Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OneWave-AI/claude-skills --skill market-sizinggit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote 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/onewave-ai/claude-skills/market-sizing)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/market-sizing"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/market-sizing/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/onewave-ai/claude-skills/market-sizing"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/market-sizing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.00730 |
| Opus 5 | $0.00039 | $0.00365 |
| Sonnet 5 | $0.00016 | $0.00146 |
| Haiku 4.5 | $0.00008 | $0.00073 |
Grade A, and why
market-sizing 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sizing Agent
Produce rigorous, investor-grade TAM/SAM/SOM analyses by combining top-down macro data with bottom-up unit economics, triangulating the two, and always showing the work, citing sources, flagging assumptions, and providing sensitivity ranges.
Contents
references/research-sources.md— source categories and search queries for every research lane.references/methodology.md— top-down, bottom-up, triangulation, sensitivity, growth, competitive sizing, and pitfalls.references/output-template.md— the fullmarket-sizing.mddocument template, Mermaid charts, and quality checklist.
Inputs
Confirm these four inputs before proceeding. If any is missing or ambiguous, ask first.
| Parameter | Description | Example |
|---|---|---|
| Industry | The broad industry or sector | "Enterprise SaaS", "Electric Vehicles" |
| Product/Service | The specific offering being sized | "AI-powered code review tool" |
| Geography | Target market geography | "United States", "Global", "DACH region" |
| Target Segment | The specific customer segment | "Mid-market companies (100-1000 employees)" |
Workflow
- Confirm the four inputs with the user; resolve any ambiguity before research.
- Research first. Gather and cite data across all four lanes (industry data, competitor revenue, growth rates, unit economics). See
references/research-sources.md. Log every source URL and date as you go. - Run the top-down calculation: broadest market figure, then geographic, segment, and product-fit filters, then a realistic SOM capture rate. See
references/methodology.md. - Run the bottom-up calculation: customer count times average revenue per customer, narrowed to reachable and obtainable. See
references/methodology.md. - Triangulate top-down and bottom-up, explain any divergence over 2x, and produce a weighted best estimate.
- Run sensitivity analysis: conservative, base, and aggressive scenarios plus the top 3-5 swing variables.
- Project market size forward 5 years and size the competitive landscape (share distribution, top competitors, barriers, positioning).
- Generate
market-sizing.mdusing the structure inreferences/output-template.md. Show all math, cite every figure, and verify against the quality checklist before delivering. - Present the result, then offer to adjust assumptions, explore alternative market definitions, or drill deeper.
What ships with it
3 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.
- 9d ago First seen · 49 lines · 79 tokens per session scan A 9a323b766983
market-sizing is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 730 once invoked, about $0.0004 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 2026-09-03.
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