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 Giskard-AI/giskard-skills --skill hub-agent-setupgit clone --depth 1 https://github.com/Giskard-AI/giskard-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/giskard-ai/giskard-skills/hub-agent-setup)<a href="https://agentmods.dev/skills/giskard-ai/giskard-skills/hub-agent-setup"><img src="https://agentmods.dev/badge/skills/giskard-ai/giskard-skills/hub-agent-setup/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/giskard-ai/giskard-skills/hub-agent-setup"><img src="https://agentmods.dev/badge/skills/giskard-ai/giskard-skills/hub-agent-setup.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 | $0.00071 | $0.01788 |
| Opus 5.5 | $0.00028 | $0.00715 |
| Sonnet 5.5 | $0.00014 | $0.00358 |
| Haiku 4.5 | $0.00007 | $0.00179 |
Grade A, and why
hub-agent-setup scanned grade A with 1 finding 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 today.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Expose only the wrapper service. Append its invocation path to the **actual HTTPS URL** reported by the tunnel. Keep both processes running in persistent sessions and proceed directly to registration; a successful public How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hub Agent Setup
Get to the first Hub connection attempt quickly: build the smallest adapter, expose it over HTTPS, register it, and use Hub's connection test to guide fixes. Use the target's stack and the coding agent's available tools. Supporting paths are relative to this skill directory.
Do not create a test suite, run broad audits or extra review passes, refactor unrelated code, or write extensive documentation before the first Hub attempt. Honor required project checks without turning this connection task into a general hardening exercise.
1. Gather the essentials
Identify the target agent first. A project can contain several agents, for example separate entry points, services, graphs, or chains. If the user did not name the target and more than one candidate exists, list the candidates briefly with their locations and ask which one to register. Never pick one yourself. Continue without asking only when the user named the target or the project contains a single candidate.
Locate the target's callable/API, input/output shape, streaming behavior, conversation state, and whether it is already deployed. Infer its name, description, and supported languages. Ask only for missing connection details:
- Hub URL.
- Hub API key: link to Finding your API key; accept secure input or an already configured environment variable.
- Hub project name or ID.
Use the giskard-hub Python SDK when Python 3.10+ is available. Without Python, connect to the Giskard Hub MCP server and use its tools for the same steps. See hub-sdk.md.
As soon as credentials are available, instantiate HubClient and resolve the project with hub.projects.list() or hub.projects.retrieve(project_id) (MCP: list_projects or retrieve_project). This checks Hub access early. Ask about ambiguous matches; do not choose the first project implicitly.
Use GISKARD_HUB_BASE_URL and GISKARD_HUB_API_KEY for the SDK. Generate a separate cryptographically random AGENT_WRAPPER_API_KEY for the wrapper. Keep secrets in the environment, an explicitly loaded and git-ignored .env, or a secret store; never in code, URLs, or logs. Never send the Hub key to the wrapper.
What ships with it
2 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.
- today Changed · +8 lines · +4 tokens per session 47c20ac15986
- 25d ago First seen · 74 lines · 67 tokens per session scan A a5ccacf7333e
hub-agent-setup is a skill published in the GitHub repository Giskard-AI/giskard-skills (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 1,788 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-22.
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