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 agentmods add skills/botlearn-ai/botlearn-skills/botlearn-mental-modelsnpx skills add botlearn-ai/botlearn-skills --skill botlearn-mental-modelsgit clone --depth 1 https://github.com/botlearn-ai/botlearn-skillsWrote 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/botlearn-ai/botlearn-skills/botlearn-mental-models)<a href="https://agentmods.dev/skills/botlearn-ai/botlearn-skills/botlearn-mental-models"><img src="https://agentmods.dev/badge/skills/botlearn-ai/botlearn-skills/botlearn-mental-models.svg" alt="Measured on agentmods" 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 | $0.00159 | $0.03486 |
| Opus 5 | $0.00079 | $0.01743 |
| Sonnet 5 | $0.00032 | $0.00697 |
| Haiku 4.5 | $0.00016 | $0.00349 |
Grade A, and why
botlearn-mental-models 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 6d 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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mental Models — Latticework Thinking Advisor
This skill succeeds when the user sees the problem differently after reading the output. Not when the analysis is thorough. When the framing shifts. That happens when two unrelated disciplines independently point to the same conclusion — convergence from separate bodies of knowledge is hard to explain away. That independence is what gives it weight.
What Good Looks Like
Read this first. Every rule below explains why this example works.
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LATTICEWORK invest in AI infrastructure company?
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Confidence MEDIUM — logic holds, timeline unknown
Wait How much do we lose if commoditization hits in 3 years?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHY You're pricing a commoditization timeline, not a company. No one knows that number — including them.
◆ PATTERN Every infrastructure layer eventually commoditized. High margins are a timing advantage, not a moat.
· Evolutionary Thinking × Scale & Power Laws
◆ INCENTIVE Their largest customers have the most incentive to build this themselves. Best clients are the most dangerous ones.
· Game Theory × Institutions Matter
◆ TENSION 3 years: expensive. 7 years: cheap. The lattice can't tell you which — that's the actual decision.
· Probabilistic Thinking
◆ RISK Two similar bets already in portfolio. A third is concentration risk, not conviction.
· Margin of Safety
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
◆ each supporting line — always labeled. Confidence in words: "3 lenses converge, one unresolved tension" not just "Medium".
The 24 Lenses — Index
4 Munger Meta-Lenses — run these on every judgment call:
| # | Lens | Lights up when... |
|---|---|---|
| M1 | Inversion | Always — flip every goal, ask what guarantees failure |
| M2 | Circle of Competence | User reasoning confidently outside their knowledge base |
| M3 | Margin of Safety | Any plan requiring things to go right |
| M4 | Lollapalooza Effect | 3+ lenses converging — name the non-linear amplification |
What ships with it
23 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.
- assets/user-profile-template.md 1.3 KB
- manifest.json 1.9 KB
- models/01-first-principles.md 2.0 KB
- models/02-evolutionary-thinking.md 1.7 KB
- models/03-systems-thinking.md 2.0 KB
- models/04-probabilistic-thinking.md 2.0 KB
- models/05-antifragile.md 2.0 KB
- models/06-paradigm-shift.md 2.1 KB
- models/07-scale-power-laws.md 2.0 KB
- models/08-entropy-information.md 2.1 KB
- models/09-game-theory.md 2.1 KB
- models/10-network-effects.md 2.1 KB
- models/11-scarcity-bandwidth.md 2.2 KB
- models/12-reframing-causation.md 2.2 KB
- models/13-institutions-matter.md 2.2 KB
- models/14-power-discourse.md 2.1 KB
- models/15-self-reference.md 2.1 KB
- models/16-narrative-reality.md 2.2 KB
- models/17-medium-shapes-message.md 2.2 KB
- models/18-meaning-under-pressure.md 2.0 KB
- models/19-scientific-skepticism.md 2.2 KB
- models/20-nonlinear-wuwei.md 2.3 KB
- package.json 1001 B
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.
- 6d ago First seen · 307 lines · 0 tokens per session scan A 30e8d6375a7d
botlearn-mental-models is a skill published in the GitHub repository botlearn-ai/botlearn-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 159 tokens to every session and 3,486 once invoked, about $0.0008 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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