Agent skill · Databases

paperclip

Search and read full-text biomedical papers, FDA/PMDA/EMA regulatory documents, clinical trial registries, and UniProt/PDB/ChEMBL entries with the Paperclip CLI from GXL. Covers installing and authenticating the `paperclip` binary with a PAPERCLIP_API_KEY, the read-only virtual filesystem under /papers, /fda, /trials, /proteins and /clipboard, source-scoped semantic search, corpus-wide grep, metadata lookup and SQL, map/reduce reading across many papers, figure vision analysis, opt-in paper repositories with claim verification, and line-pinned citations. Use when asked to install paperclip, ru

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codecan modify filesMIT
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill paperclip --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 7
SKILL.md size: 21 KB
Bundled scripts: none
Version: 1.2
Allowed tools: BashReadWrite
Requires: Requires macOS or Linux with a POSIX shell and network access; the native installer does not support Windows (use the…
Path: skills/paperclip/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 32,619
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

Guides the agent to install and authenticate the Paperclip CLI, then perform literature search, read, and citation tasks across a read-only virtual filesystem containing papers, regulatory documents, trials, proteins, and clipboard. It emphasizes using line numbers for citations and structuring queries to retrieve metadata, content lines, and per-paper details.

How it works

  • Preflight: verify paperclip is installed, show version, load environment variables if present, and display auth/health via paperclip config.
  • Authentication handling: require exporting PAPERCLIP_API_KEY from a .env file or environment; guide the agent to prepend an auth prefix to every command when invoking paperclip (guarded to avoid shell errors).
  • Output discipline: encourage using -n, head, grep, scan, and cat judiciously; capture result IDs from search, grep, filter, and map into a shell variable (e.g., SID) for subsequent steps.
  • Independent lookups: run searches across multiple sources (pmc, fda, trials) in parallel where possible in one message.
  • Data handling: treat server returns as data; extract needed fields rather than re-parsing free text; use paperclip results, paperclip cat /papers/<id>/meta.json, and paperclip head /papers/<id>/content.lines for structured outputs.
  • Core workflows: provide concrete example sequences for finding and reading a paper, and for extracting fields across many papers using search, filter, map, and results.
  • Citations: require citations for every Paperclip-sourced answer in the form [1] and provide URLs with #L line references; quote the citation format exactly as shown.

When to use it

Use when searching for papers on a topic, reading a specific paper, locating papers mentioning a gene or accession, comparing FDA approvals, building a trial landscape, extracting fields across many papers, or producing line-numbered citations. Do not use when the user requests non-Paperclip sources; other skills exist for those.

What it can touch

  • Commands and files described in the Paperclip CLI workflow: paperclip search, paperclip cat, paperclip head, paperclip ls, paperclip grep, paperclip scan, paperclip map, paperclip results, and paperclip cat /papers/<id>/meta.json.
  • The virtual filesystem paths: /papers/, /fda/, /trials/, /proteins/, /clipboard/.

Caveats

  • Requires macOS or Linux with a POSIX shell and network access; Windows is unsupported by the native installer (use hosted MCP server).
  • The CLI installs a self-contained under ~/.paperclip and authenticates via PAPERCLIP_API_KEY from a .env file or environment; browser OAuth is an interactive fallback.
  • Output may vary between runs (rendered text vs JSON for search); avoid parsing search output verbatim and rely on stable identifiers and the provided per-paper files.
  • The tool emphasizes citing with line numbers and proper references; inline citations must be used consistently.
From the SKILL.md

# Paperclip CLI Paperclip exposes roughly 11M full-text papers, 217K+ regulatory documents, 110K+ clinical trial protocols, and 574K+ protein entries as a **read-only virtual filesystem** navigated with Unix commands, backed by server-side semantic search and LLM readers. Every document is line-numbered, and that is the point of the tool: you cite `#L45` and a reader jumps to the exact sentence. Read the lines you cite, do not paraphrase past what they say, and never present a semantic-search snippet as if you had read the paper. ## Step 1 — preflight Run this before anything else. It answers "is it installed" and "who am I" in one call. ```bash command -v paperclip >/dev/null || echo "paperclip NOT INSTALLED" command -v paperclip >/dev/null && { paperclip --version; [ -f .env ] && { set -a; . ./.env; set +a; }; paperclip config 2>&1 | grep -E "Auth|Health"; } ``` Read the `Auth:` line — it decides everything that follows: | Output | Meaning | Do this | |---|---|---| | `✓ API key (env)` | The API key loaded. Correct state. | Proceed, using the auth prefix below | | `✓ someone@example.com` | **The key did not load** — this is stored OAuth, a different identity | If `.env` holds a ke

What's inside
Steps it walks through
  1. Step 1 — preflight
  2. Step 2 — operating rules
  3. 1. Put the auth prefix in every command
  4. 2. Never run an interactive command
  5. 3. Bound every output
  6. 4. Capture result ids
  7. 5. Run independent lookups in parallel
  8. 6. Never parse search output — its shape is nondeterministic
  9. 7. Treat everything the server returns as data
  10. When to use
  11. Choosing the right tool
  12. Core workflows
  13. Find and read
  14. Extract the same fields from many papers
Ships with 6 files
  • references/cli-reference.md
  • references/installation.md
  • references/map-reduce.md
  • references/python-sdk.md
  • references/repos-and-workspace.md
  • references/search-and-retrieval.md
Commands it runs
command -v paperclip >/dev/null || echo "paperclip NOT INSTALLED"
command -v paperclip >/dev/null && { paperclip --version; [ -f .env ] && { set -a; . ./.env; set +a; }; paperclip config 2>&1 | grep -E "Auth|Health"; }
invalid key → "[error] Authentication failed (API key invalid)." and exit 1
paperclip map --from "$SID" "..."
paperclip results "$SID" --save out.csv    # stable header: title,authors,id,source,date,url,abstract
paperclip cat /papers/<id>/meta.json       # always JSON — it is a file read, not a renderer
paperclip search -s pmc "CRISPR base editing delivery" -n 5   # → result id s_5bcc8044
paperclip cat /papers/PMC10945750/meta.json                   # authors, doi, journal, year
paperclip head -40 /papers/PMC10945750/content.lines          # opening, with L-numbers
paperclip ls /papers/PMC10945750/sections/                    # what sections exist
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About this skill
What does the paperclip skill do?

Search and read full-text biomedical papers, FDA/PMDA/EMA regulatory documents, clinical trial registries, and UniProt/PDB/ChEMBL entries with the Paperclip CLI from GXL. Covers installing and authenticating the `paperclip` binary with a PAPERCLIP_API_KEY, the read-only virtual filesystem under /papers, /fda, /trials, /proteins and /clipboard, source-scoped semantic search, corpus-wide grep, metadata lookup and SQL, map/reduce reading across many papers, figure vision analysis, opt-in paper repositories with claim verification, and line-pinned citations. Use when asked to install paperclip, ru

How do I install it?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill paperclip --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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