Claude Skill MartBrowse skillsQuick linesLearn by videoTerminal guideWhat is a Skill?
 
← Back to list

Market Research Reports

An audit-ready workflow for market reports where every claim carries source IDs and TAM/SAM/SOM plus forecasts are treated as scenarios.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Forces a research contract first: market definition, buyer/payer, geography, period, measure, unit, currency, base year, and taxonomy version.
  • Builds a source ledger (S-001…) and claims ledger (C-001…) so every factual or quantitative statement maps to exact evidence.
  • Computes top-down and bottom-up TAM independently with disjoint coverage rules (no adding manufacturer revenue to distributor spend, no parent+subsidiary double counting), then derives SAM/SOM per scenario.
  • Handles forecasts with explicit uncertainty: observed vs. estimated vs. forecast periods, sensitivity, switching values, and invalidation conditions.
  • Ships local, standard-library Python CLIs for ledger validation, citation auditing, sizing, forecast sensitivity, competitor matrix checks, unit consistency, and report scaffolding — plus optional LaTeX templates.

Who it's for

  • Strategy, corporate development, and product marketing teams who must defend their numbers.
  • Founders, VC analysts, consultants, and industry researchers.
  • Any team that needs reproducible methodology and traceable citations.

Example uses

  1. "Size the EU industrial IoT gateway market with downside/base/upside scenarios" → run calculate_market_sizing.py and report the top-down vs. bottom-up gap.
  2. "Audit my draft: which claims lack a valid source ID?" → audit_claim_citations.py against claims.csv and the source ledger.
  3. "Build a competitor feature matrix for six vendors on the same product edition and as-of date, marking gaps as unknown" → validate_competitor_matrix.py.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/market-research-reports/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/market-research-reports folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/market-research-reports/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/market-research-reports ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills directory: mkdir -p ~/.claude/skills
  4. Copy the skill: cp -r scientific-agent-skills/skills/market-research-reports ~/.claude/skills/
  5. Verify Python 3.11+ with python3 --version (bundled CLIs use only the standard library and make no network calls).
  6. Restart Claude Code and ask something like "Build an evidence-traceable market sizing report for X" to trigger the skill.
  7. Optional: install XeLaTeX or LuaLaTeX if you want to use the bundled LaTeX report template.
View source on GitHub ↗License: MIT