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Agent skill

Pubmed Topic Recommend

by aipoch in aipoch/medical-research-skills

Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.

MITAuto-check passedResearch & Science

Install Pubmed Topic Recommend

skills CLI
$ npx skills add aipoch/medical-research-skills --skill pubmed-topic-recommend -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills pubmed-topic-recommend --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/pubmed-topic-recommend' .claude/skills/pubmed-topic-recommend && 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
pubmed-topic-recommend
GitHub stars
2k
Token cost
~1.9k tokens
SKILL.md length
872 words
Files
4 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.

  • Works in 2 steps: Run → Output
  • A user provides a research direction/constraints and needs evidence-backed topic ideas quickly
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 11 more sections
  • Runs Python scripts from its folder; calls python

What it does

Pubmed Topic Recommend is an agent skill from aipoch/medical-research-skills. Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.

Its SKILL.md is about 1.9k 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 `pubmed-topic-recommend_audit_result_v2.json`, `references/pubmed_api.md` and `scripts/run_topic_recommendation.py`).

It sits in Research & Science, covering Academic paper search. It works with PubMed. 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

  • A user provides a research direction/constraints and needs evidence-backed topic ideas quickly
  • Tasks that involve Academic paper search

Example prompts

  • “/pubmed-topic-recommend”

Requirements

  • Python 3

Workflow steps

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

  1. Run
  2. Output

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

    No URLs in SKILL.md.

    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

Pubmed Topic Recommend loads about 1.9k tokens when it runs, and up to ~2.2k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 872 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.2k

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). 872 words, ~1,865 tokens.

Download SKILL.mdSave it as .claude/skills/pubmed-topic-recommend/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
pubmed-topic-recommend
description
Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.
license
MIT
author
AIPOCH

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

When to Use

  • You have a broad research direction (e.g., "immunotherapy biomarkers") and need 5 concrete, literature-grounded topic options to choose from.
  • You need research direction suggestions after a quick PubMed scan, with each suggestion tied to recent papers.
  • You want topic selection based on PubMed literature under constraints (time range, publication type, population, method preference).
  • You must propose actionable topics with a clear gap/opportunity statement, supported by at least 1-2 cited articles.
  • You need to iteratively refine a PubMed query (keywords/MeSH, inclusion/exclusion terms) until results are sufficient for topic generation.

Key Features

  • Uses the official PubMed E-utilities interface for literature retrieval.
  • Builds PubMed queries with:
    • keyword groups (OR) and exclusions (NOT)
    • field restrictions (e.g., Title/Abstract) and/or MeSH terms
    • date bounds via mindate / maxdate
    • optional publication-type constraints (e.g., review, meta-analysis, clinical trial)
  • Produces JSON output containing:
    • the final search query
    • retrieved literature metadata
    • ~5 topic recommendations, each with a title, justification, and supporting citations
  • Encourages evidence-consistent topic generation:
    • avoids drifting beyond retrieved themes
    • reduces redundancy across topics
    • includes a one-sentence "gap/opportunity" per topic

Dependencies

  • Python 3.10+ (recommended)
  • PubMed E-utilities (NCBI) HTTP API (no local installation required)

Example Usage

  1. Configure parameters at the top of:
  • scripts/run_topic_recommendation.py

Typical configuration items to set (names may vary by implementation):

  • keywords / MeSH terms (prefer English)
  • exclusion terms
  • time range (mindate, maxdate)
  • publication types (optional)
  • desired number of topics (default: ~5)
  1. Run:
bash
python scripts/run_topic_recommendation.py
  1. Output:
  • A JSON file or JSON printed to stdout (implementation-dependent), containing:
    • query: the PubMed query string used
    • papers: a list of retrieved records (titles/years/etc.)
    • topics: ~5 topic suggestions with justification and supporting literature (at least 1-2 cited titles/years each)

Implementation Details

  • Input collection (recommended fields)

    • Research direction / subject area (prefer English keywords; if provided in Chinese, convert to English keywords or MeSH to reduce retrieval bias)
    • Topic goals/constraints (innovation vs. application, method preference, target population)
    • Inclusion keywords and exclusion terms (support multiple OR groups and NOT groups)
    • Time range and article types (e.g., review, meta-analysis, clinical trial)
    • Optional journal/subject preferences
    • Output count (default: 5)

  • Query construction

    • Combine synonyms with OR, apply exclusions with NOT.
    • Use field tags (e.g., Title/Abstract) and/or MeSH terms to control precision.
    • Use mindate / maxdate to constrain publication dates.
    • Add publication-type filters when needed.
    • If results are too few: broaden date range, relax field restrictions, or add synonyms.

  • Retrieval

    • Uses PubMed E-utilities; API and query syntax reference: references/pubmed_api.md.

  • Topic generation rules

    • Topics must align with retrieved literature themes (evidence-consistent).
    • Avoid near-duplicate topics; cover distinct sub-directions or methodological paths.
    • Each topic includes:
      • a clear title
      • a justification grounded in retrieved papers
      • at least 1-2 supporting citations (title + year)
      • a one-sentence "research gap/opportunity" statement

When Not to Use

  • Do not use this skill when the required source data, identifiers, files, or credentials are missing.
  • Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
  • Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

  • A clearly specified task goal aligned with the documented scope.
  • All required files, identifiers, parameters, or environment variables before execution.
  • Any domain constraints, formatting requirements, and expected output destination if applicable.
Show full SKILL.md (349 more words)Show less
  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

Deterministic Output Rules

  • Use the same section order for every supported request of this skill.
  • Keep output field names stable and do not rename documented keys across examples.
  • If a value is unavailable, emit an explicit placeholder instead of omitting the field.

Output Contract

  • Return a structured deliverable that is directly usable without reformatting.
  • If a file is produced, prefer a deterministic output name such as pubmed_topic_recommend_result.md unless the skill documentation defines a better convention.
  • Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

  • Validate required inputs before execution and stop early when mandatory fields or files are missing.
  • Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
  • Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
  • Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

  • If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
  • If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
  • If partial output is returned, label it clearly and identify which checks could not be completed.

Completion Checklist

  • Confirm all required inputs were present and valid.
  • Confirm the supported execution path completed without unresolved errors.
  • Confirm the final deliverable matches the documented format exactly.
  • Confirm assumptions, limitations, and warnings are surfaced explicitly.

Quick Validation

Run this minimal verification path before full execution when possible:

bash
python scripts/run_topic_recommendation.py --help

Expected output format:

text
Result file: pubmed_topic_recommend_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any

Scope Reminder

  • Core purpose: Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.

© 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/pubmed-topic-recommend of aipoch/medical-research-skills.

  • SKILL.md
  • pubmed-topic-recommend_audit_result_v2.json
  • references/pubmed_api.md
  • scripts/run_topic_recommendation.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Pubmed Topic Recommend 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.

Pubmed Topic Recommend compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pubmed Topic Recommend this skillaipoch/medical-research-skills2k—~1.9kAutomated safety check: PassMIT
Literature Reviewneflibata-feng/MyArxiv-Agent12620 repos~5.9kAutomated safety check: NotesMIT
Citation ManagementK-Dense-AI/claude-scientific-writer2.4k2 repos~3.9kAutomated safety check: NotesMIT
Citation Managementneflibata-feng/MyArxiv-Agent12619 repos~8.1kAutomated safety check: NotesMIT
Paper Searchopenags/paper-search-mcp2.8k—~1.2kAutomated safety check: NotesMIT
Nature Academic Searchwp-a/nature-academic-search301—~1.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Pubmed Topic Recommend

What does Pubmed Topic Recommend do?

Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly. Pubmed Topic Recommend is an agent skill from aipoch/medical-research-skills. Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.

When should I use Pubmed Topic Recommend?

Pubmed Topic Recommend fits situations like: A user provides a research direction/constraints and needs evidence-backed topic ideas quickly; tasks that involve Academic paper search.

How do I install Pubmed Topic Recommend in Claude Code?

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

How do I install Pubmed Topic Recommend in Codex?

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

Can I use Pubmed Topic Recommend 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 pubmed-topic-recommend -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pubmed-topic-recommend, .gemini/skills/pubmed-topic-recommend, .github/skills/pubmed-topic-recommend and .opencode/skills/pubmed-topic-recommend in your project.

What does Pubmed Topic Recommend need to run?

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

Does Pubmed Topic Recommend access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pubmed Topic Recommend 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 Pubmed Topic Recommend use?

Pubmed Topic Recommend 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 Pubmed Topic Recommend use?

About 1.9k tokens (SKILL.md is roughly 7.5k 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 328 tokens, read only when the agent opens those files.

What are the alternatives to Pubmed Topic Recommend?

Skills that share tags, products or a category with Pubmed Topic Recommend: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Citation Management (K-Dense-AI/claude-scientific-writer, 2.4k stars), Citation Management (neflibata-feng/MyArxiv-Agent, 126 stars) and Paper Search (openags/paper-search-mcp, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pubmed Topic Recommend?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 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.