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 yonatangross/orchestkit --skill market-sizinggit clone --depth 1 https://github.com/yonatangross/orchestkitWrote 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/yonatangross/orchestkit/market-sizing)<a href="https://agentmods.dev/skills/yonatangross/orchestkit/market-sizing"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/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/yonatangross/orchestkit/market-sizing"><img src="https://agentmods.dev/badge/skills/yonatangross/orchestkit/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 | $0.00058 | $0.01226 |
| Opus 5.5 | $0.00023 | $0.00490 |
| Sonnet 5.5 | $0.00012 | $0.00245 |
| Haiku 4.5 | $0.00006 | $0.00123 |
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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sizing
TAM/SAM/SOM framework for estimating addressable market and validating opportunity size.
Definitions
| Metric | Definition | Question It Answers |
|---|---|---|
| TAM | Total Addressable Market — everyone who could possibly buy | "What's the theoretical ceiling?" |
| SAM | Serviceable Addressable Market — segment you can actually reach | "What can we realistically target?" |
| SOM | Serviceable Obtainable Market — realistic share in 3 years | "What will we actually capture?" |
+-------------------------------------------------------+
| TAM |
| +---------------------------------------------------+ |
| | SAM | |
| | +-----------------------------------------------+| |
| | | SOM || |
| | +-----------------------------------------------+| |
| +---------------------------------------------------+ |
+-------------------------------------------------------+
When to Use Top-Down vs. Bottom-Up
| Method | Use When | Risk |
|---|---|---|
| Top-Down | Industry reports exist; investor pitch; quick estimate | Overestimates SOM |
| Bottom-Up | Sales capacity known; pricing validated; more credible | More work; requires assumptions |
| Both (recommended) | High-stakes decisions; fundraising; board decks | Cross-validate to build confidence |
Always cross-validate both methods and reconcile within 20%. If they diverge by more than that, revisit your assumptions.
Quick Formulas
Top-Down
TAM = (# potential customers) × (annual value per customer)
SAM = TAM × (% your solution can address)
SOM = SAM × (realistic market share % in 3 years)
Bottom-Up
SOM = (# customers you can acquire) × (average deal size)
SAM = SOM / (your expected market share %)
TAM = SAM / (your segment as % of total market)
Example: AI Code Review Tool
What ships with it
4 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 Changed · -1 lines 7dccb068fa62
- 17d ago Changed ad148e6cbd3b
- 18d ago Changed · -1 lines e8d15b2536b7
- 26d ago Changed 7952d9397c8f
- 1mo ago First seen · 151 lines · 58 tokens per session scan A b8ebac2e2e3f
market-sizing is a skill published in the GitHub repository yonatangross/orchestkit (295 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 1,226 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 2026-09-03.
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