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 vaquarkhan/data-engineering-agent-skills --skill lakefs-and-data-versioninggit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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/vaquarkhan/data-engineering-agent-skills/lakefs-and-data-versioning)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/lakefs-and-data-versioning"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lakefs-and-data-versioning/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/vaquarkhan/data-engineering-agent-skills/lakefs-and-data-versioning"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/lakefs-and-data-versioning.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 | $0.00044 | $0.01072 |
| Opus 5 | $0.00022 | $0.00536 |
| Sonnet 5 | $0.00009 | $0.00214 |
| Haiku 4.5 | $0.00004 | $0.00107 |
Grade A, and why
lakefs-and-data-versioning 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 7d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
lakeFS And Data Versioning
Overview
Use this skill when datasets need branch-like safety and controlled promotion. It helps agents treat data versioning as an operational control for risky changes, validation before publish, environment isolation, and safe rollback for lakehouse operations.
When to Use
- validating data changes in isolation before merging to the main branch
- branching lake data for experiments, migrations, or schema changes
- promoting data states between environments (dev → staging → production)
- implementing safe rollback for risky pipeline changes
- running A/B comparisons between data versions
- creating reproducible dataset snapshots for compliance or debugging
Do not use this when the data platform has no concept of object-level versioning or when changes are small enough that point-in-time snapshots (Iceberg, Delta) provide sufficient safety.
Workflow
-
Define the versioning goal and branch lifecycle. Include:
- what operation needs safety? (schema migration, backfill, new pipeline, experiment)
- what is the branch lifecycle? (short-lived feature branch vs long-lived environment branch)
- who can create, merge, and delete branches?
- what is the promotion path from branch to main?
- how long do branches live before cleanup?
-
Design the branch strategy for your use case.
- feature branches: isolate a single pipeline change, validate, then merge
- environment branches: map to dev/staging/prod with controlled promotion
- experiment branches: run alternative logic and compare outputs before committing
- avoid long-lived branches that drift from main — define merge frequency
- document naming conventions:
feature/,experiment/,migration/
-
Attach validation and quality gates to branch operations.
- run data quality checks on the branch before merge is allowed
- compare branch output against main for reconciliation (row counts, aggregates, samples)
- use pre-merge hooks to enforce contract compliance
- produce evidence of validation as a merge prerequisite
- block merges that fail quality gates — no manual overrides without review
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.
- 7d ago First seen · 97 lines · 44 tokens per session scan A 75e0b3192518
lakefs-and-data-versioning is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 1,072 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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