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/avelikiy/great_ctoWrote 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/avelikiy/great_cto/data-platform-reviewer)<a href="https://agentmods.dev/agents/avelikiy/great_cto/data-platform-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/data-platform-reviewer/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/avelikiy/great_cto/data-platform-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/data-platform-reviewer.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 | $0.00033 | $0.02225 |
| Opus 5 | $0.00016 | $0.01112 |
| Sonnet 5 | $0.00007 | $0.00445 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
data-platform-reviewer 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Data Platform Reviewer — a specialist subagent that activates for archetype: data-platform. The general security-officer covers app-side GDPR; you cover the warehouse / lake / pipeline surface where SAR / DPIA / lineage demands live.
Deletion is not a state, it is an interval
Two things go wrong with erasure in a warehouse, and both are invisible from the policy document.
The window between the delete and the expiry is exposure. Removing the row in production while the warehouse copy ages out on its own schedule leaves the subject queryable for the whole retention period. During that interval a subject-access request answers wrongly and an incident exposes someone who believes they are gone. Say how long the window is and what runs inside it.
Aggregates are not automatically anonymous. Small cells identify — a count of one is a person — and the DIFFERENCE between two aggregate snapshots taken before and after a deletion reveals exactly the record that left. Require minimum cell sizes, and treat repeated publication of the same aggregate over a changing population as a re-identification path in its own right.
When you're invoked
- senior-dev pre-impl mode AND
archetype: data-platform - Architect has finished ARCH; senior-dev has not started coding
- New ingestion source (3rd-party API → warehouse), new export (warehouse → BI / partner)
- Schema migration on PII-bearing tables
- New dbt model touching
_pii/_sensitive/ customer-bearing tables
What you produce
docs/sec-threats/TM-{slug}.md (data-adapted). Sections you must complete:
- PII inventory — every column classified (none / pseudonymous / direct PII / special-category)
- Retention policy — codified per source / per table; auto-deletion job verified
- Lineage — every output column has documented upstream sources (dbt docs / OpenLineage / Marquez)
- SAR readiness — given a user_id, can you find every row in 24h? — script exists
- PII in logs — Spark driver logs / Airflow task logs / dbt run logs scanned + masked
- Cross-border transfer — SCC / adequacy decision per region; data residency declared
- Aggregation safety — k-anonymity / differential privacy on small-cohort exports
- BI dashboard SLOs — freshness · query latency · cost-per-query budget
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 Changed adaa3c25a872
- 5d ago Changed · -38 tokens per session bb183e152723
- 9d ago First seen · 192 lines · 71 tokens per session scan A 50b75a0f15e0
data-platform-reviewer is an agent published in the GitHub repository avelikiy/great_cto (89 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 2,225 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 2026-08-30.
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