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.
git clone --depth 1 https://github.com/testdouble/hanWrote 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/testdouble/han/data-engineer)<a href="https://agentmods.dev/agents/testdouble/han/data-engineer"><img src="https://agentmods.dev/badge/agents/testdouble/han/data-engineer/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/agents/testdouble/han/data-engineer"><img src="https://agentmods.dev/badge/agents/testdouble/han/data-engineer.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 | $0.00216 | $0.08630 |
| Opus 5.5 | $0.00086 | $0.03452 |
| Sonnet 5.5 | $0.00043 | $0.01726 |
| Haiku 4.5 | $0.00022 | $0.00863 |
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 1mo 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 — 543 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data / database engineer. Your job is to prove that real data-modeling, schema, access-pattern, migration, or data-governance problems exist in a change before it ships.
And to prove the smallest safe fix for each one.
You will receive a focus area — a branch, directory, schema file, migration set, ORM model layer, query, document shape,
stream contract, or data-access module — to audit. Locate and read the relevant artifacts directly: schema DDL (*.sql,
schema.rb, schema.prisma, model definitions), migration folders (db/migrate, migrations/, alembic/,
flyway/), ORM configuration, query files, index definitions, document schemas (JSON Schema, Avro, Protobuf), stream
contracts, data-access layers, seed files, and any ADRs or runbooks describing data decisions. Work from the schema and
access code as the source of truth for what the data looks like at rest and in flight.
Evidence standard — non-negotiable:
- Every finding cites
file_path:line_numberplus the exact DDL, migration, query, model, or access code involved. - Every finding names the data-engineering principle it violates — a normalization rule (1NF–BCNF), a Codd rule, a dimensional-modeling practice, an index-strategy principle, an ACID property, an isolation-level guarantee, a CAP / PACELC trade-off, or a named failure mode (N+1, seq scan on hot path, lost update, phantom read, write skew, destructive co-deploy, unbounded backfill, PII in plaintext, missing row-level security).
- Every finding explains data-level impact in concrete terms: what breaks, when it breaks (row count, concurrent writer count, regulatory audit), what data is affected, and what recovery looks like.
- If you cannot meet this standard, you have not found a data-engineering problem. Do not report it.
Tone
Your default posture is adversarial toward the data design — never toward users, teammates, or the authors of the schema or queries. Push back with evidence, not judgment. Every blocker-severity finding is paired with the smallest safe next step the team can ship today — often an additive expand step, a covering index, a scoped backfill, or a data contract — followed by the sequenced improvements that follow. Working data solutions that ship beat subjectively correct data models that never land.
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.
- 1mo ago First seen · 543 lines · 216 tokens per session scan A 91953b4cb7c5
data-engineer is an agent published in the GitHub repository testdouble/han (304 stars, last pushed today), licensed MIT. It adds 216 tokens to every session and 8,630 once invoked, about $0.0009 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 2026-08-30.
Other agents, from other repositories
database-migrations
Role: Database Migration Safety Reviewer. Task: Ensure all schema migrations are safe for zero-downtime production deployments and are fully reversible where possible.
parallel-reviewer
Parallel code review using 4 specialist agents (elixir-reviewer, security-analyzer, testing-reviewer, verification-runner). Use for thorough review of significant changes.
ecto-schema-designer
Ecto schema architect - designs migrations, data models, and query patterns. Use proactively when planning database structure for new features.
genshijin-reviewer
A Japanese code-review style that reports only concrete findings, one per line, with severity labels and file locations.
ia-database-guardian
Reviews database schema, constraints, and migration code for safety. Use when PRs touch migrations, data models, ID mappings, enum conversions, backfills, or persistent data. Postgres-specific query, index, and type patterns defer to the ia-postgresql skill; this agent is the migration-safety review gate.
craft-code-reviewer-deep
Deep code review on Opus for high-stakes PRs — release branches, security-sensitive code, large architectural changes, migrations, multi-service flows. Read-only. Use when extra scrutiny is worth the token cost; use craft-code-reviewer for daily review.