Meta Abstract Screener

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

Abstract Screener

This skill helps screen research papers by analyzing their titles and abstracts against specific inclusion/exclusion criteria. It follows a rigorous two-step process to ensure consistency and strictly excludes systematic reviews/meta-analyses unless otherwise specified.

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Screens research papers based on title/abstract and inclusion criteria, providing a structured Yes/No/Maybe decision. Use when you need to filter literature for meta-analysis or systematic reviews.
• Packaged executable path(s): scripts/screen_paper.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

cd "20260316/scientific-skills/Data Analytics/meta-abstract-screener"
python -m py_compile scripts/screen_paper.py
python scripts/screen_paper.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/screen_paper.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/screen_paper.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.

Workflow

To screen a paper, follow this process:

1. Analysis Phase

   • Read the Paper Title and Abstract and the Inclusion/Exclusion Criteria.
   • Apply the screening logic defined in references/screening_prompts.md (Step 1).
   • Note: Be particularly vigilant about excluding other "Systematic Reviews" or "Meta-analyses".

2. Formatting Phase

   • Take the conclusion from the Analysis Phase.
   • Format it into a JSON object using the schema defined in references/screening_prompts.md (Step 2).
   • The output must contain strictly Result and Reason.

3. Validation (Optional)

   • If you need to verify the output format programmatically, use the included script:

     python scripts/screen_paper.py '<json_output>'

Resources

• Prompts: references/screening_prompts.md - Contains the detailed role definitions and logic for the LLM.
• Validation: scripts/screen_paper.py - Ensures the output JSON matches the required schema.

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.