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Agent skill

Outcome Extraction For Clinical Trials

by aipoch in aipoch/medical-research-skills

Clinical research outcome extraction for meta-analysis. An agent skill from aipoch/medical-research-skills.

MITAuto-check passedResearch & Science

Install Outcome Extraction For Clinical Trials

skills CLI
$ npx skills add aipoch/medical-research-skills --skill outcome-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 outcome-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/outcome-extraction-for-clinical-trials' .claude/skills/outcome-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
outcome-extraction-for-clinical-trials
GitHub stars
1.9k
Token cost
~1.4k tokens
SKILL.md length
580 words
Files
4 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Clinical research outcome extraction for meta-analysis. An agent skill from aipoch/medical-research-skills.

  • Works in 4 steps: Confirm the user input, output path, and… → Edit the in-file CONFIG block or… → Run python scripts/extract_pdf.py with… → …
  • Users need to extract outcome measures (binary
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Outcome Extraction For Clinical Trials is an agent skill from aipoch/medical-research-skills. Clinical research outcome extraction for meta-analysis. Use when users need to extract outcome measures (binary, continuous, or survival data) from clinical research papers for systematic review and meta-analysis. Handles both database lookup by PMID and real-time LLM extraction.

Its SKILL.md is about 1.4k 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 `outcome-extraction-for-clinical-trials_audit_result_v1.json`, `references/extraction-prompts.md` and `scripts/extract_pdf.py`).

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

  • Users need to extract outcome measures (binary
  • Survival data) from clinical research papers for systematic review and meta-analysis

Example prompts

  • “/outcome-extraction-for-clinical-trials”

Requirements

  • Python 3

Workflow steps

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

  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.

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

Outcome Extraction For Clinical Trials loads about 1.4k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 580 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

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). 580 words, ~1,384 tokens.

Download SKILL.mdSave it as .claude/skills/outcome-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
outcome-extraction-for-clinical-trials
description
Clinical research outcome extraction for meta-analysis. Use when users need to extract outcome measures (binary, continuous, or survival data) from clinical research papers for systematic review and meta-analysis. Handles both database lookup by PMID and real-time LLM extraction.
license
MIT
author
AIPOCH

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

Clinical Outcome Extraction

Extract structured outcome data from clinical research papers for meta-analysis.

When to Use

  • Use this skill when you need clinical research outcome extraction for meta-analysis. use when users need to extract outcome measures (binary, continuous, or survival data) from clinical research papers for systematic review and meta-analysis. handles both database lookup by pmid and real-time llm extraction 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 outcome-extraction for clinical trials package behavior rather than a generic answer.

Key Features

  • Scope-focused workflow aligned to: Clinical research outcome extraction for meta-analysis. Use when users need to extract outcome measures (binary, continuous, or survival data) from clinical research papers for systematic review and meta-analysis. Handles both database lookup by PMID and real-time LLM extraction.
  • 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/outcome-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.

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.
Show full SKILL.md (221 more words)Show less

Workflow

  1. Input Processing

    • User provides: full paper text + optional PMID
    • If PMID provided: query database first for existing results
    • If no PMID or no database match: proceed to LLM extraction

  2. Outcome Identification (LLM)

    • Extract all outcome measures from the paper
    • Determine outcome types: binary, continuous, or survival
    • Identify measurement time points
    • Output JSON format with outcome classification

  3. Data Classification (Code)

    • Separate outcomes into three categories:
      • bi_outcomes: Binary/dichotomous outcomes
      • con_outcomes: Continuous outcomes
      • sur_outcomes: Survival outcomes

  4. Data Extraction by Type

Binary Outcomes

Extract for each intervention group:

  • Sample size (n)
  • Number of events (event)
Continuous Outcomes

Extract for each intervention group:

  • Sample size (n)
  • Mean (mean)
  • Standard deviation (sd)
Survival Outcomes

Extract for each intervention group:

  • Sample size (n)
  • Hazard ratio (HR)
  • 95% Lower CI
  • 95% Upper CI
  1. Output Formatting
    • Combine all extracted data
    • Ensure consistent JSON structure
    • Convert values to strings

Output Format

json
[
  {
    "outcome_name": "PFS",
    "detection_time_point": "12 months",
    "groups": [
      {
        "group_name": "Treatment A",
        "sample_size": "100",
        "outcome_type": "Binary|Continuous|Survival",
        "data": [
          {"value_type": "Events|Mean|SD|HR|95%Lower CI|95%Upper CI", "value": "25"}
        ]
      }
    ]
  }
]

‼️‼️‼️See references (extraction-promots.md) for detailed JSON structures for each outcome type (binary, continuous, survival)‼️‼️‼️

Requirements

  • Extract from full text, not just abstract
  • Consider ALL intervention groups in the paper
  • Include ALL outcome measures of interest
  • Report all data regardless of statistical significance
  • Use specific group names (intervention names in English), not generic terms like "treatment group"
  • Output in JSON format
  • Output language: English for all field values
  • If data not found: output blank space ""

© 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/outcome-extraction-for-clinical-trials of aipoch/medical-research-skills.

  • SKILL.md
  • outcome-extraction-for-clinical-trials_audit_result_v1.json
  • references/extraction-prompts.md
  • scripts/extract_pdf.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Outcome 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.

Outcome Extraction For Clinical Trials compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Outcome Extraction For Clinical Trials this skillaipoch/medical-research-skills1.9k—~1.4kAutomated safety check: PassMIT
Research Paperluwill/research-skills862—~1.9kAutomated safety check: PassNone
Research Proposalluwill/research-skills862—~4.5kAutomated safety check: NotesNone
Medical Imaging ReviewLeonChaoX/qinyan-academic-skills9443 repos~1.1kAutomated safety check: NotesMIT
Medical Imaging Reviewluwill/research-skills862—~4kAutomated safety check: PassNone
Nature Paper CardYuan1z0825/nature-skills47k2 repos~2.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Outcome Extraction For Clinical Trials

What does Outcome Extraction For Clinical Trials do?

Clinical research outcome extraction for meta-analysis. An agent skill from aipoch/medical-research-skills. Outcome Extraction For Clinical Trials is an agent skill from aipoch/medical-research-skills. Clinical research outcome extraction for meta-analysis.

When should I use Outcome Extraction For Clinical Trials?

Outcome Extraction For Clinical Trials fits situations like: users need to extract outcome measures (binary; survival data) from clinical research papers for systematic review and meta-analysis.

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

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

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

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

Can I use Outcome 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 outcome-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/outcome-extraction-for-clinical-trials, .gemini/skills/outcome-extraction-for-clinical-trials, .github/skills/outcome-extraction-for-clinical-trials and .opencode/skills/outcome-extraction-for-clinical-trials in your project.

What does Outcome Extraction For Clinical Trials need to run?

Going by SKILL.md and its folder, Outcome 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 Outcome 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 Outcome 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 Outcome Extraction For Clinical Trials use?

Outcome 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 Outcome Extraction For Clinical Trials use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Outcome Extraction For Clinical Trials?

Skills that share tags, products or a category with Outcome Extraction For Clinical Trials: Research Paper (luwill/research-skills, 862 stars), Research Proposal (luwill/research-skills, 862 stars), Medical Imaging Review (LeonChaoX/qinyan-academic-skills, 944 stars) and Medical Imaging Review (luwill/research-skills, 862 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Outcome 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.