| name | meta-abstract-screener |
| description | 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. |
| license | MIT |
| author | AIPOCH |
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:
- Confirm the user input, output path, and any required config values.
- Edit the in-file
CONFIG block or documented parameters if the script uses fixed settings.
- Run
python scripts/screen_paper.py with the validated inputs.
- 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:
-
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".
-
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.
-
Validation (Optional)
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.
Input Validation
This skill accepts requests that match the documented purpose of meta-abstract-screener and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
meta-abstract-screener only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
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
