast-autoresearch`: optimizer → `"learning rate schedule warmup Muon AdamW language model"`; architecture → `"transformer architecture depth width normalization activation language model"`; data/throughput → `"efficient pretraining batch size sequence packing throughput"`; regularization/init → `"weight initialization dropout embedding regularization transformer"`. Add `--recent 2 --abstracts --json`. - **Filter for the constraints before suggesting.** The loop edits a single `train.py` in pure PyTorch with a fixed dependency set and a 5-minute budget. Prefer techniques that are self-contained, dependency-free, and cheap to try; drop anything needing new packages, huge compute, or multi-file infra. Lead with the one-line change an agent can drop into the training loop. **Honest boundary:** OpenAlex covers published papers and arXiv preprints well, but **not** GitHub-only / blog-only results (e.g. modded-nanogpt speedrun records, brand-new tricks shared only in repos or on X). For those, web search is the right tool — use this skill for the peer-reviewed/arXiv substrate and web search for the bleeding edge, and say which is which. ### Drop-in `program.md` snippet Add under the "NEVER STOP / if ideas run dry" clause so subagents mine the literature for their focus area: ```markdown When ideas in your focus area run dry, query the literature before guessing. Run (no email is sent; OpenAlex is keyless): ~/.claude/skills/research/.venv/bin/python ~/.claude/skills/research/oa.py \ search "" --recent 2 --abstracts --json --limit 15 Skim the returned abstracts for a concrete, self-contained technique that fits a single-file pure-PyTorch train.py and the 5-minute budget (no new deps). To go to the frontier, pick one strong paper and run `... oa.py cites --recent 1 --json`. Pick ONE idea, note its source (title + OpenAlex id) in findings.md, and implement it as your next experiment. If a method needs the exact recipe, fetch it: `... oa.py fetch ` and read only the Method section. Skip anything needing new packages or infra — OpenAlex won't have GitHub-only speedrun tricks; use web search for those. ``` ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [bednarjosef](https://github.com/bednarjosef) - **Source:** [bednarjosef/claude-research-skill](https://github.com/bednarjosef/claude-research-skill) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-bednarjosef-claude-research-skill-claude-research-skill - Seller: https://agentstack.voostack.com/s/bednarjosef - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.