New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Meta Analysis Execution

by InternScience in InternScience/scp

Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries.

MITAuto-check passedResearch & Science

Install Meta Analysis Execution

skills CLI
$ npx skills add InternScience/scp --skill meta-analysis-execution -a claude-code

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

GitHub CLI
$ gh skill install InternScience/scp meta-analysis-execution --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/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/meta-analysis-execution .claude/skills/meta-analysis-execution && 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-analysis-execution
GitHub stars
169
Used in
1 other repo
Token cost
~1.1k tokens
SKILL.md length
180 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries.

  • Works in 2 steps: MCP Server Definition → Meta-Analysis Workflow
  • Research & Science work in your project
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Meta Analysis Execution is an agent skill from InternScience/scp. Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science. The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/meta-analysis-execution”

Requirements

  • Python 3

Workflow steps

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

  1. MCP Server Definition
  2. Meta-Analysis Workflow

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are 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 Analysis Execution loads about 1.1k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 180 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 180 words, ~1,144 tokens.

Download SKILL.mdSave it as .claude/skills/meta-analysis-execution/SKILL.md (or your agent's skills folder).
name
meta-analysis-execution
description
Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries.
license
MIT license
metadata.skill-author
PJLab

Meta-Analysis Execution

Usage

1. MCP Server Definition
python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class InternAgentClient:
    """InternAgent MCP Client"""

    def __init__(self, server_url: str, api_key: str):
        self.server_url = server_url
        self.api_key = api_key
        self.session = None

    async def connect(self):
        try:
            self.transport = streamablehttp_client(
                url=self.server_url,
                headers={"SCP-HUB-API-KEY": self.api_key}
            )
            self.read, self.write, self.get_session_id = await self.transport.__aenter__()
            self.session_ctx = ClientSession(self.read, self.write)
            self.session = await self.session_ctx.__aenter__()
            await self.session.initialize()
            return True
        except Exception as e:
            print(f"✗ connect failure: {e}")
            return False

    async def disconnect(self):
        try:
            if self.session:
                await self.session_ctx.__aexit__(None, None, None)
            if hasattr(self, 'transport'):
                await self.transport.__aexit__(None, None, None)
        except Exception as e:
            print(f"✗ disconnect error: {e}")

    def parse_result(self, result):
        try:
            if hasattr(result, 'content') and result.content:
                content = result.content[0]
                if hasattr(content, 'text'):
                    return json.loads(content.text)
            return str(result)
        except Exception as e:
            return {"error": f"parse error: {e}", "raw": str(result)}
2. Meta-Analysis Workflow

Synthesize multiple studies to generate comprehensive research insights.

Workflow Steps:

  1. Define Research Question - Specify meta-analysis objective
  2. Execute Analysis - Process multiple studies systematically
  3. Generate Report - Create summary tables or comprehensive reports

Implementation:

python
## Initialize client
client = InternAgentClient(
    "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
    "<your-api-key>"
)

if not await client.connect():
    print("connection failed")
    exit()

## Input: Meta-analysis query
prompt = "Analyze the effectiveness of mRNA vaccines against COVID-19"
report_type = "table"  # or "comprehensive"

## Execute meta-analysis
result = await client.session.call_tool(
    "MetaAnalysis",
    arguments={
        "prompt": prompt,
        "file_list": None,
        "type": report_type
    }
)

data = client.parse_result(result)

if 'final_report' in data:
    print("✅ Meta-analysis completed")
    print(f"Task ID: {data.get('task_id', 'N/A')}")
    final_report = data['final_report']
    print(f"\nReport Type: {final_report.get('type', 'N/A')}")
    print(f"\nContent:\n{final_report.get('content', 'N/A')}")
else:
    print(f"❌ Analysis failed: {data.get('error', 'Unknown error')}")

await client.disconnect()
Tool Descriptions

InternAgent Server:

  • MetaAnalysis: Perform meta-analysis on research studies
    • Args:
      • prompt (str): Research question for meta-analysis
      • file_list (list, optional): Additional study files
      • type (str): Output format ("table" or "comprehensive")
    • Returns:
      • task_id (str): Analysis task identifier
      • final_report (dict): Meta-analysis results
        • type (str): Report format
        • content (str): Analysis findings
Input/Output

Input:

  • prompt: Research question or hypothesis
  • type: Report format (table for structured data, comprehensive for detailed analysis)
  • file_list: Optional list of study files to include

Output:

  • Structured report with:
    • Study summaries
    • Effect sizes and confidence intervals
    • Statistical heterogeneity metrics
    • Summary conclusions
Use Cases
  • Systematic reviews of clinical trials
  • Evidence synthesis in medicine
  • Research effectiveness evaluation
  • Policy decision support
  • Academic literature reviews
Performance Notes
  • Execution time: 1-5 minutes depending on number of studies
  • Output formats: Markdown tables or comprehensive text reports
  • Data quality: Automatically assesses study quality indicators

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

Files

Just SKILL.md in skills/meta-analysis-execution of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Meta Analysis Execution 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 Analysis Execution compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Meta Analysis Execution this skillInternScience/scp1691 repos~1.1kAutomated safety check: PassMIT
Hypothesis Generationspacering-net/codeg3.8k15 repos~3.6kAutomated safety check: NotesMIT
GitHub Deep Researchbytedance/deer-flow83k5 repos~1.3kAutomated safety check: PassMIT
Nature Paper CardYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0
Read arXiv Paperkarpathy/nanochat58k2 repos~494Automated safety check: PassMIT
Content Research Writerweapp-tailwindcss/weapp-tailwindcss1.9k25 repos~3.5kAutomated safety check: PassMIT

Similar skills

  • Hypothesis Generation

    spacering-net/codeg

    Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.

    3.8k GitHub starsUsed in 15 repos~3.6k tokens
    Research & ScienceAuto-check: notes

  • GitHub Deep Research

    bytedance/deer-flow

    Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.

    83k GitHub starsUsed in 5 repos~1.3k tokens
    Research & ScienceAuto-check passed

  • Nature Paper Card

    Yuan1z0825/nature-skills

    Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.

    46k GitHub starsUsed in 2 repos~2.1k tokens
    Research & ScienceAuto-check passed

  • Read arXiv Paper

    karpathy/nanochat

    Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.

    58k GitHub starsUsed in 2 repos~494 tokens
    Research & ScienceAuto-check passed

  • Content Research Writer

    weapp-tailwindcss/weapp-tailwindcss

    Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.

    1.9k GitHub starsUsed in 25 repos~3.5k tokens
    Research & ScienceAuto-check passed

  • Peer Review

    spacering-net/codeg

    Structured manuscript/grant review with checklist-based evaluation.

    3.8k GitHub starsUsed in 18 repos~5.9k tokens
    Research & ScienceAuto-check: notes

More from InternScience/scp

All 73 skills in this repo

  • Comprehensive Variant Annotation

    InternScience/scp

    Given an rsID, query multiple databases (dbSNP, FAVOR, GWAS Catalog, ClinVar, gnomAD, PharmGKB, ClinGen) for comprehensive annotation.

    169 GitHub starsUsed in 1 repo~2.2k tokens
    Auto-check passed

  • Drugsda Esmfold

    InternScience/scp

    Use ESMFold model to predict 3D structure of the input protein sequence.

    169 GitHub starsUsed in 2 repos~721 tokens
    Auto-check passed

  • Drugsda Prosst

    InternScience/scp

    Given a protein sequence and its structure, employ ProSST model to predict mutation effects and obtain the top-k mutated sequences.

    169 GitHub starsUsed in 2 repos~949 tokens
    Auto-check passed

  • Atmospheric Science Calculations

    InternScience/scp

    Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.

    169 GitHub starsUsed in 1 repo~2.1k tokens
    Auto-check passed

  • Biomedical Web Search

    InternScience/scp

    Search biomedical literature and web content using Tavily search engine for research and clinical information.

    169 GitHub starsUsed in 1 repo~598 tokens
    Auto-check passed

  • Buoyancy Acceleration Calculation

    InternScience/scp

    Calculate buoyancy forces and acceleration for fluid mechanics and hydrodynamics analysis.

    169 GitHub starsUsed in 1 repo~540 tokens
    Auto-check passed

Categories

Questions about Meta Analysis Execution

What does Meta Analysis Execution do?

Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries. Meta Analysis Execution is an agent skill from InternScience/scp. Perform meta-analysis on scientific studies to synthesize research findings and generate comprehensive reports with statistical summaries.

When should I use Meta Analysis Execution?

Meta Analysis Execution fits situations like: research & Science work in your project.

How do I install Meta Analysis Execution in Claude Code?

Run `npx skills add InternScience/scp --skill meta-analysis-execution -a claude-code`. Or copy the skill folder (skills/meta-analysis-execution in InternScience/scp) into .claude/skills/meta-analysis-execution in your project. Claude Code loads it when a task matches its description.

How do I install Meta Analysis Execution in Codex?

Run `npx skills add InternScience/scp --skill meta-analysis-execution -a codex`. Or copy the skill folder (skills/meta-analysis-execution in InternScience/scp) into .agents/skills/meta-analysis-execution in your project. Codex loads it when a task matches its description.

Can I use Meta Analysis Execution 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 InternScience/scp --skill meta-analysis-execution -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-analysis-execution, .gemini/skills/meta-analysis-execution, .github/skills/meta-analysis-execution and .opencode/skills/meta-analysis-execution in your project.

What does Meta Analysis Execution need to run?

SKILL.md names no scripts, command-line tools or credentials: Meta Analysis Execution is instructions for the agent only. Our summary lists: Python 3.

Does Meta Analysis Execution 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 Analysis Execution 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. Review the folder before installing.

What licence does Meta Analysis Execution use?

Meta Analysis Execution 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 Analysis Execution 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.

What are the alternatives to Meta Analysis Execution?

Skills that share tags, products or a category with Meta Analysis Execution: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Meta Analysis Execution?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 169 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.