Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias.
Install Ma Screening Quality
$ npx skills add htlin222/meta-pipe --skill ma-screening-quality -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install htlin222/meta-pipe ma-screening-quality --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-screening-quality .claude/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .claude/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-qualityType 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-screening-quality -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install htlin222/meta-pipe ma-screening-quality --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-screening-quality .agents/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .agents/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-quality -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install htlin222/meta-pipe ma-screening-quality --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-screening-quality .cursor/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .cursor/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-quality--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-screening-quality -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install htlin222/meta-pipe ma-screening-quality --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-screening-quality .gemini/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .gemini/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-qualityInstalls 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-screening-quality -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-screening-quality .github/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .github/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-quality -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-screening-quality --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-screening-quality .opencode/skills/ma-screening-quality && 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-screening-quality" agent skill from https://github.com/htlin222/meta-pipe/tree/main/ma-screening-quality into .opencode/skills/ma-screening-quality/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ma-screening-quality", 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-screening-quality- GitHub stars
- 139
- Token cost
- ~2.2k tokens
- SKILL.md length
- 754 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias.
- Works in 7 steps: AI Screening (Reviewer 1): Run… → Human/AI Reviewer 2: Either run… → Compute agreement: Run… → …
- Selecting eligible studies for meta-analysis
- SKILL.md covers Overview, Inputs, Outputs and Commands, plus 7 more sections
- Runs Python scripts from its folder; calls uv and claude
What it does
Ma Screening Quality is an agent skill from htlin222/meta-pipe. Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias. Use when selecting eligible studies for meta-analysis.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/dual-review-schema.md`, `references/rayyan-setup.md` and `references/screening-labels.md`).
It sits in Research & Science. 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
- Selecting eligible studies for meta-analysis
Example prompts
- “/ma-screening-quality”
Requirements
- Python 3
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- AI Screening (Reviewer 1): Run ai_screen.py --project to auto-screen all records against eligibility.md.
- Human/AI Reviewer 2: Either run ai_screen.py --reviewer 2 for dual-AI review, or have a human fill Reviewer2_Decision and Reviewer2_Reason…
- Compute agreement: Run dual_review_agreement.py to calculate Cohen's kappa (target >= 0.60).
- Resolve conflicts: Review records where Reviewer 1 and 2 disagree; fill Final_Decision.
- Record exclusion reasons using standardized labels from references/screening-labels.md.
- Quality/RoB assessment: Assess included studies using the tool appropriate for the study design.
- Create included.bib from final included studies.
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 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uvclaude
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
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 Screening Quality loads about 2.2k tokens when it runs, and up to ~8.4k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 754 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 754 words (~2,195 tokens).
“Screen search results, document decisions, and assess risk of bias or quality.”
- name
- ma-screening-quality
Files
SKILL.md and 5 other files (scripts, references) in ma-screening-quality of htlin222/meta-pipe.
- SKILL.md
- references/dual-review-schema.md
- references/rayyan-setup.md
- references/screening-labels.md
- references/screening-quickstart-template.md
- scripts/dual_review_agreement.py
Open the folder on GitHubat commit 5c5c3f0
Compare with similar skills
Ma Screening Quality 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 Screening Quality this skillhtlin222/meta-pipe | 139 | — | ~2.2k | Automated safety check: Pass | Custom licence | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.9k | 17 repos | ~5.9k | Automated safety check: Notes | MIT |
Similar skills
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.
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.
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.
Peer Review
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
Last30days
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
More from htlin222/meta-pipe
All 15 skills in this repoMa Data Extraction
htlin222/meta-pipe
Define extraction schema, extract study data from full texts, and store it in a structured database for meta-analysis.
Ma 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 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
Categories
Questions about Ma Screening Quality
What does Ma Screening Quality do?
Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias. Ma Screening Quality is an agent skill from htlin222/meta-pipe. Perform title and abstract screening, apply inclusion and exclusion criteria, and assess study quality or risk of bias.
When should I use Ma Screening Quality?
Ma Screening Quality fits situations like: selecting eligible studies for meta-analysis.
How do I install Ma Screening Quality in Claude Code?
Run `npx skills add htlin222/meta-pipe --skill ma-screening-quality -a claude-code`. Or copy the skill folder (ma-screening-quality in htlin222/meta-pipe) into .claude/skills/ma-screening-quality in your project. Claude Code loads it when a task matches its description.
How do I install Ma Screening Quality in Codex?
Run `npx skills add htlin222/meta-pipe --skill ma-screening-quality -a codex`. Or copy the skill folder (ma-screening-quality in htlin222/meta-pipe) into .agents/skills/ma-screening-quality in your project. Codex loads it when a task matches its description.
Can I use Ma Screening Quality 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-screening-quality -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-screening-quality, .gemini/skills/ma-screening-quality, .github/skills/ma-screening-quality and .opencode/skills/ma-screening-quality in your project.
What does Ma Screening Quality need to run?
Going by SKILL.md and its folder, Ma Screening Quality needs Python for the scripts in its folder and the command-line tools its instructions call (uv and claude). Our summary lists: Python 3.
Does Ma Screening Quality access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Ma Screening Quality 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 Screening Quality use?
Ma Screening Quality 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 Screening Quality use?
About 2.2k tokens (SKILL.md is roughly 8.8k 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 6.2k tokens, read only when the agent opens those files.
What are the alternatives to Ma Screening Quality?
Skills that share tags, products or a category with Ma Screening Quality: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ma Screening Quality?
htlin222 (a GitHub user) maintains it in htlin222/meta-pipe, which has 139 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.