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 5.5 | $0.00015 | $0.00616 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo 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 (17 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 5.5. 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 skills, from other repositories
night-watch
Autonomous maintenance (dep updates, dead code, small refactors) in isolated branch, off-hours. Triggers: night watch, autonomous maintenance, dep updates.
checkpoint-management
Git-backed state management for safe rollback. Create and restore checkpoints with tagged commits and metadata tracking.
git-batch-commit
A Git workflow tool for splitting mixed file changes into several focused commits. A commit is a saved group of project changes in Git, and this tool groups files by type and creates standardised commit messages.
save
Lightweight incremental checkpoint for a long autonomous run — refresh the handoff, commit converged work, and push, fired frequently (after each subagent merge, periodically, and when nearing the rate-limit → then schedule a resume past the window reset). Complements /pause (deliberate one-shot handoff) and /resume…
dimension-door
Dimension Door is the quick-switch spell: branch to branch, environment to environment, project to project. It is not a migration and it is not discovery. It assumes both endpoints are known, prepared, and close enough in shape that you can step across with only the context you actually need.
agent-evaluation-reporting
Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.