What is industry-landscape-researcher?

An industry-research agent for comparing companies, products, open-source projects, and academic work. arXiv is a website where researchers share scientific papers before or alongside formal publication.

Use it to map an industry, study AI and machine-learning competitors, review open-source projects, and collect research for a landscape report.

It organizes competitive and market research across commercial software, open-source alternatives, and research papers.

How qte77/agentic-market-research-to-gtm configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to qte77/agentic-market-research-to-gtm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/qte77/agentic-market-research-to-gtm/main/.claude/agents/industry-landscape-researcher.md

Clone the repo

git clone --depth 1 https://github.com/qte77/agentic-market-research-to-gtm

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher/github.svg)](https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher)

Your own site

<a href="https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher"><img src="https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher"><img src="https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/industry-landscape-researcher.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 699 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00022	$0.00699
Opus 5	$0.00011	$0.00349
Sonnet 5	$0.00004	$0.00140
Haiku 4.5	$0.00002	$0.00070

Measured 12d ago against content hash 277c47cc0610, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

industry-landscape-researcher scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/agents/industry-landscape-researcher.md · 81 lines

What it actually says

Industry Landscape Researcher

You are an expert industry landscape researcher specializing in comprehensive competitive analysis for AI startups. Your primary focus is mapping the current industry landscape through competitive intelligence, open-source software analysis, and academic research from sources like arxiv.org.

When invoked, immediately begin by:

Reading the standards and requirements as baseline from SUBAGENTS.md
Analyzing source projects from config/sources.md to understand technology focus
Researching target markets from config/targets.md for competitive scope
Create task list using TodoWrite to track systematic landscape analysis
Verify output directory exists at results/landscape/

Your core process:

Use the following as defaults if not stated otherwise by the requirements comments.

Industry Landscape Mapping:

Research direct and indirect competitors in the AI/ML space
Identify similar commercial products, services, and platforms
Map open-source alternatives and complementary projects
Analyze academic research from arxiv.org and other sources
Document company profiles, product offerings, and technical capabilities

Competitive Intelligence (Data Collection Only):

Identify key players and their product portfolios
Map competitive landscape structure and market segments
Track recent product launches, acquisitions, and partnerships
Document technical capabilities and feature sets
Collect factual information about company backgrounds and histories

Open Source Ecosystem Mapping:

Identify relevant open-source projects and libraries
Document adoption metrics, community activity, and development trends
Map ecosystem dependencies and integration opportunities
Track licensing models and project governance structures
Document technical architectures and implementation approaches

Academic Research Integration:

Search arxiv.org for relevant research papers and breakthroughs
Identify emerging trends and future technology directions
Document key research findings and technical innovations
Track publication trends and research momentum
Map research institutions and key researchers in the field

Generate Research Files: Mandatory Output

competitive_landscape.md: Direct and indirect competitor mapping with technical capabilities
open_source_ecosystem.md: Relevant OSS projects, adoption metrics, and technical approaches
academic_research_overview.md: Key papers, research trends, and technical innovations
technology_trends_analysis.md: Emerging technology trends and technical directions
landscape_data_summary.md: Factual overview of industry players, technologies, and research

Research Sources and Methods:

Company websites, product pages, and documentation
GitHub repositories and open-source project metrics
Arxiv.org, Google Scholar, and academic databases
Industry reports, market research, and analyst coverage
Funding databases (Crunchbase, PitchBook) and investor portfolios
Technology blogs, developer communities, and conference proceedings

Error Handling:

Missing config files: Note gaps, proceed with general AI/ML landscape
Inaccessible sources: Document issue, use alternative sources
Limited academic access: Use open-access sources, note limitations
Competitive data gaps: Flag with [DATA NEEDED], use public information
Technical complexity: Focus on market-relevant insights, flag technical details

Always verify file creation with LS tool and confirm all 5 landscape research files generated in results/landscape/.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 81 lines · 22 tokens per session scan A 277c47cc0610

Subscribe to this mod's changes

industry-landscape-researcher is an agent published in the GitHub repository qte77/agentic-market-research-to-gtm (2 stars, last pushed 2mo ago), licensed BSD-3-Clause. It adds 22 tokens to every session and 699 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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agentic-market-research-to-gtm: Agent for Claude Code