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.

Required Inputs

• A clearly specified task goal aligned with the documented scope.
• All required files, identifiers, parameters, or environment variables before execution.
• Any domain constraints, formatting requirements, and expected output destination if applicable.

Output Contract

• Return a structured deliverable that is directly usable without reformatting.
• If a file is produced, prefer a deterministic output name such as

  meta_abstract_screener_result.md

  unless the skill documentation defines a better convention.
• Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

• Validate required inputs before execution and stop early when mandatory fields or files are missing.
• Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
• Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
• Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

• If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
• If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
• If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

python scripts/screen_paper.py --help

Expected output format:

Result file: meta_abstract_screener_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any