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-securitygit 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-security)<a href="https://agentmods.dev/skills/wardawgmalvicious/agent-config/fabric-security"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-security/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-security"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/fabric-security.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 | $0.00183 | $0.02074 |
| Opus 5 | $0.00092 | $0.01037 |
| Sonnet 5 | $0.00037 | $0.00415 |
| Haiku 4.5 | $0.00018 | $0.00207 |
Grade A, and why
fabric-security 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 5d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security model
Permission Layers (broadest to finest)
- Workspace roles: Admin, Member, Contributor, Viewer
- Item-level permissions: Read, ReadData, ReadAll
- OneLake security data access roles (GA May 2026): lake-level RBAC — folder + row + column scopes enforced across all Fabric engines (see below)
- SQL granular permissions: GRANT/DENY/REVOKE
Key Principles
- Admin/Member/Contributor roles grant full data read and bypass RLS/CLS/DDM
- Use Viewer role + SQL GRANT for least-privilege consumer access
- Sharing an item with no extra permissions → CONNECT only (can't read tables until GRANT SELECT)
- "Read all data using SQL" (ReadData) → equivalent to
db_datareader - "Read all data using Apache Spark" (ReadAll) → OneLake file access, does NOT affect SQL permissions
- RLS/CLS enforcement is mode-dependent (changed by OneLake security GA, May 2026):
- SQL-defined RLS/CLS (the
CREATE SECURITY POLICY/ column-GRANT surface below) is still enforced on the SQL endpoint only — users with Spark/OneLake access (ReadAll) bypass it. This hole is unchanged. - OneLake-security-defined RLS/CLS (lake-level data access roles) is enforced across all Fabric engines — Lakehouse, Spark notebooks, Direct Lake on OneLake, and SQL analytics endpoints switched to User's identity access mode are all GA. "Any security set applies to access from all engines in Fabric." SQL analytics endpoints left in delegated-identity mode do not honor OneLake security roles (they keep the old behavior). The mode is a one-time per-endpoint switch under the endpoint Security tab; newer SQL analytics endpoints default to user's-identity mode.
- SQL-defined RLS/CLS (the
- Users auto-created on first GRANT/DENY —
CREATE USERis not supported - Limit warehouses + SQLEPs to ≤ 40 per workspace to avoid system token size limits
SQL Granular Permissions
Users are auto-created on first GRANT/DENY — CREATE USER is not supported (and not needed).
-- Object-level
GRANT SELECT ON dbo.FactSales TO [[email protected]];
DENY SELECT ON dbo.EmployeeSalary TO [[email protected]];
GRANT EXECUTE ON dbo.sp_TopProducts TO [[email protected]];
GRANT SELECT ON SCHEMA::reporting TO [[email protected]];
-- Column-level (CLS) — column list in GRANT or DENY
GRANT SELECT ON dbo.Customers (CustomerID, CustomerName, Region) TO [[email protected]];
DENY SELECT ON dbo.Customers (SSN, CreditCardNumber) TO [[email protected]];
What ships with it
1 file 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.
- 5d ago Changed · +3 lines dadd9bd4cd3b
- 9d ago First seen · 105 lines · 183 tokens per session scan A 9a272d953d19
fabric-security is a skill published in the GitHub repository wardawgmalvicious/agent-config (1 stars, last pushed yesterday), licensed MIT. It adds 183 tokens to every session and 2,074 once invoked, about $0.0009 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 2026-08-31.
Other skills, from other repositories
marketing-strategy-pmm
Product marketing, positioning, GTM strategy, and competitive intelligence. Includes ICP definition, April Dunford positioning methodology, launch playbooks, competitive battlecards, and international market entry guides. Use when developing positioning, planning product launches, creating messaging, analyzing…
loki-mode
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention.…
email-sequence
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle…
qa-test-planner
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.
content-research-writer
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
training-llms-megatron
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA…