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name: market-research-report-generator description: Generates professional market research reports by analyzing business intent, decision levels, and conducting multi-source data retrieval (Web, PubMed, Clinical Trials). license: MIT author: AIPOCH

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

Market Research Report Generator

This skill generates comprehensive market research reports based on a topic and optional requirements. It follows a strict workflow: Intent Analysis -> Decision Level Analysis -> Question Generation -> Data Collection -> Report Synthesis.

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 professional market research reports by analyzing business intent, decision levels, and conducting multi-source data retrieval (Web, PubMed, Clinical Trials).
• Packaged executable path(s): scripts/research_orchestrator.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/market-research-report-generator"
python -m py_compile scripts/research_orchestrator.py
python scripts/research_orchestrator.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/research_orchestrator.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/research_orchestrator.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.

Input

• topic (required): The main subject of the research (e.g., "Low-altitude economy", "Humanoid robots").
• requirements (optional): Specific focus areas or constraints.

Output

• A Markdown report containing Executive Summary, Market Overview, Competitive Landscape, Technical/Clinical Analysis, and Strategic Recommendations.

Workflow

1. Intent & Strategy Analysis

First, analyze the user's request to determine the business intent and the target audience's decision-making level.

• Intent Analysis: Classify the request into categories like Market Entry, Investment, or Product Strategy. Refer to references/intent_classification.md for guidelines.
• Decision Level: Determine if the report is for C-Level (strategic, concise), VP/Director (tactical, detailed), or R&D (technical). Refer to references/decision_level.md.

2. Core Question Generation

Based on the intent and level, generate 5-7 core questions that the research must answer.

• For Investment reports, focus on ROI, CAGR, and risks.
• For Product Strategy, focus on features, competitors, and user needs.
• For C-Level, prioritize high-level trends and financial impact.

3. Data Collection (Multi-Source)

You must collect data from multiple sources to ensure accuracy and depth. Do NOT make up data. Use the following tools:

A. General Market Search (If available)

If the environment provides a web search capability (e.g., WebSearch tool):

• Generate 3-5 distinct search queries based on the Core Questions.
• Find market size, trends, and news.

B. Clinical/Medical Search (If applicable)

If the topic is related to healthcare, medicine, or bio-tech:

• Unified Database Search: Use the provided script to query both clinicaltrials.gov and PubMed simultaneously.
  • Command: python scripts/research_orchestrator.py '["query1", "query2"]'
  • The script will return JSON data containing results from both sources.

4. Data Aggregation & Synthesis

• Review all gathered information.
• Cross-reference numbers (e.g., market size predictions) from different sources.
• Highlight conflicts or uncertainties.

5. Report Generation

Write the final report in Markdown.

• Tone: Professional, objective, and aligned with the Decision Level (e.g., "Strategic & Direct" for C-Level).
• Structure:
  1. Executive Summary: Key findings and bottom-line recommendations (BLUF).
  2. Market Overview: Size, growth (CAGR), and drivers.
  3. Competitive Landscape: Key players and their market share/positioning.
  4. Technical/Clinical Analysis: (If applicable) Technology maturity or clinical evidence.
  5. Strategic Recommendations: Actionable steps based on the Intent.

Quality Rules

• QR-INTENT-001: The report must directly address the identified Business Intent.
• QR-LEVEL-001: The language complexity must match the Decision Level.
• QR-SOURCE-001: You must cite sources (e.g., "According to Gartner...", "ClinicalTrials.gov data shows...").

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 market_research_report_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/research_orchestrator.py --help

Expected output format:

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