Oleafly Review Manuscript
Oleafly/Oleafly
Run a referee-grade review of the manuscript in the open project without changing a single manuscript file.
Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis.
Install Ma Data Extraction
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install htlin222/meta-pipe ma-data-extraction --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ma-data-extraction .claude/skills/ma-data-extraction && rm -rf skills-srcUse ~/.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/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .claude/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/htlin222/meta-pipe/tree/main/ma-data-extractionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install htlin222/meta-pipe ma-data-extraction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .agents/skills && cp -r skills-src/ma-data-extraction .agents/skills/ma-data-extraction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .agents/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install htlin222/meta-pipe ma-data-extraction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/ma-data-extraction .cursor/skills/ma-data-extraction && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .cursor/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/htlin222/meta-pipe.git --path ma-data-extraction--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install htlin222/meta-pipe ma-data-extraction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/ma-data-extraction .gemini/skills/ma-data-extraction && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .gemini/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install htlin222/meta-pipe ma-data-extractionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .github/skills && cp -r skills-src/ma-data-extraction .github/skills/ma-data-extraction && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .github/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add htlin222/meta-pipe --skill ma-data-extraction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install htlin222/meta-pipe ma-data-extraction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/htlin222/meta-pipe.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/ma-data-extraction .opencode/skills/ma-data-extraction && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ma-data-extraction" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-data-extraction into .opencode/skills/ma-data-extraction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-data-extraction", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ma-data-extraction- GitHub stars
- 134
- Token cost
- ~1.6k tokens
- SKILL.md length
- 576 words
- Files
- 11 (incl. scripts, references)
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis.
- Works in 2 steps: Web-Based Extraction (Default — Run First) → PDF-Based Extraction (Only for Gaps)
- Moving from full-text collection to statistical analysis
- SKILL.md covers Overview, Inputs, Outputs and Workflow (Web-First Hybrid —…, plus 5 more sections
- Runs Python scripts from its folder; calls uv; reaches pubmed.ncbi.nlm.nih.gov and clinicaltrials.gov
What it does
Ma Data Extraction is an agent skill from htlin222/meta-pipe. Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis. Use when moving from full-text collection to statistical analysis.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts and reference files (for example `references/data-dictionary-template.md`, `references/open-data-sources.md` and `references/web-extraction.md`).
It sits in Research & Science, covering Statistics. It works with SQLite. The repository describes itself as: Claude Code-powered end-to-end meta-analysis automation: AI-assisted literature review, screening, extraction, analysis, and manuscript generation for systematic reviews and….
When your agent uses it
- Moving from full-text collection to statistical analysis
- Tasks that involve Statistics
Example prompts
- “/ma-data-extraction”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
- Web-Based Extraction (Default — Run First)
- PDF-Based Extraction (Only for Gaps)
What it can do on your machine
Read from SKILL.md and the folder at commit 5c5c3f0. 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 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
pubmed.ncbi.nlm.nih.govclinicaltrials.goveuropepmc.org
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
Ma Data Extraction loads about 1.6k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 51 tokens; SKILL.md has 576 words of instructions outside code blocks.
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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 576 words (~1,629 tokens).
“Extract consistent data, capture provenance, and build a clean analysis dataset.”
- name
- ma-data-extraction
Files
SKILL.md and 10 other files (scripts, references) in ma-data-extraction of htlin222/meta-pipe.
- SKILL.md
- references/data-dictionary-template.md
- references/open-data-sources.md
- references/source-template.csv
- references/study-map-template.csv
- references/web-extraction.md
- scripts/init_extraction_db.py
- scripts/llm_extract.py
- scripts/validate_sources.py
- templates/prognostic_factor.md
- templates/prognostic_factor.yaml
Open the folder on GitHubat commit 5c5c3f0
Compare with similar skills
Ma Data Extraction 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ma Data Extraction this skillhtlin222/meta-pipe | 134 | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Oleafly Review ManuscriptOleafly/Oleafly | 206 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| Manuscript Statistics AuditYuan1z0825/nature-skills | 46k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Experimental DesignOleafly/Oleafly | 206 | 4 repos | ~3.5k | Automated safety check: Notes | MIT | |
| Manuscript Writing Reviewlabarba/sciwrite | 852 | — | ~2.4k | Automated safety check: Pass | CC-BY-4.0 |
Similar skills
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Manuscript Statistics Audit
Yuan1z0825/nature-skills
Audits or rewrites the statistical reporting in a manuscript: experimental units, replication, tests, uncertainty and figure legends, without inventing missing details.
Experimental Design
Oleafly/Oleafly
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.
Manuscript Writing Review
labarba/sciwrite
A skill your agent uses when asked to review, edit, or improve the writing quality of a scientific or engineering manuscript.
Math Tools
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Deterministic mathematical computation using SymPy. An agent skill from ananddtyagi/cc-marketplace.
More from htlin222/meta-pipe
All 15 skills in this repoMa End To End
htlin222/meta-pipe
End-to-end AI-assisted meta-analysis pipeline orchestration from TOPIC.txt to final manuscript and reviewer responses.
Ma Fulltext Management
htlin222/meta-pipe
Collect and manage full-text PDFs for included studies, track provenance, and prepare documents for extraction.
Ma Screening Quality
htlin222/meta-pipe
Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias.
Ma Search Bibliography
htlin222/meta-pipe
Conduct literature searches for meta-analysis using Python with uv, query PubMed and other databases, deduplicate results, and store round-based bibliographies with notes.
Ma Manuscript Quarto
htlin222/meta-pipe
Draft and render a meta-analysis manuscript with Quarto using an IMRaD structure and embedded figures/tables.
Ma Meta Analysis
htlin222/meta-pipe
Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables.
Related
Works with
Categories
Questions about Ma Data Extraction
What does Ma Data Extraction do?
Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis. Ma Data Extraction is an agent skill from htlin222/meta-pipe. Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis.
When should I use Ma Data Extraction?
Ma Data Extraction fits situations like: moving from full-text collection to statistical analysis; tasks that involve Statistics.
How do I install Ma Data Extraction in Claude Code?
Run `npx skills add htlin222/meta-pipe --skill ma-data-extraction -a claude-code`. Or copy the skill folder (ma-data-extraction in htlin222/meta-pipe) into .claude/skills/ma-data-extraction in your project. Claude Code loads it when a task matches its description.
How do I install Ma Data Extraction in Codex?
Run `npx skills add htlin222/meta-pipe --skill ma-data-extraction -a codex`. Or copy the skill folder (ma-data-extraction in htlin222/meta-pipe) into .agents/skills/ma-data-extraction in your project. Codex loads it when a task matches its description.
Can I use Ma Data Extraction 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 htlin222/meta-pipe --skill ma-data-extraction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ma-data-extraction, .gemini/skills/ma-data-extraction, .github/skills/ma-data-extraction and .opencode/skills/ma-data-extraction in your project.
What does Ma Data Extraction need to run?
Going by SKILL.md and its folder, Ma Data Extraction needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Ma Data Extraction access the network?
SKILL.md names 3 domains. In commands or code: pubmed.ncbi.nlm.nih.gov, clinicaltrials.gov and europepmc.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Is Ma Data Extraction 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 Ma Data Extraction use?
Ma Data Extraction has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Ma Data Extraction use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 4.2k tokens, read only when the agent opens those files.
What are the alternatives to Ma Data Extraction?
Skills that share tags, products or a category with Ma Data Extraction: Oleafly Review Manuscript (Oleafly/Oleafly, 206 stars), Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), Manuscript Statistics Audit (Yuan1z0825/nature-skills, 46k stars) and Experimental Design (Oleafly/Oleafly, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ma Data Extraction?
htlin222 (a GitHub user) maintains it in htlin222/meta-pipe, which has 134 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 23, 2026.
Source: htlin222/meta-pipe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.