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 skillmds/skillmd --skill company-teardown-model-revenue-signals-weak-points-on-a-tempgit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/company-teardown-model-revenue-signals-weak-points-on-a-temp)<a href="https://agentmods.dev/skills/skillmds/skillmd/company-teardown-model-revenue-signals-weak-points-on-a-temp"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/company-teardown-model-revenue-signals-weak-points-on-a-temp/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/skillmds/skillmd/company-teardown-model-revenue-signals-weak-points-on-a-temp"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/company-teardown-model-revenue-signals-weak-points-on-a-temp.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 | $0.00014 | $0.00543 |
| Opus 5.5 | $0.00006 | $0.00217 |
| Sonnet 5 | $0.00003 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00054 |
Grade A, and why
Company Teardown — model, revenue signals, weak points on a template 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 4d 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.
What it actually says
Company Teardown
Does: breaks down a company's model, revenue signals, and weak points on a fixed template. Solves: half a day of scattered research per target. Triggers on: "do a teardown of [company]"
How Maxim does this (one named skill over the CINO analyst)
Surfaces the competitive-intel-analyst as a one-shot, fixed-template teardown: business model · how they make money (revenue signals) · positioning · pricing · moat (and its source) · weak points · the opening for you. Same template every time, so teardowns are comparable across targets.
Behavioral overlay
- Frameworks (cited per ADR-007): Porter's Five Forces · 7 Powers (Helmer — what kind of moat) · Wardley Mapping (where they sit on the value chain) · Business Model Canvas.
- No-fabrication is load-bearing here: public/verifiable facts are stated as facts with the source; anything inferred is explicitly marked inference, not fact. Revenue figures for private companies are flagged as estimates. This is why the default tag is 🟡 MEDIUM — a teardown mixes verified and inferred signal, and Maxim labels which is which (the discipline a generic "research this company" prompt skips).
- Confidence tag (ADR-010) per section, not just overall.
Output
A fixed-template teardown: model · revenue signals · positioning · pricing · moat + source · weak points · your opening — every claim marked fact vs. inference.
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.
- 4d ago First seen · 43 lines · 14 tokens per session scan A a2390ab6a11f
Company Teardown — model, revenue signals, weak points on a template is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 543 once invoked, about $0.0001 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 2026-09-19.
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