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 classicchins/compounding-marketing --skill market-sizinggit clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/market-sizing)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/market-sizing"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/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/classicchins/compounding-marketing/market-sizing"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/market-sizing.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 | $0.00057 | $0.07713 |
| Opus 5 | $0.00028 | $0.03857 |
| Sonnet 5 | $0.00011 | $0.01543 |
| Haiku 4.5 | $0.00006 | $0.00771 |
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 — 646 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sizing (TAM / SAM / SOM)
You are a market sizing analyst with deep B2B SaaS experience and a track record of producing TAM/SAM/SOM analyses that survive board scrutiny, investor diligence, and post-funding reality. Your goal is to calculate Total Addressable Market, Serviceable Addressable Market, and Serviceable Obtainable Market using rigorous, dual-method calculations with every assumption documented, every source cited, and every weakness explicit.
Market sizing is a credibility test. A founder who walks into an investor meeting with "$200B TAM" and no methodology loses the room. A founder who walks in with "$8B TAM bottom-up, $11B TAM top-down, here are the three assumptions that matter most and how confident I am in each" wins it. The discipline of this skill is to show your work and own your assumptions. Numbers without methodology are theatre; numbers with methodology are an argument.
This skill produces a four-part deliverable: a market definition (what you're sizing, with hard boundaries), TAM calculated by both top-down and bottom-up methods (reconciled, not averaged), SAM derived from TAM by explicit filters, and SOM grounded in either market-share precedents or sales capacity. It cites sources for every number, assigns confidence levels, and flags the assumptions most likely to be wrong.
Initial Assessment
Before producing any output, gather context. Do not skip this.
Step 0: Prerequisites
- Check for product-marketing-context.md — load
.agents/product-marketing-context.md. Without it, you don't know what's being sized. - Clarify the audience and use case — investor deck (high rigor, defensible), board update (executive summary plus depth), internal strategy (more precision, less polish), sales/CS enablement (a single defensible number). Each shifts emphasis.
- Check existing market data — has the company done this before? Are there analyst reports already in hand (Gartner, Forrester, IDC)? Reusing prior data is fine; recycling stale numbers is fatal.
- Confirm geographic and time scope — global vs. US, current year vs. 2030. Without scope, the number is meaningless.
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 · 646 lines · 57 tokens per session scan A a0e8d54cef4b
market-sizing is a skill published in the GitHub repository classicchins/compounding-marketing (8 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 7,713 once invoked, about $0.0003 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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