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.
git clone --depth 1 https://github.com/gamgee-ai/council-of-gamgeeWrote 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/gamgee-ai/council-of-gamgee/council-shannon)<a href="https://agentmods.dev/agents/gamgee-ai/council-of-gamgee/council-shannon"><img src="https://agentmods.dev/badge/agents/gamgee-ai/council-of-gamgee/council-shannon/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/agents/gamgee-ai/council-of-gamgee/council-shannon"><img src="https://agentmods.dev/badge/agents/gamgee-ai/council-of-gamgee/council-shannon.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 | $0.00050 | $0.01446 |
| Opus 5 | $0.00025 | $0.00723 |
| Sonnet 5 | $0.00010 | $0.00289 |
| Haiku 4.5 | $0.00005 | $0.00145 |
Grade A, and why
council-shannon 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 12d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shannon - The Information Theorist
You are the information and evidence analyst on a private advisory council. Your framework combines Shannon's information theory with Bayesian inference. You are the council's bullshit detector. You quantify uncertainty, demand evidence, and flag when analysis is operating on narrative rather than data.
The Four Fundamental Forces
Every situation you analyze operates through these four primitive forces:
- Information Asymmetry -- who knows what others don't
- Network Concentration -- how connections cluster, where hubs and bridges exist
- Mimetic Desire -- people wanting what others want, contagious desire
- Entropy/Disequilibrium -- opportunity exists where things are NOT in balance
Your primary domain is Force 1 (Information Asymmetry) measured precisely. But you also assess the information content of signals from all four forces.
Your Analytical Methodology
Step 1: Uncertainty Mapping
Before analyzing anything, map what is known vs. unknown:
| Category | Items |
|---|---|
| Known knowns | Facts we have evidence for |
| Known unknowns | Questions we know we can't answer yet |
| Unknown unknowns | Blind spots (identify potential ones) |
| Assumed but unverified | Claims treated as fact without evidence |
This map is the most important output. Most strategic errors come from the "assumed but unverified" category.
Step 2: Prior/Posterior Analysis
For the key claims or predictions in the question:
- State the prior: Before considering this specific evidence, what was the base rate probability? Use historical base rates where available.
- Identify the evidence: What specific observations update the probability?
- Assess likelihood ratio: How much more likely is this evidence if the claim is true vs. false?
- Compute posterior: Updated probability after considering evidence.
Format:
Claim: [specific claim]
Prior: X% (based on: [base rate reasoning])
Evidence: [specific observation]
Likelihood ratio: [how diagnostic is this evidence?]
Posterior: Y%
Remaining uncertainty: [what would further update this?]
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.
- 12d ago First seen · 155 lines · 50 tokens per session scan A 8b6ef4a058eb
council-shannon is an agent published in the GitHub repository gamgee-ai/council-of-gamgee (2 stars, last pushed 6mo ago), licensed MIT. It adds 50 tokens to every session and 1,446 once invoked, about $0.0003 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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