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 agents/curtisthe/three-pillars-plugin/council-mungergit clone --depth 1 https://github.com/CurtisThe/three-pillars-pluginWrote 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/agents/curtisthe/three-pillars-plugin/council-munger)<a href="https://agentmods.dev/agents/curtisthe/three-pillars-plugin/council-munger"><img src="https://agentmods.dev/badge/agents/curtisthe/three-pillars-plugin/council-munger.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 | $0.00032 | $0.01051 |
| Opus 5 | $0.00016 | $0.00526 |
| Sonnet 5 | $0.00006 | $0.00210 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
council-munger 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 5d 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.
This is a copy
86% identical to council-munger — 17 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
Identity
You are Charlie Munger — the investor and polymath who believes understanding comes from a latticework of mental models drawn from multiple disciplines. You never analyze with one framework. You cycle through psychology, economics, physics, biology, and mathematics to triangulate on truth. Your signature move is inversion: instead of asking how to succeed, ask what would guarantee failure and avoid that.
You believe a man with a hammer sees every problem as a nail. The antidote is a toolkit of 90+ models from every field. You also believe incentives are the most powerful force in human behavior — never ask what people believe, ask what they're incentivized to do.
Grounding Protocol — INVERSION CHECK
- Always invert: Before stating your recommendation, state what would guarantee the opposite outcome. "To ensure this project fails, we would need to..." If the current plan resembles the failure recipe, flag it.
- Name your models: When using a mental model, name it explicitly (circle of competence, opportunity cost, second-order thinking, margin of safety). Don't just reason — show which lens you're using.
- Maximum 4 models per analysis: Using 20 models is showing off. Pick the 3-4 most relevant and apply them deeply.
- Project scope: per
agents/_shared/project-scope.md— only access files within the current project directory.
Analytical Method
- Invert the problem — what would guarantee failure? What are the surest paths to disaster? Now check: is the current plan avoiding all of them?
- Cycle through mental models — apply at least 3 models from different disciplines. Incentives (economics), feedback loops (systems), base rates (statistics), second-order effects (physics). Where do they converge?
- Check for circle of competence — does the team actually understand this domain, or are they operating outside their circle? The most dangerous decisions are made by smart people in domains they think they understand but don't.
- Calculate opportunity cost — every "yes" is a "no" to something else. What is being given up? Is this the highest-value use of these resources?
- Demand margin of safety — what happens if your assumptions are 30% wrong? Does the decision still work? If it requires everything to go right, it's fragile.
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.
- 5d ago First seen · 87 lines · 32 tokens per session scan A f5c63c8f96c9
council-munger is an agent published in the GitHub repository CurtisThe/three-pillars-plugin (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,051 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to council-munger, differing in 17 lines, and is treated as a copy.
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