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Agent skill

Baseline Extraction For Clinical Trials

by aipoch in aipoch/medical-research-skills

Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID.

MITAuto-check passedResearch & Science

Install Baseline Extraction For Clinical Trials

skills CLI
$ npx skills add aipoch/medical-research-skills --skill baseline-extraction-for-clinical-trials -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills baseline-extraction-for-clinical-trials --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/Data Analysis/baseline-extraction-for-clinical-trials' .claude/skills/baseline-extraction-for-clinical-trials && 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
baseline-extraction-for-clinical-trials
GitHub stars
1.9k
Token cost
~1.2k tokens
SKILL.md length
552 words
Files
4 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID.

  • Works in 2 steps: Check PubMed (Deterministic) → LLM Extraction (Fallback)
  • Tasks that involve Clinical and healthcare research
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 4 more sections
  • Runs Python scripts from its folder; calls python

What it does

Baseline Extraction For Clinical Trials is an agent skill from aipoch/medical-research-skills. Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Checks PubMed for metadata; always falls back to LLM extraction for full details.

Its SKILL.md is about 1.2k 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 `baseline-extraction-for-clinical-trials_audit_result_v1.json`, `references/extraction_schema.md` and `scripts/extract_pdf.py`).

It sits in Research & Science, covering Clinical and healthcare research and 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

  • Tasks that involve Clinical and healthcare research
  • Tasks that involve Academic paper search

Example prompts

  • “Use the baseline-extraction-for-clinical-trials skill to extract clinical trial baseline data (study, region, participants, etc.) from article text…”
  • “/baseline-extraction-for-clinical-trials”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Check PubMed (Deterministic)
  2. LLM Extraction (Fallback)

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

Baseline Extraction For Clinical Trials loads about 1.2k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 552 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.5k

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). 552 words, ~1,202 tokens.

Download SKILL.mdSave it as .claude/skills/baseline-extraction-for-clinical-trials/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
baseline-extraction-for-clinical-trials
description
Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Checks PubMed for metadata; always falls back to LLM extraction for full details.
license
MIT
author
AIPOCH

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

Baseline Extraction (RCT)

This skill extracts 10 key baseline characteristics from clinical trial articles. It implements a hybrid workflow:

  1. PubMed Lookup: Checks PubMed API using the PMID to verify existence and get basic metadata.
  2. LLM Extraction: Analyzes the article text to extract detailed baseline data (since PubMed metadata is limited).

When to Use

  • Use this skill when you need extracts clinical trial baseline data (study, region, participants, etc.) from article text or pmid. checks pubmed for metadata; always falls back to llm extraction for full details in a reproducible workflow.
  • Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
  • Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
  • Use this skill when scripts/extract_pdf.py is the most direct path to complete the request.
  • Use this skill when you need the baseline-extraction for clinical trials package behavior rather than a generic answer.

Key Features

  • Scope-focused workflow aligned to: Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Checks PubMed for metadata; always falls back to LLM extraction for full details.
  • Packaged executable path(s): scripts/extract_pdf.py.
  • Reference material available in references/ for task-specific guidance.
  • Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

  • Python: 3.10+. Repository baseline for current packaged skills.
  • Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

bash
cd "20260316/scientific-skills/Data Analytics/baseline-extraction-for-clinical-trials"
python -m py_compile scripts/extract_pdf.py
python scripts/extract_pdf.py --help

Example run plan:

  1. Confirm the user input, output path, and any required config values.
  2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
  3. Run python scripts/extract_pdf.py with the validated inputs.
  4. Review the generated output and return the final artifact with any assumptions called out.
Show full SKILL.md (256 more words)Show less

Implementation Details

See ## Workflow above for related details.

  • Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
  • Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
  • Primary implementation surface: scripts/extract_pdf.py.
  • Reference guidance: references/ contains supporting rules, prompts, or checklists.
  • Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
  • Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Workflow

Step 1: Check PubMed (Deterministic)

If the user provides a PMID, use the baseline_extractor.py script to check PubMed.

python
import subprocess
import json

# Replace <PMID> with actual PMID
result = subprocess.run(["python", "scripts/baseline_extractor.py", "<PMID>"], capture_output=True, text=True)
print(result.stdout)

Analyze the Script Output:

  • If status is "success": Stop here. Return the data JSON to the user.
  • If status is "not_found", "incomplete", or "error" (or if no PMID was provided): Proceed to Step 2.
Step 2: LLM Extraction (Fallback)

If Step 1 did not yield a complete result, use the LLM to extract the information from the full article text.

Input:

  • Full article text provided by the user.

Instructions:

  1. Read the Extraction Schema carefully.
  2. Analyze the text to identify all 10 required fields.
  3. Ensure the output is strictly in the JSON format defined in the schema.
  4. Constraint: Do not hallucinate. If a field is not mentioned in the text, set it to null or an empty string.

Output

Return the final result as a Markdown code block containing the JSON object.

json
{
  "study": "...",
  "region": "...",
  ...
}

Helper Scripts

PDF Text Extraction

When the user provides a PDF file path, use extract_pdf.py to extract the text content before assessment:

© 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/Data Analysis/baseline-extraction-for-clinical-trials of aipoch/medical-research-skills.

  • SKILL.md
  • baseline-extraction-for-clinical-trials_audit_result_v1.json
  • references/extraction_schema.md
  • scripts/extract_pdf.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Baseline Extraction For Clinical Trials 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.

Baseline Extraction For Clinical Trials compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Baseline Extraction For Clinical Trials this skillaipoch/medical-research-skills1.9k—~1.2kAutomated safety check: PassMIT
Medical Research ToolkitFreedomIntelligence/OpenClaw-Medical-Skills3.1k1 repos~2.4kAutomated safety check: PassNone
Pubmed Databasegoogle-deepmind/science-skills3.2k2 repos~2.1kAutomated safety check: NotesApache-2.0
Biomedical Searchwu-yc/LabClaw1.1k2 repos~1.4kAutomated 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

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Works with

Categories

Questions about Baseline Extraction For Clinical Trials

What does Baseline Extraction For Clinical Trials do?

Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Baseline Extraction For Clinical Trials is an agent skill from aipoch/medical-research-skills.) from article text or PMID.

When should I use Baseline Extraction For Clinical Trials?

Baseline Extraction For Clinical Trials fits situations like: tasks that involve Clinical and healthcare research; tasks that involve Academic paper search.

How do I install Baseline Extraction For Clinical Trials in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill baseline-extraction-for-clinical-trials -a claude-code`. Or copy the skill folder (scientific-skills/Data Analysis/baseline-extraction-for-clinical-trials in aipoch/medical-research-skills) into .claude/skills/baseline-extraction-for-clinical-trials in your project. Claude Code loads it when a task matches its description.

How do I install Baseline Extraction For Clinical Trials in Codex?

Run `npx skills add aipoch/medical-research-skills --skill baseline-extraction-for-clinical-trials -a codex`. Or copy the skill folder (scientific-skills/Data Analysis/baseline-extraction-for-clinical-trials in aipoch/medical-research-skills) into .agents/skills/baseline-extraction-for-clinical-trials in your project. Codex loads it when a task matches its description.

Can I use Baseline Extraction For Clinical Trials 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 baseline-extraction-for-clinical-trials -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/baseline-extraction-for-clinical-trials, .gemini/skills/baseline-extraction-for-clinical-trials, .github/skills/baseline-extraction-for-clinical-trials and .opencode/skills/baseline-extraction-for-clinical-trials in your project.

What does Baseline Extraction For Clinical Trials need to run?

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

Does Baseline Extraction For Clinical Trials 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 Baseline Extraction For Clinical Trials 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 Baseline Extraction For Clinical Trials use?

Baseline Extraction For Clinical Trials 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 Baseline Extraction For Clinical Trials use?

About 1.2k tokens (SKILL.md is roughly 4.8k 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 315 tokens, read only when the agent opens those files.

What are the alternatives to Baseline Extraction For Clinical Trials?

Skills that share tags, products or a category with Baseline Extraction For Clinical Trials: Medical Research Toolkit (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Pubmed Database (google-deepmind/science-skills, 3.2k stars), Biomedical Search (wu-yc/LabClaw, 1.1k stars) and Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Baseline Extraction For Clinical Trials?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,937 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.