Source: 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.

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:

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

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:

No local script validation step is required for this skill.

Expected output format:

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.