Agent skill

Reference Finder

by aipoch in aipoch/medical-research-skills

Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API.

MITAuto-check passedResearch & Science

Install Reference Finder

skills CLI
$ npx skills add aipoch/medical-research-skills --skill reference-finder -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills reference-finder --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/reference-finder' .claude/skills/reference-finder && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reference-finder
GitHub stars
2k
Token cost
~1.1k tokens
SKILL.md length
385 words
Files
4 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API.

  • Works in 5 steps: Sentence splitting: The input text is… → PubMed search (ESearch): For each… → Record retrieval (EFetch): The top… → …
  • You need titles
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python; reaches eutils.ncbi.nlm.nih.gov

What it does

Reference Finder is an agent skill from aipoch/medical-research-skills. Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `reference-finder_audit_result_v1.json`, `references/evaluation-checklist.md` and `scripts/find_refs.py`).

It sits in Research & Science, covering Academic paper search. It works with PubMed and Python. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need titles
  • Brief recommendation reasons from the PubMed E-utilities API

Example prompts

  • “/reference-finder”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Sentence splitting: The input text is split into sentences (implementation-defined; typically punctuation-based).
  2. PubMed search (ESearch): For each sentence, a query is sent to
  3. Record retrieval (EFetch): The top candidate PMIDs are fetched via
  4. Field extraction: Title, year, PMID, and DOI (when present) are extracted from the returned metadata.
  5. Ranking and selection: Candidates are scored and the top N are returned with a short recommendation reason.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • eutils.ncbi.nlm.nih.gov

    Also links to:

    • ncbi.nlm.nih.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reference Finder loads about 1.1k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 50 tokens; SKILL.md has 385 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 385 words, ~1,138 tokens.

Download SKILL.mdSave it as .claude/skills/reference-finder/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
reference-finder
description
Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You have a scientific paragraph and want suggested PubMed papers for each sentence.
  • You need top-ranked references with title, DOI, PMID, year, and a short why recommended explanation.
  • You are drafting or reviewing a manuscript and want quick literature grounding for key claims.
  • You want a lightweight reference matcher that uses only the official PubMed E-utilities API (no third-party services).
  • You need a scriptable tool for batch or CLI workflows to generate candidate citations.

Key Features

  • Sentence-level reference matching for scientific text.
  • Returns the top N (default: 3) most relevant PubMed records per sentence.
  • Outputs structured fields: title, DOI, PMID, year, recommendation reason.
  • Relevance ranking based on:
    • keyword overlap / match strength,
    • publication year preference,
    • citation-count signal (when available/derivable).
  • Safety constraints:
    • Network access restricted to eutils.ncbi.nlm.nih.gov.
    • No local filesystem writes except to outputs/ during execution.
    • Request timeout set to 30 seconds with clear error messages.
  • Supports Python API usage and CLI usage (including interactive mode).

Dependencies

  • Python 3.x (standard library only; no third-party packages required)

Example Usage

Python (direct call)
python
from reference_finder import find_references

text = "CRISPR-Cas9 gene editing has revolutionized biomedical research."

results = find_references(text)

for ref in results[:3]:
    print(f"- {ref['title']} ({ref['year']})")
    print(f"  DOI: {ref['doi']}")
    print(f"  PMID: {ref['pmid']}")
    print(f"  Reason: {ref['reason']}")
CLI (single input)
bash
python scripts/find_refs.py "CRISPR-Cas9 gene editing has revolutionized biomedical research."
CLI (interactive mode)
bash
python scripts/find_refs.py
Example output (JSON)
json
[
  {
    "pmid": "PMID:",
    "title": "A Programmable Dual-RNA-Guided DNA Endonuclease in Vitro",
    "doi": "10.1126/science.1225829",
    "year": 2012,
    "reason": "Highest keyword match for 'CRISPR-Cas9', foundational paper"
  }
]

Implementation Details

Data flow
  1. Sentence splitting: The input text is split into sentences (implementation-defined; typically punctuation-based).
  2. PubMed search (ESearch): For each sentence, a query is sent to:
    • https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi
  3. Record retrieval (EFetch): The top candidate PMIDs are fetched via:
    • https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi
  4. Field extraction: Title, year, PMID, and DOI (when present) are extracted from the returned metadata.
  5. Ranking and selection: Candidates are scored and the top N are returned with a short recommendation reason.
Show full SKILL.md (126 more words)Show less
Ranking signals
  • Keyword match: Measures overlap between sentence terms and retrieved record metadata (e.g., title/abstract terms when available).
  • Publication year: Used as a preference signal (e.g., favoring more recent work unless a classic/foundational match is strong).
  • Citation count: Incorporated when available/derivable; otherwise treated as missing without failing the run.
Operational constraints and safety
  • Allowed network host: eutils.ncbi.nlm.nih.gov only.
  • Prohibited: Any third-party URLs.
  • Filesystem: Do not write outside outputs/ during execution.
  • Rate limiting: Use a reasonable request cadence (e.g., ~0.5s between requests) to respect API limits.
  • Timeout: 30 seconds per request.
  • Error handling: Return semantic, user-readable error messages for network/API/parse failures.
Defaults
  • Top references per sentence: 3
  • Endpoints:
    • ESearch: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi
    • EFetch: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in scientific-skills/Evidence Insight/reference-finder of aipoch/medical-research-skills.

  • SKILL.md
  • reference-finder_audit_result_v1.json
  • references/evaluation-checklist.md
  • scripts/find_refs.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Reference Finder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reference Finder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reference Finder this skillaipoch/medical-research-skills2k—~1.1kAutomated safety check: PassMIT
Citation ManagementK-Dense-AI/claude-scientific-writer2.4k2 repos~3.9kAutomated safety check: NotesMIT
PaperSeek Literature SearchMingfengHong/paperseek200—~1.6kAutomated safety check: PassApache-2.0
Literature ReviewK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: NotesMIT
Ma Search Bibliographyhtlin222/meta-pipe134—~2.1kAutomated safety check: NotesCustom licence
Citation Managementforyourhealth111-pixel/Vibe-Skills3.6k—~7.6kAutomated safety check: NotesMIT

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Works with

Questions about Reference Finder

What does Reference Finder do?

Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API. Reference Finder is an agent skill from aipoch/medical-research-skills. Automatically finds and ranks PubMed references for each sentence in scientific text; use when you need titles, DOIs, and brief recommendation reasons from the PubMed E-utilities API.

When should I use Reference Finder?

Reference Finder fits situations like: you need titles; brief recommendation reasons from the PubMed E-utilities API.

How do I install Reference Finder in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill reference-finder -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/reference-finder in aipoch/medical-research-skills) into .claude/skills/reference-finder in your project. Claude Code loads it when a task matches its description.

How do I install Reference Finder in Codex?

Run `npx skills add aipoch/medical-research-skills --skill reference-finder -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/reference-finder in aipoch/medical-research-skills) into .agents/skills/reference-finder in your project. Codex loads it when a task matches its description.

Can I use Reference Finder in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill reference-finder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reference-finder, .gemini/skills/reference-finder, .github/skills/reference-finder and .opencode/skills/reference-finder in your project.

What does Reference Finder need to run?

Going by SKILL.md and its folder, Reference Finder needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Reference Finder access the network?

SKILL.md names 2 domains. In commands or code: eutils.ncbi.nlm.nih.gov; the agent is likely to contact it when it follows the instructions. As links in the text: ncbi.nlm.nih.gov. This is read from the text; nothing was executed.

Is Reference Finder safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Reference Finder use?

Reference Finder is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reference Finder use?

About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 657 tokens, read only when the agent opens those files.

What are the alternatives to Reference Finder?

Skills that share tags, products or a category with Reference Finder: Citation Management (K-Dense-AI/claude-scientific-writer, 2.4k stars), PaperSeek Literature Search (MingfengHong/paperseek, 200 stars), Literature Review (K-Dense-AI/scientific-agent-skills, 48k stars) and Ma Search Bibliography (htlin222/meta-pipe, 134 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reference Finder?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,973 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.