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 vasilyu1983/AI-Agents-public --skill foundations-team-theorygit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-team-theory)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-team-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-team-theory/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/vasilyu1983/ai-agents-public/foundations-team-theory"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-team-theory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.06399 |
| Opus 5 | $0.00019 | $0.03200 |
| Sonnet 5 | $0.00008 | $0.01280 |
| Haiku 4.5 | $0.00004 | $0.00640 |
Grade A, and why
foundations-team-theory 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 9d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team Theory Foundations
10 canonical team-theory primitives for cooperative multi-agent decision problems — agents share a payoff but each sees a different slice of the world. Game theory handles strategic conflict; decision theory handles solo choice under uncertainty; team theory handles the regime in between, which is exactly where subagents and orchestrated AI agents operate.
The field was founded by Jacob Marschak (1955) and formalized by Roy Radner (1962). It is the formal basis for: when to centralize a decision, what each agent must observe, when communication pays for itself, and why decentralized teams can be optimal even with free communication channels. Modern multi-agent reinforcement learning (Dec-POMDPs, MARL) is its computational descendant.
When to Apply
Apply team-theory when:
- Multiple agents with shared payoff but partitioned observations (the canonical subagent setting)
- Designing what each subagent sees vs. what is centralized
- Choosing between centralized orchestrator, decentralized swarm, and hierarchical structures
- Costing communication: is it worth the latency / token / coordination overhead?
- Building agent teams where role specialization matters (each agent owns a different observation lane)
- Multi-agent RL or Dec-POMDP problem framing
Skip and use simpler alternatives when:
- Agents have divergent incentives → use foundations-game-theory (mechanism design, auctions, debates)
- Single agent / single-shot decision under uncertainty → use foundations-decision-theory
- Communication itself is the bottleneck on a known structure → use foundations-information-theory (channel capacity)
- The problem is recursive control of a viable system, not a one-shot team decision → use foundations-cybernetics-vsm
- All agents see the same information — there is no team problem; centralize trivially
What ships with it
8 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.
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.
- 9d ago First seen · 255 lines · 38 tokens per session scan A ea6c56035592
foundations-team-theory is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 38 tokens to every session and 6,399 once invoked, about $0.0002 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 2026-09-03.
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