mflux-dev-env
It runs Makefile targets to initialize the mflux environment, lint code, format files, and execute test suites.
Install
mkdir -p .claude/skills/mflux-dev-env && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/2703" && unzip -o skill.zip -d .claude/skills/mflux-dev-env && rm skill.zipInstalls to .claude/skills/mflux-dev-env
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Set up and work in the mflux dev environment (arm64 expectation, uv, Makefile targets, lint/format/test).Key capabilities
- →Initialize local development environment
- →Execute linting and formatting checks
- →Run project test suites
- →Build project artifacts
How it works
The skill utilizes pre-configured Makefile targets to standardize development tasks and uses uv to manage Python environment execution.
Inputs & outputs
When to use mflux-dev-env
- →Initialize local dev environment
- →Run linting and formatting
- →Execute project test suite
About this skill
mflux dev environment
This repo expects macOS arm64 and prefers uv + justfile recipes.
When to Use
- You’re setting up the repo locally or diagnosing environment/setup issues.
- You need the canonical way to run lint/format/check/build/test.
Instructions
- Prereq:
just≥ 1.50 (brew install just). CI lints the justfile withjust --fmt --check; runjust fmt-justfileto auto-fix formatting. - Prefer justfile recipes:
- Install:
just install - Lint:
just lint - Format:
just format - Pre-commit suite:
just check - Build:
just build
- Install:
- Prefer
uv run ...for running Python commands to ensure the correct environment. - When running tests, keep
MFLUX_PRESERVE_TEST_OUTPUT=1enabled (the justfile test recipes already do this).
Type checking (ty)
- The type checker is astral's
ty, pinned exactly inpyproject.tomldev deps (mirrored in.pre-commit-config.yamland run in CI). Run it withjust typecheck(oruv run ty check). [tool.ty.rules]inpyproject.tomlcarries a one-time migration baseline (mypy → ty): every rule listed there had pre-existing violations and is set to"ignore", with violation counts in comments.- Planned burn-down: we intend to re-enable these rules piecemeal — delete one ignore line, fix all resulting
ty checkerrors, commit, repeat. Prefer starting with low-count rules. Do not add new ignore lines; new code must pass all rules not in the baseline.
When not to use it
- →Non-macOS arm64 environments
Prerequisites
Limitations
- →Expects macOS arm64 architecture
How it compares
It enforces a canonical set of commands for the repository rather than requiring manual environment configuration.
Compared to similar skills
mflux-dev-env side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| mflux-dev-env (this skill) | 2 | 6mo | No flags | Beginner |
| machine-learning-ops-ml-pipeline | 4 | 5mo | No flags | Advanced |
| uv | 3 | 7mo | Review | Beginner |
| vastai-core-workflow-b | 1 | 2mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by filipstrand
View all by filipstrand →You might also like
machine-learning-ops-ml-pipeline
sickn33
Design and implement a complete ML pipeline for: $ARGUMENTS
uv
mitsuhiko
Use `uv` instead of pip/python/venv. Run scripts with `uv run script.py`, add deps with `uv add`, use inline script metadata for standalone scripts.
vastai-core-workflow-b
jeremylongshore
Execute Vast.ai secondary workflow: Core Workflow B. Use when implementing secondary use case, or complementing primary workflow. Trigger with phrases like "vastai secondary workflow", "secondary task with vastai".
hugging-face-jobs
patchy631
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
mlops-initialization
fmind
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.
mqtt-uns-feeder
clemensv
Use when adding MQTT/Unified-Namespace transport to a feeder in this repo — either alongside an existing Kafka feeder (transport split) or for a brand-new source where MQTT is the primary fit. Covers UNS topic-tree design, xRegistry MQTT messagegroup pattern, paho-mqtt v5 binary-mode CloudEvent emis