expert-interview-generator

Source: https://github.com/aipoch/medical-research-skills

Expert Interview Article Generator

This skill orchestrates the generation of a professional expert interview article, simulating a Dify workflow.

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: Generates a full expert interview article including introduction, Q&A body, and summary based on interview questions and expert background. Use when you have interview questions and an expert profile and need a polished article.
• Packaged executable path(s): scripts/flow.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/Others/expert-interview-generator"
python -m py_compile scripts/flow.py
python scripts/flow.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/flow.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/flow.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.

Inputs

• background (Required): Expert profile (Name, Title, Affiliation, Research Direction, Achievements).
• question (Required): List of interview questions.
• title (Required): Article title.
• text1 (Optional): Existing interview draft content.

Workflow

Step 1: Generate Expert Introduction

Use the Expert Introduction Prompt in references/prompts.md to generate the intro section. Input: background

Step 2: Generate Q&A Body

Determine which generation path to use based on text1:

• Path A (With Draft): If text1 is provided (not empty), use the Body Generation (With Draft) Prompt in references/prompts.md.
  • Inputs: text1, question, background, title
• Path B (No Draft): If text1 is empty, use the Body Generation (No Draft) Prompt in references/prompts.md.
  • Inputs: question, background, title

Constraint: The output must be approximately 2000 words, strictly following the Q&A format defined in the prompt.

Step 3: Generate Preface

Use the Preface Prompt in references/prompts.md to write a 150-word introduction. Inputs: Generated Body (from Step 2), title, background

Step 4: Generate Summary

Use the Summary Prompt in references/prompts.md to write a 150-word conclusion. Inputs: Generated Body (from Step 2), Generated Preface (from Step 3), background, title

Step 5: Final Assembly

Combine the generated sections into a final Markdown article using the structure below. You may use scripts/flow.py to handle text processing if needed, or assemble manually.

Structure:

1. Title: title
2. Preface: (Result from Step 3)
3. Expert Profile: (Result from Step 1)
4. Interview Content: (Result from Step 2)
5. Summary: (Result from Step 4)

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 expert_interview_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/flow.py --help

Expected output format:

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