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 Cotal-AI/Cotal --skill team-topologygit clone --depth 1 https://github.com/Cotal-AI/CotalWrote 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/cotal-ai/cotal/team-topology)<a href="https://agentmods.dev/skills/cotal-ai/cotal/team-topology"><img src="https://agentmods.dev/badge/skills/cotal-ai/cotal/team-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/cotal-ai/cotal/team-topology"><img src="https://agentmods.dev/badge/skills/cotal-ai/cotal/team-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 | $0.00168 | $0.02576 |
| Opus 5 | $0.00084 | $0.01288 |
| Sonnet 5 | $0.00034 | $0.00515 |
| Haiku 4.5 | $0.00017 | $0.00258 |
Grade A, and why
team-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 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team topology
A method for defining a multi-agent team for a task as an explicit topology: not "spawn some agents" but a legible deployment you can draw, hand off, and reason about. Who runs where, who talks to whom, what each node can touch, which model sits in which seat. Substrate-agnostic: the same method applies to a Cotal mesh, harness subagents, workflow stages, or plain processes.
When to use
- The user asks to define / design / lay out a team or formation, or asks "what topology?".
- You are about to stand up more than one or two agents and want them arranged deliberately.
- You need to communicate a running deployment in a scannable form.
Step 1: pick the shape from the dominant risk
A topology is a defense against the way the task fails by default. Name the task's dominant risk, then pick the shape that structurally prevents it:
| Task type | Dominant risk | Shape |
|---|---|---|
| audit / review / research | missed findings, groupthink | hub-and-spoke fan-out: independent finder lanes, an adversarial verify tier, coordinator synthesizes |
| fix / implementation | write races, unverified changes | writer lanes + merge authority: 1-2 writers in isolated workspaces, everyone else fenced read-only, a proof gate before merge |
| staged transform (migration, ETL, generation) | loss at handoffs | pipeline: stages connected by explicit artifacts, each stage validates its input |
| open design question | anchoring on the first idea | panel + judge: N proposals produced blind, then scored and synthesized |
| long-running ops / monitoring | drift, silent death | operator + watchdog: one active node, one that only checks liveness and invariants |
This catalog is a starting set, not a menu. Hybrids are normal (an audit's repro tier is a small pipeline), and inventing a shape for the task at hand is expected. The number of channels, tiers, and agents is a free parameter: derive it from the task's size, risk, and budget (a quick check might be 1 channel / 2 agents; a deep audit 4 channels / 10). Never copy a previous deployment's headcount out of habit.
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 · 97 lines · 168 tokens per session scan A 43125706c1ad
team-topology is a skill published in the GitHub repository Cotal-AI/Cotal (274 stars, last pushed today), licensed Apache-2.0. It adds 168 tokens to every session and 2,576 once invoked, about $0.0008 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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