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market-research-reports

Helps your agent create market research reports with evidence. It can also generate market sizing or forecast scenarios based on assumptions.

What it does

Creates detailed and traceable market research reports. It helps understand markets, customers, and competitors, with verifiable data.

When to use it

  • ✓To define a new market.
  • ✓To understand the competition.
  • ✓To forecast market size.

Example of what to ask your AI

“Create a market research report on the solar energy sector in Brazil, focusing on market size and key players.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/market-research-reports/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/market-research-reports/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The AI gathers information from reliable sources, organizes the data, and presents a structured report. It separates facts from estimates and shows where the information comes from.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add K-Dense-AI/scientific-agent-skills --skill market-research-reports

Or download just the file:

curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/market-research-reports/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Market Research Reports

Purpose

Create decision-focused market reports whose claims, calculations, assumptions, and uncertainties can be audited. Match depth and format to the question and evidence. There is no required length, chapter count, visual count, or output format.

Do not:

  • imitate or imply affiliation with a consulting, analyst, or research brand;
  • invent citations, quotes, market shares, or paid-market figures;
  • present TAM/SAM/SOM or a forecast as one certain truth;
  • treat a framework, chart, or fluent narrative as evidence;
  • provide investment, legal, antitrust, tax, accounting, or regulatory advice.

Operating principles

  1. Define before sizing. Fix product, customer, geography, channel, period, measure, unit, denominator, currency/base year, and taxonomy.
  2. Map every claim. Every factual or quantitative claim has a claim ID and exact source IDs.
  3. Separate statement types. Distinguish facts, estimates, calculations, forecasts, opinions, and recommendations.
  4. Prefer primary evidence. Use official statistics, regulator records, filed company disclosures, and transparent original studies before secondary synthesis.
  5. Preserve uncertainty. Retain source conflicts, revisions, scenario ranges, sensitivity, and limitations.
  6. Keep methods reproducible. Use local structured inputs and deterministic calculations when practical.
  7. Collect lawfully and ethically. No deception, PII disclosure, access circumvention, confidential material, or trade-secret acquisition.

Workflow

1. Establish the research contract

Clarify:

  • decision, audience, deadline, and materiality threshold;
  • formal market definition and adjacent exclusions;
  • buyer, payer, user, transaction, and value-chain level;
  • geography and treatment of imports, exports, and channels;
  • historical period, forecast period, and retrieval cutoff;
  • revenue/expenditure, gross output/value added, units, capacity, users, or another measure;
  • stock/flow, gross/net, taxes, and denominator;
  • currency, base year, and nominal/real/current/constant basis;
  • industry and product classification with version;
  • permitted data sources, primary research, confidentiality, and output format.

Ask a focused question when a missing choice would materially change the denominator or result. Otherwise state a provisional scope and proceed.

Use references/report_structure_guide.md for modular report design.

2. Build the evidence plan

Route each question to the source closest to the underlying event:

  1. primary law, regulator decision, official filing, or official statistic;
  2. original company filing or attributable first-party disclosure;
  3. transparent survey/study with inspectable methods;
  4. institutional or peer-reviewed research using identifiable primary data;
  5. industry association data with disclosed coverage;
  6. reputable secondary synthesis;
  7. lawfully accessed paid estimate with inspectable scope and method;
  8. news/commentary for leads or attributable events.

For company data, prefer the official filing system in the relevant jurisdiction. For industry, labor, prices, population, trade, and national accounts, prefer the responsible national statistical agency or central bank. For cross-country work, use harmonized World Bank, IMF, OECD, or Eurostat data only after checking definitions and original-source lineage.

Read references/official_data_sources.md before using public APIs. API rules and limits are a dated snapshot: verify current official terms before automated or high-volume retrieval. Never put an API key in a report or bundled script.

3. Create the source ledger

Assign stable IDs (S-001, S-002, ...). Record:

  • title, publisher, URL/persistent ID, source type;
  • publication date and retrieval date;
  • original producer when accessed through an aggregator;
  • geography, covered population, period, and vintage;
  • currency, base year, price basis, measure type, unit, and denominator;
  • taxonomy and version;
  • preliminary/revised/final/current status;
  • method, sample, imputation, suppression, and limitations;
  • license/terms and lawful local snapshot path.

Use assets/source_ledger_template.csv and validate it:

python3 scripts/validate_evidence_ledger.py data/source_ledger.csv

If publication date is unavailable, record not-stated; do not guess.

4. Maintain a claims ledger

…

FAQ

Does the AI invent data?

No, the AI only uses information from sources you can verify and does not invent numbers or citations.

Can I trust the numbers?

Yes, the AI shows the origin of each data point and separates facts from estimates or forecasts, indicating uncertainties.

Is the report like those from famous consulting firms?

The AI creates organized, evidence-based reports but does not imitate consulting brands or offer financial or legal advice.

Details

Original description by the author

Builds evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.

repo: K-Dense-AI/scientific-agent-skills

Popularity

GitHub stars
51k
Ranking position
#1901
License
MIT
Updated
10/15/2026

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