Meta Abstract Screener

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) 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