Auto-Company is a software system that runs a recurring team of AI agents on a personal computer, where the agents plan work, make decisions, write code, deploy applications, and handle marketing. It is intended for autonomous product development on Windows, Linux, or macOS, using command-line agent tools and a local dashboard to track cycles and results. The catalogue entries are skills, agents, settings, and instructions that support this automated workflow.
Borrowing it
Nothing to install: this file belongs to MaxMiksa/Auto-Company. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MaxMiksa/Auto-Company/main/.claude/skills/market-sizing-analysis/SKILL.mdgit clone --depth 1 https://github.com/MaxMiksa/Auto-CompanyWrote 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.
[](https://agentmods.dev/skills/maxmiksa/auto-company/market-sizing-analysis)<a href="https://agentmods.dev/skills/maxmiksa/auto-company/market-sizing-analysis"><img src="https://agentmods.dev/badge/skills/maxmiksa/auto-company/market-sizing-analysis/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.
<a href="https://agentmods.dev/skills/maxmiksa/auto-company/market-sizing-analysis"><img src="https://agentmods.dev/badge/skills/maxmiksa/auto-company/market-sizing-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00058 | $0.02671 |
| Opus 5.5 | $0.00023 | $0.01068 |
| Sonnet 5.5 | $0.00012 | $0.00534 |
| Haiku 4.5 | $0.00006 | $0.00267 |
Grade A, and why
market-sizing-analysis 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 1mo 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- market-sizing-analysis — 91% identical, 23 lines differ
- market-sizing-analysis — 91% identical, 86 lines differ
- market-sizing-analysis — 88% identical, 89 lines differ
How it starts
The opening of the file, as written. The whole thing — 439 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sizing Analysis
Comprehensive market sizing methodologies for calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for startup opportunities.
Overview
Market sizing provides the foundation for startup strategy, fundraising, and business planning. Calculate market opportunity using three complementary methodologies: top-down (industry reports), bottom-up (customer segment calculations), and value theory (willingness to pay).
Core Concepts
The Three-Tier Market Framework
TAM (Total Addressable Market)
- Total revenue opportunity if achieving 100% market share
- Defines the universe of potential customers
- Used for long-term vision and market validation
- Example: All email marketing software revenue globally
SAM (Serviceable Available Market)
- Portion of TAM targetable with current product/service
- Accounts for geographic, segment, or capability constraints
- Represents realistic addressable opportunity
- Example: AI-powered email marketing for e-commerce in North America
SOM (Serviceable Obtainable Market)
- Realistic market share achievable in 3-5 years
- Accounts for competition, resources, and market dynamics
- Used for financial projections and fundraising
- Example: 2-5% of SAM based on competitive landscape
When to Use Each Methodology
Top-Down Analysis
- Use when established market research exists
- Best for mature, well-defined markets
- Validates market existence and growth
- Starts with industry reports and narrows down
Bottom-Up Analysis
- Use when targeting specific customer segments
- Best for new or niche markets
- Most credible for investors
- Builds from customer data and pricing
Value Theory
- Use when creating new market categories
- Best for disruptive innovations
- Estimates based on value creation
- Calculates willingness to pay for problem solution
Three-Methodology Framework
Methodology 1: Top-Down Analysis
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · -13 lines 3ce515d82f95
- 1mo ago First seen · 452 lines · 58 tokens per session scan A f0011cf9f820
market-sizing-analysis is a skill published in the GitHub repository MaxMiksa/Auto-Company (3,156 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 2,671 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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