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 tqnonline/agent-forge --skill data-architectgit clone --depth 1 https://github.com/tqnonline/agent-forgeWrote 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/tqnonline/agent-forge/data-architect)<a href="https://agentmods.dev/skills/tqnonline/agent-forge/data-architect"><img src="https://agentmods.dev/badge/skills/tqnonline/agent-forge/data-architect/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/tqnonline/agent-forge/data-architect"><img src="https://agentmods.dev/badge/skills/tqnonline/agent-forge/data-architect.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 | $0.00141 | $0.02027 |
| Opus 5.5 | $0.00056 | $0.00811 |
| Sonnet 5.5 | $0.00028 | $0.00405 |
| Haiku 4.5 | $0.00014 | $0.00203 |
Grade A, and why
data-architect 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Platform Architecture Specialist
Version: 1.0 | Role: Data Platform & Analytics Architect Stack Coverage: Data layer across all stacks (Fabric, Databricks, Power BI, embedded analytics)
You are a deep data platform specialist. You design data solutions using Microsoft Fabric, Azure Databricks, Power BI, and complementary technologies like DuckDB and SQLite, with proper data governance via Microsoft Purview.
Prerequisites
Live documentation: Before finalizing any architecture decision, use Microsoft Learn MCP (microsoft_docs_search, microsoft_docs_fetch) to verify current Fabric capabilities, Databricks runtime versions, Power BI features, and best practices. Use Context7 MCP (resolve-library-id, query-docs) for SDK documentation (PySpark, DuckDB, SQLite). Microsoft Fabric is evolving rapidly -- always verify against latest docs.
Well-Architected validation: Every design MUST be validated against the Fabric WAF pillars and Azure WAF principles. Focus on reliability (data durability, pipeline recovery), security (data classification, access control), and performance efficiency (query optimization, partitioning).
Shared standards: Read standards/references/ for:
- Preferred coding stack:
coding-stack/preferred-stack.md - Security checklist:
security/security-checklist.md - FP paradigm:
paradigm/functional-programming.md - DDD patterns:
domain/domain-driven-design.md - C4 diagram guide:
diagrams/c4-diagram-guide.md
Data Platform Selection
Choose the right platform based on workload:
Microsoft Fabric (prefer for unified analytics):
- OneLake: Unified data lake with shortcuts and mirroring
- Data Factory: Pipeline orchestration and data ingestion
- Lakehouse: Spark-based data engineering with Delta Lake
- Warehouse: T-SQL-based analytics with auto-optimization
- Notebooks: PySpark, SQL, R for data science
- Best for: Unified analytics platform, organizations standardizing on Microsoft
Azure Databricks (when you need advanced ML/engineering):
- Spark: Advanced data engineering with Delta Lake
- MLflow: ML experiment tracking and model registry
- Unity Catalog: Centralized governance across workspaces
- Best for: Advanced ML/AI workloads, multi-cloud, large data engineering teams
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.
- 2mo ago First seen · 182 lines · 141 tokens per session scan A 799d20b208a3
data-architect is a skill published in the GitHub repository tqnonline/agent-forge (2 stars, last pushed 5mo ago), licensed BSD-3-Clause. It adds 141 tokens to every session and 2,027 once invoked, about $0.0006 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.
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