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 fabric-testbed/claude-plugin-marketplace --skill fabric-resource-discoverygit clone --depth 1 https://github.com/fabric-testbed/claude-plugin-marketplaceWrote 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/fabric-testbed/claude-plugin-marketplace/fabric-resource-discovery)<a href="https://agentmods.dev/skills/fabric-testbed/claude-plugin-marketplace/fabric-resource-discovery"><img src="https://agentmods.dev/badge/skills/fabric-testbed/claude-plugin-marketplace/fabric-resource-discovery/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/fabric-testbed/claude-plugin-marketplace/fabric-resource-discovery"><img src="https://agentmods.dev/badge/skills/fabric-testbed/claude-plugin-marketplace/fabric-resource-discovery.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 | $0.00022 | $0.01786 |
| Opus 5.5 | $0.00009 | $0.00714 |
| Sonnet 5.5 | $0.00004 | $0.00357 |
| Haiku 4.5 | $0.00002 | $0.00179 |
Grade A, and why
fabric-resource-discovery 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 2mo 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
When the user asks about FABRIC sites, resources, available hardware, images, or capacity, generate code to discover and query testbed resources. This skill is also auto-invoked when context suggests the user needs resource information.
Always assume FABlib is already initialized as fablib. If unsure, prepend initialization code.
API Reference
Site Discovery
# List all sites with details
fablib.list_sites(
output=None, # "text", "pandas", "json", "list" (default: pandas in notebook)
fields=None, # List of field names to display
quiet=False, # Suppress printed output
filter_function=None, # Lambda to filter sites
pretty_names=True, # Use human-readable field names
start=None, # datetime: future availability window start
end=None, # datetime: future availability window end
)
# List hosts at sites
fablib.list_hosts(
output=None,
fields=None,
quiet=False,
filter_function=None,
)
# Get a random site (useful for testing)
fablib.get_random_site(
avoid=None, # List of site names to avoid
filter_function=None, # Lambda to filter eligible sites
update=True, # Refresh resource data
) -> str
# Get multiple random sites
fablib.get_random_sites(
count=1,
avoid=None,
filter_function=None,
update=True,
) -> list[str]
Image Discovery
# Get available OS images
fablib.get_image_names() -> dict[str, dict]
# Returns: {"image_name": {"default_user": "...", "description": "..."}, ...}
Common Images
default_ubuntu_24— Ubuntu 24.04 (default if not specified)default_ubuntu_22— Ubuntu 22.04default_ubuntu_20— Ubuntu 20.04default_centos_9_stream— CentOS 9 Streamdefault_centos_8— CentOS 8default_rocky_9— Rocky Linux 9default_rocky_8— Rocky Linux 8default_debian_12— Debian 12default_fedora_41— Fedora 41
Component Model Constants
Use these string values with node.add_component(model=...):
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.
- 2mo ago First seen · 259 lines · 22 tokens per session scan A c7d7d86dd6f2
fabric-resource-discovery is a skill published in the GitHub repository fabric-testbed/claude-plugin-marketplace (2 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 1,786 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
fabric-resource-discovery compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| fabric-resource-discoveryfabric-testbed/claude-plugin-marketplace | Skill | 2 | 8mo ago | Apache-2.0 | A | 22 | Claude Code |
| neuroskill-bciNousResearch/hermes-agent | Skill | 261k | yesterday | MIT | A | 18 | Claude Code, Codex |
| ruview-applicationsruvnet/RuView | Skill | 103k | yesterday | MIT | A | 79 | Claude Code |
| lab-hardware-cadK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 108 | Claude Code |
| pylabrobotK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 49 | Claude Code |
| pybammK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 70 | Claude Code, Codex |
| calibrate-roomruvnet/RuView | Skill | 103k | yesterday | MIT | A | 41 | Claude Code |