Borrowing it
Nothing to install: this file belongs to LeanOS-Technologies/strategy-os. 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/LeanOS-Technologies/strategy-os/main/.claude/skills/stg-sizing-markets/SKILL.mdgit clone --depth 1 https://github.com/LeanOS-Technologies/strategy-osWrote 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/leanos-technologies/strategy-os/stg-sizing-markets)<a href="https://agentmods.dev/skills/leanos-technologies/strategy-os/stg-sizing-markets"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-sizing-markets/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/leanos-technologies/strategy-os/stg-sizing-markets"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-sizing-markets.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 | $0.00042 | $0.01472 |
| Opus 5 | $0.00021 | $0.00736 |
| Sonnet 5 | $0.00008 | $0.00294 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
stg-sizing-markets 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 10d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Sizing
Calculate market opportunity with TAM/SAM/SOM and timing analysis. All figures are range-based, tier-labeled, and source-cited.
Procedure
Step 1: Load Context [S]
Read governor input: business mode (VENTURE/BOOTSTRAP/HYBRID), constraints (budget, team, timeline), problem space description.
Produce: research parameters (industry, geography, segment filters).
Gate: context_loaded: bool -- mode identified, problem space described.
- Pass: Step 2.
- Fail: Cannot proceed without mode and problem space. Report missing inputs.
Step 2: Calculate TAM Using Both Methods [S]
Read: research parameters.
WebSearch for industry reports (Gartner, Forrester, IBISWorld, Statista, Grand View Research).
Top-down: Industry size x relevant segment percentage. Cite specific report, publisher, date, figure.
Bottom-up: Total potential customers x average revenue per customer. Cite customer count source and revenue assumption separately.
If methods differ >50%, investigate the discrepancy and report both with reconciliation attempt.
Produce: TAM range [low, high] with sources. Each figure labeled T1 (from published report) or T2 (calculated from multiple sources).
Gate: tam_calculated: bool -- two methods attempted, range produced, sources cited with dates.
- Pass: Step 3.
- Fail: If no reports found, widen search terms (adjacent industry, broader category). If still nothing, report: "TAM data insufficient -- mark as T3 assumption" and provide best available estimate.
Step 3: Calculate SAM [S]
Read: TAM range, segment filters from context.
Apply filters sequentially to TAM:
- Geographic filter: reduce by geography relevance (cite source for geographic distribution)
- Segment filter: reduce by target segment proportion (cite source)
- Technical filter: reduce by technology compatibility requirements
- Vertical filter: reduce by industry vertical if applicable
Document each filter: what it is, reduction percentage, source, tier label.
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.
- 10d ago First seen · 139 lines · 42 tokens per session scan A 214a9327f825
stg-sizing-markets is a skill published in the GitHub repository LeanOS-Technologies/strategy-os (37 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 1,472 once invoked, about $0.0002 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 2026-08-30.
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