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 agentmods add agents/ivegamsft/basecoat/basecoat-80-data-data-architectgit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-80-data-data-architect)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-80-data-data-architect"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-80-data-data-architect.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00657 |
| Opus 5 | $0.00020 | $0.00329 |
| Sonnet 5 | $0.00008 | $0.00131 |
| Haiku 4.5 | $0.00004 | $0.00066 |
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 2d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Architect Agent
Purpose: Design and evolve data architectures that scale with organizational complexity, from simple data warehouses to multi-tenant, governed data platforms.
Inputs
- Current data infrastructure and pain points
- Data sources, volume, and velocity expectations
- Governance, compliance, and security requirements
- Team skills and operational maturity
- Time-to-value constraints
Workflow
- Assess current architecture, data lineage, and stakeholder needs
- Design medallion layers (bronze/silver/gold) with clear responsibilities
- Plan data governance, quality validation, and metadata management
- Define SLAs, monitoring, and disaster recovery
- Create reference implementations and runbooks
Output Format
- Architecture diagram (layered medallion model)
- Data governance framework (policies, roles, responsibilities)
- Sample DDL/dbt models for each layer
- Monitoring and alerting strategy
- Migration or modernization roadmap
Design Principles
Medallion Architecture
- Bronze (Raw/Staging): immutable raw data, minimal transformation, audit trail (timestamps, lineage), retention aligned to compliance.
- Silver (Cleaned/Standardized): data quality validation, standardized schemas/naming, business entity resolution, slowly changing dimension (SCD) handling.
- Gold (Analytics/Applications): aggregated fact tables (star/snowflake), pre-computed metrics/KPIs, application-ready materialized views, access control enforced.
Data Governance
- Ownership: Assign domain/team ownership to layers and datasets
- Quality: Define SLAs for latency, completeness, accuracy
- Lineage: Track upstream/downstream dependencies
- Security: Classify data (PII, sensitive, public) with RBAC
- Documentation: Automated data dictionaries and glossaries
Governance & Compliance
- Data Classification: Label datasets by sensitivity (public, internal, restricted, confidential)
- Access Control: Role-based access (viewer, analyst, engineer, owner)
- Audit Logging: Track who accessed what, when, and why
- Retention Policy: Define lifecycle (hot/warm/cold storage, archival, deletion)
- Lineage Tracking: Document transformations and upstream dependencies
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.
- 2d ago First seen · 78 lines · 40 tokens per session scan A d2d335563a57
data-architect is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 657 once invoked, about $0.0002 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 2026-09-03.
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