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 oyi77/1ai-skills --skill lakefs-versioninggit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/lakefs-versioning)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/lakefs-versioning"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/lakefs-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/oyi77/1ai-skills/lakefs-versioning"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/lakefs-versioning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 64 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00038 | $0.01539 |
| Opus 5 | $0.00019 | $0.00770 |
| Sonnet 5 | $0.00008 | $0.00308 |
| Haiku 4.5 | $0.00004 | $0.00154 |
Grade A, and why
lakefs-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 12d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
LakeFS brings Git-like version control to data lakes. It enables branching, committing, merging, and reverting data changes — supporting atomic operations, isolated experimentation, and CI/CD for data.
Capabilities
- Create branches for isolated data experiments
- Commit atomic changes to data
- Merge branches with conflict detection
- Time travel to any previous data state
- Run pre-commit and pre-merge hooks (CI/CD)
- Works with S3, Azure Blob, GCS as storage backends
- Compatible with Spark, Presto, Trino, Hive, dbt
When to Use
Trigger phrases:
-
"lakefs versioning"
-
"LakeFS data versioning — Git-like branching for data lakes, atomic commits, time"
-
Needing reproducible data pipelines
-
Experimenting with data changes without risk
-
Implementing CI/CD for data quality
-
Rolling back failed data updates
-
Supporting multi-tenant data isolation
When NOT to Use
- Task is about data processing, not versioning
- You need real-time data streaming (use streaming tools)
- Task is about data storage, not version control
- You don't have data pipeline infrastructure
- Task is about database migrations (use migration tools)
- You need data warehousing, not versioning
Pseudo Code
Implementation patterns for common use cases with this skill.
Installation
# Docker
docker run -p 8000:8000 treeverse/lakefs:latest
# Access at http://localhost:8000
# Default credentials: admin / admin
CLI Operations
# Install lakectl
brew install lakefs/tap/lakectl
# Configure
lakectl config
# Endpoint: http://localhost:8000
# Access Key: AKIAIOSFODNN7EXAMPLE
# Secret Key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
# Repository operations
lakectl repo create my-repo s3://my-bucket
lakectl branch create my-repo/main-branch
# Branch
lakectl branch create my-repo/experiment-1 --source my-repo/main
# Upload data
lakectl fs upload s3://my-repo/experiment-1/data/file.parquet --source ./file.parquet
# Commit
lakectl commit my-repo/experiment-1 -m "Add new data"
# Merge
lakectl merge my-repo/experiment-1 my-repo/main
# Diff
lakectl diff my-repo/main my-repo/experiment-1
# Time travel
lakectl fs ls my-repo/main@commit-abc123/data/
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.
- 12d ago First seen · 240 lines · 38 tokens per session scan A 3afebe02ce9d
lakefs-versioning is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 1,539 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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commit-lint
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