Meta Criteria Generator

Example Usage

See ## Usage above for related details.

cd "20260316/scientific-skills/Data Analytics/meta-criteria-generator"
python -m py_compile scripts/extract_criteria.py
python scripts/extract_criteria.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/extract_criteria.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation 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/extract_criteria.py with additional helper scripts under scripts/.
• 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.

Validation Shortcut

Run this minimal command first to verify the supported execution path:

python scripts/extract_criteria.py --help

Meta-Analysis Criteria Generator

This skill generates inclusion and exclusion criteria for Meta-Analysis based on the PICO framework (Population, Intervention, Comparator, Outcomes) and Study Design.

Usage

1. Ask for Title/Keywords: If not provided, ask the user for the Meta-Analysis topic.
2. Generate Inclusion Criteria: Use LLM to generate criteria based on the input.
3. Generate Exclusion Criteria: Use LLM to generate exclusion criteria that do not contradict the inclusion criteria.
4. Format Output: Use scripts/extract_criteria.py to extract the final criteria from the LLM outputs and present them clearly.

Workflow Details

Step 1: Generate Inclusion Criteria

Prompt the LLM to act as a Meta-Analysis expert. Input: User provided title/keywords. Requirements:

• Cover P (Population), I (Intervention), C (Comparator), O (Outcomes), S (Study Design).
• Output must be in English.
• Crucial: Enclose the final criteria list in {} for extraction.
• Format: {(1) Participants: ...; (2) Interventions: ...; ...}

Step 2: Generate Exclusion Criteria

Prompt the LLM to generate exclusion criteria. Input: Inclusion Criteria from Step 1, User title. Requirements:

• Must NOT contradict Inclusion Criteria.
• Must NOT repeat Inclusion Criteria.
• Output must be in English.
• Crucial: Enclose the final criteria list in {} for extraction.

Step 3: Extract and Format

Run the extraction script to clean up the outputs.

python scripts/extract_criteria.py --inclusion "<inclusion_text>" --exclusion "<exclusion_text>"

Quality Rules

• Language: All outputs must be in English.
• Format: The final output must be clearly separated into "Inclusion Criteria" and "Exclusion Criteria".
• Consistency: Exclusion criteria must be logically consistent with inclusion criteria.

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.

Recommended Workflow

1. Validate the request against the skill boundary and confirm all required inputs are present.
2. Select the documented execution path and prefer the simplest supported command or procedure.
3. Produce the expected output using the documented file format, schema, or narrative structure.
4. Run a final validation pass for completeness, consistency, and safety before returning the result.

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_criteria_generator_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/extract_criteria.py --help

Expected output format:

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

Deterministic Output Rules

• Use the same section order for every supported request of this skill.
• Keep output field names stable and do not rename documented keys across examples.
• If a value is unavailable, emit an explicit placeholder instead of omitting the field.

Completion Checklist

• Confirm all required inputs were present and valid.
• Confirm the supported execution path completed without unresolved errors.
• Confirm the final deliverable matches the documented format exactly.
• Confirm assumptions, limitations, and warnings are surfaced explicitly.