modular
12 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
modularbenchmark-model
Benchmark a model served on MAX with the max benchmark command: measure throughput (tokens/sec), latency (TTFT, TPOT, inter-token latency), and GPU utilization by driving load against a running max serve endpoint. Use this whenever the user wants to benchmark, load-test, or measure the performance of a MAX model, get tokens-per-second / TTFT / TPOT numbers, run a concurrency or request-rate sweep, compare latency vs throughput, size a deployment, or produce benchmark JSON, even if they don't say
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modulardebug-model
Debug a MAX model that loads, compiles, serves, and generates tokens but whose output disagrees with a reference implementation. Use whenever parity debugging stalls on scalar taps, the model returns gibberish or wrong greedy tokens, logit cosine is high but argmax differs, or generation is coherent then diverges. Applies during an architecture port, a quantization bring-up, a multi-GPU conversion, or after a MAX upgrade. Triggers on "parity failure", "silent corruption", "logits match but token
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modulareval-model
Measures the task accuracy of text models served by MAX using standard benchmarks such as GSM8K, MMLU, HellaSwag, ARC, AIME, GPQA, TruthfulQA, WinoGrande, and BABILong. Use when benchmarking a served model, comparing it with model-card or reference scores, verifying that a new MAX model produces correct answers, or running repeatable dataset evaluations against a MAX OpenAI-compatible endpoint.
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularmigrate-max-v2-to-v3
Use when migrating a MAX model from the V2 graph API (max.nn, TensorValue, explicit Graph construction) to ModuleV3 (max.experimental.nn, Tensor, F.lazy() + compile()). Triggers on: "migrate to ModuleV3", "port a max.nn model to max.experimental", "convert TensorValue to Tensor", "update my model to the V3 API", "ModuleV3 migration". Workflow: read the concept map, rewrite imports and call signatures, and replace graph building with compile(weights=...). Align weight names with the new module hi
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularmojo-gpu-fundamentals
The basics of how to program GPUs using Mojo. Use this skill in addition to mojo-syntax when writing Mojo code that targets GPUs or other accelerators. Use targeting code to NVIDIA, AMD, Apple silicon GPUs, or others. Use this skill to overcome misconceptions about how Mojo GPU code is written.
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularmojo-python-interop
Aids in writing Mojo code that interoperates with Python using current syntax and conventions. Use this skill in addition to mojo-syntax when writing Mojo code that interacts with Python, calls Python libraries from Mojo, or exposes Mojo types/functions to Python. Also use when the user wants to build Python extension modules in Mojo, wrap Mojo structs for Python consumption, or convert between Python and Mojo types.
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularmojo-syntax
Help to write Mojo code using current syntax and conventions. Always use this skill when writing any Mojo code, including when other Mojo-specific skills (e.g., mojo-gpu-fundamentals) also apply. Use when writing or reviewing Mojo code, translating projects to Mojo, or otherwise generating Mojo. Use this skill to overcome misconceptions with how Mojo is written.
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularnew-modular-project
Creates a new Mojo or MAX project. Use when wanting to start a new Mojo or MAX project, initializing the pixi or uv environment to use Mojo or MAX, or when the user wants to begin a new Mojo or MAX project from scratch.
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularprofile-model
Profile a model running on MAX to find where it spends time and whether the GPU is saturated. Use when the user asks to "profile my model," "where is my model spending time," "why is inference slow," "is my GPU being utilized," "how much GPU am I using," "get a kernel breakdown," "capture an nsys/rocprof/ncu trace of max serve," or wants to measure MAX inference performance. Works for any model MAX can run — built-in architectures and custom ones loaded with --custom-architectures — from a pip o
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularserve-model
Serve a model with MAX's max serve command: set up the environment (pixi or uv with the max-nightly conda channel / nightly wheel index), point the server at a Hugging Face repo or local checkpoint, target a custom architecture with --custom-architectures, and pick the right serve flags for the model. Use this whenever the user wants to run, launch, start, or host a model on MAX, bring up an OpenAI-compatible endpoint, serve a custom/ported architecture, debug a max serve startup failure, or fig
- instalações
- 2
- GitHub Stars
- 204
- Atualizado
- 22 de out.
modularclosure-migration
- Migrates Mojo code off legacy parametric closures (capturing[], @parameter / @parameter, apifn) onto value-taking unified closures (api(args, fn) with {imm} / {mut} / {var} / named capture lists). Use when removing parametric overloads, fixing "capturing thin" conversion errors, rewriting nested launch/callback closures, or migrating any API that took a comptime function parameter. Also use when CI SIGSEGVs while compiling a Mojo object with compileoffloadclosure func must be fully bound — tha
- instalações
- 1
- GitHub Stars
- 188
- Atualizado
- 6 de set.
modularimport-model
Use when importing a new model architecture into MAX from a Hugging Face model ID. Triggers on: "import a model into MAX", "add model to MAX", "bring up in MAX". Workflow: inspect Hugging Face config and modeling code, scaffold from a similar MAX architecture, implement each graph layer to match HF, serve, then verify against the Hugging Face reference. When the server runs but output is wrong (gibberish, greedy mismatch, coherent-then-diverges), load debug-model for the divergence hunt instead
- instalações
- 1
- GitHub Stars
- 188
- Atualizado
- 6 de set.