---
name: meta-results-sensitivity-analysis
description: Generates the "Results" section for meta-analysis sensitivity analysis based on statistical tables and titles. Use when the user wants to describe sensitivity analysis results or format sensitivity tables for a meta-analysis paper.
license: MIT
author: aipoch
---
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
Use this skill when:
1. The user provides a sensitivity analysis table (Leave-One-Out) and wants a textual description.
2. The user needs to format the "Results" section for a meta-analysis paper regarding sensitivity checks.
3. The user specifies a target language (Chinese or English) for the output.
## Key Features
- Scope-focused workflow aligned to: Generates the "Results" section for meta-analysis sensitivity analysis based on statistical tables and titles. Use when the user wants to describe sensitivity analysis results or format sensitivity tables for a meta-analysis paper.
- Packaged executable path(s): `scripts/validate_skill.py`.
- 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
See `## Usage` above for related details.
```bash
cd "20260316/scientific-skills/Academic Writing/meta-results-sensitivity-analysis"
python -m py_compile scripts/validate_skill.py
python scripts/validate_skill.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/validate_skill.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/validate_skill.py`.
- 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:
```bash
python scripts/validate_skill.py --help
```
# Meta Sensitivity Analysis Generator
This skill generates a descriptive "Results" section for meta-analysis sensitivity analysis. It processes statistical tables (Leave-One-Out method), generates a textual description using an LLM, and formats the output with proper table citations and legends.
## Workflow
1. **Generate Description**: The LLM describes the sensitivity analysis table based on the meta-analysis title and outcome name.
2. **Format Output**: A script inserts the table citation (e.g., `(Table 5)`) and formats the table with a standard legend.
## Usage
### Input Parameters
* `title` (optional): Title of the meta-analysis.
* `sensitivity_table` (optional): The raw statistical table data.
* `language` (required): Output language (`Chinese` or `English`).
* `outcome_name` (optional): Name of the outcome indicator.
### Example
```python
from scripts.format_result import format_sensitivity_result
# 1. LLM generates the description (simulated)
# description = llm.generate(prompt="Describe the sensitivity table...", context=inputs)
# 2. Script formats the final result
# final_output = format_sensitivity_result(
# text=description,
# table_data=inputs['sensitivity_table'],
# language=inputs['language']
# )
```
## Quality Rules
1. **Language**: Output must be strictly in the user-specified language.
2. **Formatting**: Remove any JSON formatting from LLM output.
3. **Citation**: Must insert table citation (Table 5) before the last punctuation of the description.
## 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_results_sensitivity_analysis_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:
```text
No local script validation step is required for this skill.
```
Expected output format:
```text
Result file: meta_results_sensitivity_analysis_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.