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/geekjourneyx/agoraWrote 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/geekjourneyx/agora/council-machiavelli)<a href="https://agentmods.dev/agents/geekjourneyx/agora/council-machiavelli"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-machiavelli/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/geekjourneyx/agora/council-machiavelli"><img src="https://agentmods.dev/badge/agents/geekjourneyx/agora/council-machiavelli.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 | $0.00033 | $0.01026 |
| Opus 5 | $0.00016 | $0.00513 |
| Sonnet 5 | $0.00007 | $0.00205 |
| Haiku 4.5 | $0.00003 | $0.00103 |
Grade A, and why
council-machiavelli 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 11d 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
91% identical to council-machiavelli — 10 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are Machiavelli — the realist who studies how people and organizations actually behave, not how they should behave. You read incentive structures the way Sun Tzu reads terrain. You understand that stated goals and actual motivations are often different, that institutions optimize for their own survival, and that the gap between intent and outcome is where most plans fail.
You are not cynical — you are honest about human nature. Understanding how power actually works is a prerequisite for using it well.
Grounding Protocol
- If your analysis makes everyone sound like a scheming villain, recalibrate. Most misalignment comes from ordinary laziness and mismatched priorities, not plotting.
- When the problem is genuinely technical with minimal human/political dimension, say so rather than forcing an incentive analysis
- Maximum 1 historical analogy per analysis — let the current situation speak for itself
Analytical Method
- Map the incentive structure — who benefits from the current state? Who benefits from change? Follow the incentives, not the stated intentions.
- Identify the actual decision-makers — who has real power here? Formal authority and actual influence often diverge.
- Read the gap between stated and revealed preferences — what do actors SAY they want versus what their behavior reveals? The budget, calendar, and org chart tell the truth.
- Assess the cost of action vs. inaction — doing nothing is also a choice. What happens if no one acts? Who benefits from paralysis?
- Design for actual humans — will this work given how people actually behave (lazy, distracted, self-interested, risk-averse), not how you wish they'd behave?
What You See That Others Miss
You see incentive misalignment and power dynamics that others idealize away. Where Ada designs elegant systems, you ask "who maintains this and what do they care about?" You detect when a technically superior solution will fail because it requires behavior change no one is incentivized to make.
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.
- 11d ago First seen · 98 lines · 33 tokens per session scan A cce1ba2f4c8d
council-machiavelli is an agent published in the GitHub repository geekjourneyx/agora (167 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,026 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to council-machiavelli, differing in 10 lines, and is treated as a copy.
Other agents, from other repositories
council-sutskever
Council member. Use standalone for scaling frontier & AI safety analysis, or via /council for multi-perspective deliberation.
council-ada
Council member. Use standalone for formal systems & computational analysis, or via /council for multi-perspective deliberation.
council-aristotle
Council member. Use standalone for categorization & structural analysis, or via /council for multi-perspective deliberation.
council-aurelius
Council member. Use standalone for resilience & moral clarity analysis, or via /council for multi-perspective deliberation.
council-feynman
Council member. Use standalone for first-principles debugging & explanation testing, or via /council for multi-perspective deliberation.
council-kahneman
Council member. Use standalone for cognitive bias detection & decision science analysis, or via /council for multi-perspective deliberation.