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 wardawgmalvicious/agent-config --skill fabric-iqgit clone --depth 1 https://github.com/wardawgmalvicious/agent-configWrote 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/wardawgmalvicious/agent-config/fabric-iq)<a href="https://agentmods.dev/skills/wardawgmalvicious/agent-config/fabric-iq"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-iq/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/wardawgmalvicious/agent-config/fabric-iq"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-iq.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 | $0.00088 | $0.01160 |
| Opus 5.5 | $0.00035 | $0.00464 |
| Sonnet 5.5 | $0.00018 | $0.00232 |
| Haiku 4.5 | $0.00009 | $0.00116 |
Grade A, and why
fabric-iq 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 10d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fabric IQ items
Learn's IQ workload holds six items ("What is Fabric IQ?", read
2026-10-12). Four have a reference here, in the table below; Power BI
semantic models are fabric-tmdl's, and Plan has none. Each reference
holds the depth, and this file picks the item and carries only what
crosses items. Take the steps in order: the wrong item sends every later
step to the wrong file.
1. Pick the item
By the job asked for, or by the folder of the file in hand:
| The job | Folder | Read |
|---|---|---|
| A shared business vocabulary bound to data: entity types, properties, relationships | <name>.Ontology/ |
ontology.md |
| GQL over a labeled property graph built from OneLake tables | <name>.GraphModel/ |
graph.md |
| Natural-language Q&A over a domain's data, read-only, asked by a user | <name>.DataAgent/ |
data-agent.md |
| Watching live data and acting on it, unattended | <name>.OperationsAgent/ |
operations-agent.md |
operations-agent.md § 7 sets the two agents side by side. Near these items, and not them:
- KQL's graph operators in an Eventhouse (
make-graph,graph-match):fabric-eventhouse, a different engine from the GraphModel. - Rule-based alerting with no LLM:
fabric-activator. - The semantic model an ontology is generated from, or a data agent
queries:
fabric-tmdl.
2. What crosses items
- An ontology grounds both agents. A data agent takes one as a source, with known issues of its own (data-agent.md § What a Data Agent is). An operations agent takes one as its single source, in the agent's own workspace (operations-agent-reference.md § Data source constraints). Model the ontology in ontology.md, and configure each agent in its own file.
- An ontology can generate a graph: a child
GraphModel, made only on opt-in through Manage graph, and only from eligible entity types (ontology.md, its opening and § The constraints that produce silent or confusing failures). GQL itself is graph.md's. - Two MCP endpoints, two shapes. An ontology is an MCP server (ontology.md § Consuming an ontology), and so is a published data agent (data-agent.md § Consumption surfaces). The URLs differ in shape, so neither is built from the other.
- Each item has its own tenant settings; check them first when an item won't create. The ontology's are in ontology.md § Before you start, and, with both agents', in ontology-reference.md § 7. The data agent's are on the Learn page its § Reference links, and the operations agent's in operations-agent-reference.md § Runtime, identity and availability. graph.md names none.
What ships with it
18 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.
- references/data-agent/assets/example-retail-agent.md 3.6 KB
- references/data-agent/data-agent.md 16 KB
- references/data-agent/references/authentication.md 3.1 KB
- references/data-agent/references/authoring-workflow.md 8.3 KB
- references/data-agent/references/configuration-layers.md 10 KB
- references/data-agent/references/consumption-surfaces.md 8.1 KB
- references/data-agent/references/creator-agent.md 2.1 KB
- references/data-agent/references/evaluation.md 5.8 KB
- references/data-agent/references/REFERENCE.md 4.2 KB
- references/data-agent/references/status-and-retirements.md 2.8 KB
- references/graph-reference.md 21 KB
- references/graph.md 14 KB
- references/ontology-reference.md 26 KB
- references/ontology.md 18 KB
- references/operations-agent-reference.md 9.7 KB
- references/operations-agent.md 15 KB
- scripts/data-agent-eval.py 36 KB runs code
- scripts/data-agent-settings.py 12 KB runs code
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.
- 10d ago Changed · +7 lines a9b2d6d140f4
- 14d ago First seen · 85 lines · 88 tokens per session scan A 08771399d639
fabric-iq is a skill published in the GitHub repository wardawgmalvicious/agent-config (1 stars, last pushed 9d ago), licensed MIT. It adds 88 tokens to every session and 1,160 once invoked, about $0.0004 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 2026-10-13.
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