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 j4flmao/agent-skills --skill multi-agent-topologygit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/multi-agent-topology)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/multi-agent-topology"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/multi-agent-topology/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/j4flmao/agent-skills/multi-agent-topology"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/multi-agent-topology.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 | $0.00018 | $0.00782 |
| Opus 5 | $0.00009 | $0.00391 |
| Sonnet 5 | $0.00004 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
multi-agent-topology 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 13d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Topology: The Societal Structures of AI
At the ontological core of multi-agent systems lies the concept of Topology: the formalized graph of interaction, authority, and information flow between autonomous nodes (agents). A single agent possesses limited cognitive aperture; a topology binds multiple cognitive units into a singular, macro-intelligent organism. To master multi-agent design is to architect the sociology of synthetic minds.
These structures transcend frameworks. They are the mathematical and sociological primitives of distributed intelligence.
I. The Primitives of Topology
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Supervisor-Worker (Hierarchical) The classical delegation paradigm. A central orchestrator (Supervisor) decompose tasks, dispatches sub-routines to specialized nodes (Workers), and synthesizes the outputs. This structure minimizes cognitive overload on individual nodes but risks centralizing points of failure. Axiom of Delegation: The Supervisor must not compute the task; it computes the routing and aggregation of the task.
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Sequential Pipelines (Linear Autonomy) A deterministic chain of cognitive processing. Agent $A_n$ transforms state $S_n$ into $S_{n+1}$, which serves as the immutable input for Agent $A_{n+1}$. This topology enforces extreme strictness and narrow-focus optimization. Axiom of Linearity: Information flows unilaterally. Entropy decreases at each node as raw data is refined into structured conclusions.
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Debate & Reflection Swarms (Polyphonic Convergence) The dialectical approach to truth-seeking. Multiple nodes are instantiated with adversarial or orthogonal personas, iterating on a shared context until a consensus metric is achieved or a maximum reflection depth is reached. Axiom of Divergence: Epistemic certainty is achieved not by a single genius node, but through the cross-examination of multiple probabilistic priors.
II. Topological State Flow
1. Hierarchical Topology
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
S((Supervisor)) -->|Decomposes| W1(Worker: Analyze)
S -->|Decomposes| W2(Worker: Synthesize)
W1 -.->|State Refinement| S
W2 -.->|State Refinement| S
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.
- 13d ago First seen · 59 lines · 18 tokens per session scan A deda9caa3ff2
multi-agent-topology is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 6d ago), licensed MIT. It adds 18 tokens to every session and 782 once invoked, about $0.0001 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 2026-08-30.
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