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 Agent-Engineer-Master/skill-engineer --skill size-marketgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote 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/agent-engineer-master/skill-engineer/size-market)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/size-market"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/size-market/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/agent-engineer-master/skill-engineer/size-market"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/size-market.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 | $0.00176 | $0.02184 |
| Opus 5.5 | $0.00070 | $0.00874 |
| Sonnet 5.5 | $0.00035 | $0.00437 |
| Haiku 4.5 | $0.00018 | $0.00218 |
Grade C, and why
size-market scanned grade C with 1 finding 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Size Market
For a defined industry or sub-segment, produce a granular market sizing using McKinsey G3 decomposition + arenas qualification screen. Output: market-sizing.md with explicit de-averaging.
The discipline (McKinsey G3 / granular growth): aggregate market growth rates are misleading. G3-level sub-segment portfolio choice explains ~65% of organic top-line growth. Always decompose before accepting any aggregate rate.
Iron rules:
- Every numeric claim carries a V/C/A/I tag — see
../_shared/provenance-tagging.md. - ≥3 sub-segment growth rates required (Quick mode) or ≥5 (Deep mode). Single-rate sizing fails validation.
- Top-down + bottom-up triangulation required; gap >25% triggers reconciliation; gap <5% triggers circular-sourcing check.
- Arenas screen run — does the market qualify (high growth + high dynamism) per McKinsey 2024 criteria?
- Definition locked at intake; base currency declared; sources <24mo old (or justified as still current).
Process
1. Intake — lock the analysis frame
Confirm and write to output header: industry slug, geographic scope, base currency (default USD), reporting year (current 2026), time horizon (current + 3-5yr), depth (Quick = TAM+SAM + ≥3 G3 / Deep = TAM+SAM+SOM + ≥5 G3 + share-shift data), and a one-sentence definition lock (what's in, what's out). Read references/sizing-methodology.md "Intake" before research.
2. Top-down sizing
Read references/sizing-methodology.md "Top-down" + references/data-sources.md. Source order: regulatory filings, trade bodies, government stats, syndicated paid (IBISWorld, Gartner, etc.), sell-side analyst notes. Never cite an AI aggregator (Perplexity, ChatGPT) without the underlying source. Capture: total market value, currency, year, geographic basis, definition used. Reconcile to locked definition. Every figure tagged V/C/A/I with report name + year + section.
3. Bottom-up sizing
Read references/sizing-methodology.md "Bottom-up". Estimate via volume × price, customers × spend × penetration, or value-theory (benefit × capture rate). Use independent sources — not the same report as top-down (circular sourcing fails). Apply the 10-customer test: can you name 10 specific customers in this market? Every assumption tagged. Software/SaaS markets (industry slug or definition contains any of: saas, software, cloud, platform, api, developer tools, observability, security software, fintech-software) must include value-theory as a required third triangulation leg — unit counts are noisy and value-per-customer is more defensible.
What ships with it
7 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 First seen · 87 lines · 176 tokens per session scan C 709f1f1ae509
size-market is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 3mo ago), licensed MIT. It adds 176 tokens to every session and 2,184 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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openspec-new-change
Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach. Also use when the user says "openspec new change" or "opsx new".
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
extract
Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
writing
A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.