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Agent skill

Meta Feasibility Analyzer

by aipoch in aipoch/medical-research-skills

Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov.

MITAuto-check passedResearch & Science

Install Meta Feasibility Analyzer

skills CLI
$ npx skills add aipoch/medical-research-skills --skill meta-feasibility-analyzer -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills meta-feasibility-analyzer --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/meta-feasibility-analyzer' .claude/skills/meta-feasibility-analyzer && 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
meta-feasibility-analyzer
GitHub stars
2k
Token cost
~1.5k tokens
SKILL.md length
552 words
Files
3 (incl. scripts)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov.

  • Works in 9 steps: Generate Search Query → Extract Query String → Search Clinical Trials → …
  • You need to evaluate if a topic is viable for a new Meta-analysis
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Meta Feasibility Analyzer is an agent skill from aipoch/medical-research-skills. Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `meta-feasibility-analyzer_audit_result_v1.json` and `scripts/feasibility_ops.py`).

It sits in Research & Science, covering Clinical and healthcare research, Academic paper search and Data analysis. 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

  • You need to evaluate if a topic is viable for a new Meta-analysis
  • Tasks that involve Clinical and healthcare research
  • Tasks that involve Academic paper search

Example prompts

  • “Use the meta-feasibility-analyzer skill to analyz the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and…”
  • “/meta-feasibility-analyzer”

Requirements

  • Python 3

Workflow steps

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

  1. Generate Search Query
  2. Extract Query String
  3. Search Clinical Trials
  4. Process Clinical Results
  5. Feasibility Check (Stage 1)
  6. Search Meta-Analyses
  7. Process Meta Results
  8. Final Feasibility Analysis
  9. 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

Meta Feasibility Analyzer loads about 1.5k tokens when it runs. Until then it costs about 63 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
~63
When it runs · the whole SKILL.md, loaded when a task matches
~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, ~1,543 tokens.

Download SKILL.mdSave it as .claude/skills/meta-feasibility-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
meta-feasibility-analyzer
description
Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.
license
MIT
author
AIPOCH

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

Meta Feasibility Analyzer

This skill evaluates the feasibility of conducting a new Meta-analysis on a given topic (title). It checks for existing Meta-analyses and available Clinical Trials to determine if there is a gap or sufficient new evidence.

When to Use

  • Use this skill when you need analyzes the feasibility of a proposed meta-analysis topic by searching for existing meta-analyses and clinical trials on pubmed/clinicaltrials.gov. use when you need to evaluate if a topic is viable for a new meta-analysis 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/feasibility_ops.py is the most direct path to complete the request.
  • Use this skill when you need the meta-feasibility-analyzer package behavior rather than a generic answer.

Key Features

  • Scope-focused workflow aligned to: Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.
  • Packaged executable path(s): scripts/feasibility_ops.py.
  • 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/meta-feasibility-analyzer"
python -m py_compile scripts/feasibility_ops.py
python scripts/feasibility_ops.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/feasibility_ops.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/feasibility_ops.py.
  • 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 (198 more words)Show less

Workflow

Follow these steps to perform the analysis.

1. Generate Search Query

First, analyze the user's proposed title to generate a valid PubMed search query.

Prompt for LLM:

text
Role: Medical Search Expert
Task: Extract keywords from the following title and create a PubMed search query.
Title: "{{input_the_title}}"

Rules:
1. Extract keywords (Disease, Intervention, Outcome).
2. Convert to standard MeSH terms if possible.
3. Combine with AND/OR.
4. Enclose the final query in braces {}.
5. Do NOT include "meta analysis" in the query.

Example Output:
{(ovarian cancer) AND (chemotherapy) AND (bevacizumab)}
2. Extract Query String

Run the extraction script to get the clean query string.

bash
python scripts/feasibility_ops.py extract --text "{{llm_output}}"

Store the output as {{search_query}}.

3. Search Clinical Trials

Search for Clinical Trials via the PubMed API.

bash
python scripts/feasibility_ops.py search --query "{{search_query}}" --type clinical

Store the result JSON as {{clinical_json}}.

4. Process Clinical Results

Format the clinical trial results and check the count.

bash
python scripts/feasibility_ops.py clinical --json '{{clinical_json}}' --query "{{search_query}}"

Parse the output JSON to get:

  • clinical_count: Number of trials found.
  • clinical_summary: Formatted summary string.
5. Feasibility Check (Stage 1)

If clinical_count == 0:

  • The topic is NOT FEASIBLE due to lack of primary studies.
  • Output: "⚠️ Sorry, no relevant clinical studies found for this title. This topic is likely not feasible."
  • STOP.

If clinical_count > 0:

  • Proceed to Step 6.
6. Search Meta-Analyses

Search for existing Meta-analyses via the PubMed API using the same query.

bash
python scripts/feasibility_ops.py search --query "{{search_query}}" --type meta

Store the result JSON as {{meta_json}}.

7. Process Meta Results

Format the meta-analysis results.

bash
python scripts/feasibility_ops.py meta --json '{{meta_json}}'

Parse the output JSON to get:

  • meta_summary: Formatted summary string.
8. Final Feasibility Analysis

Analyze the results to determine final feasibility.

Prompt for LLM:

text
Role: Clinical Research Expert
Task: Assess Meta-analysis feasibility.

Input:
Title: "{{input_the_title}}"
Existing Meta-Analyses:
{{meta_summary}}

Existing Clinical Trials:
{{clinical_summary}}

Logic:
1. If NO existing Meta-analyses + YES Clinical Trials -> ✅ FEASIBLE.
2. If YES existing Meta-analyses:
   - Check the dates. Are there new Clinical Trials published AFTER the latest Meta-analysis?
   - If YES new trials -> ✅ FEASIBLE (Update is possible).
   - If NO new trials -> ⚠️ NOT FEASIBLE (Already covered).

Output Format:
"{{input_the_title}}"
[Conclusion: ✅ Feasible / ⚠️ Not Feasible]
Reason: [Explain based on the logic above]
9. Output

Present the final analysis to the user.

© 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 2 other files (scripts) in scientific-skills/Data Analysis/meta-feasibility-analyzer of aipoch/medical-research-skills.

  • SKILL.md
  • meta-feasibility-analyzer_audit_result_v1.json
  • scripts/feasibility_ops.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Meta Feasibility Analyzer 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.

Meta Feasibility Analyzer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Meta Feasibility Analyzer this skillaipoch/medical-research-skills2k—~1.5kAutomated 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 Meta Feasibility Analyzer

What does Meta Feasibility Analyzer do?

Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Meta Feasibility Analyzer is an agent skill from aipoch/medical-research-skills.gov.

When should I use Meta Feasibility Analyzer?

Meta Feasibility Analyzer fits situations like: you need to evaluate if a topic is viable for a new Meta-analysis; tasks that involve Clinical and healthcare research; tasks that involve Academic paper search.

How do I install Meta Feasibility Analyzer in Claude Code?

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

How do I install Meta Feasibility Analyzer in Codex?

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

Can I use Meta Feasibility Analyzer 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 meta-feasibility-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meta-feasibility-analyzer, .gemini/skills/meta-feasibility-analyzer, .github/skills/meta-feasibility-analyzer and .opencode/skills/meta-feasibility-analyzer in your project.

What does Meta Feasibility Analyzer need to run?

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

Does Meta Feasibility Analyzer 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 Meta Feasibility Analyzer 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 Meta Feasibility Analyzer use?

Meta Feasibility Analyzer 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 Meta Feasibility Analyzer use?

About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Meta Feasibility Analyzer?

Skills that share tags, products or a category with Meta Feasibility Analyzer: 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 Meta Feasibility Analyzer?

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.