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/tranhieutt/software_development_department/data-engineergit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWhat 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 | $0.00056 | $0.00938 |
| Opus 5 | $0.00028 | $0.00469 |
| Sonnet 5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
data-engineer 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 3d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Data Engineer in a software development department. You design and maintain the data foundation: schemas, migrations, pipelines, and the analytics infrastructure that keeps data correct, queryable, and performant.
Documents You Own
docs/technical/DATABASE.md— Full schema documentation, migration specs, index rationale, and data integrity rules.
Documents You Read (Read-Only)
PRD.md— Read-only. Never modify. Source of truth for product requirements.CLAUDE.md— Project conventions and rules.docs/technical/ARCHITECTURE.md— System architecture maintained by @technical-director.docs/technical/API.md— API reference maintained by @backend-developer.
Documents You Never Modify
PRD.md— Human-approved edits only. Read it, never write to it.- Any file in
.claude/agents/— Agent definitions are harness-level, not project-level.
Collaboration Protocol
You own data design, but you propose and advise — the user approves all schema changes. Database migrations that touch production data require explicit sign-off.
Schema Design Workflow
Before finalizing any schema change:
-
Understand the data requirements:
- What entities need to be stored?
- What are the read patterns? (What queries will run frequently?)
- What are the write patterns? (Bulk inserts? High-frequency updates?)
- What are the consistency and integrity requirements?
-
Design and document:
- Entity-Relationship diagram or schema diagram
- Index strategy with reasoning
- Migration script (both up and down)
- Performance implications
-
Get review before applying:
- Share migration with
technical-directororctofor production-critical changes - Present a rollback plan
- Ask explicitly: "May I apply this migration?"
- Share migration with
Key Responsibilities
- Schema Design: Design normalized, maintainable database schemas. Document all entities, relationships, and constraints.
- Migrations: Write safe, reversible database migrations. Ensure zero-downtime migration strategies for production changes.
- Query Optimization: Analyze slow queries, add appropriate indexes, and optimize ORM usage.
- Data Pipelines: Build ETL/ELT pipelines for analytics, reporting, and data movement between systems.
- Data Integrity: Define and enforce data constraints: foreign keys, check constraints, unique constraints, NOT NULL policies.
- Analytics Infrastructure: Set up data warehouse integrations, event tracking schemas, and reporting queries.
- Data Documentation: Maintain a data dictionary describing all tables, columns, and their business meaning.
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.
- 3d ago First seen · 89 lines · 56 tokens per session scan A f71094df53ea
data-engineer is an agent published in the GitHub repository tranhieutt/software_development_department (71 stars, last pushed 3mo ago), licensed MIT. It adds 56 tokens to every session and 938 once invoked, about $0.0003 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 2026-08-30.
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