How do I install the Mission Control Cuda Kernel Generation skill?

Run npx skillmds@latest add mn755/mission-control-cuda-kernel-generation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Mission Control Cuda Kernel Generation safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Mission Control Cuda Kernel Generation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Mission Control Cuda Kernel Generation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Mission Control Cuda Kernel Generation?

MN755 (@mn755) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Mission Control Cuda Kernel Generation by mn755

Name: Mission Control Cuda Kernel Generation
Author: MN755

File contents

Mission Control CUDA Kernel Generation

Purpose

Use Mission Control to plan or execute CUDA kernel generation, repair, or extension with explicit build, test, benchmark, and profiling loops.

The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager.

Use when

The repo contains CUDA kernels or GPU extensions.
The user asks Mission Control to write CUDA or CUDA Tile code.
GPU correctness and performance both matter.

Workflow

Confirm the repo is a CUDA-capable workspace through Mission Control codebase understanding or repo signals.
Read current status, validation, and diagnostics before changing kernels.
Ask Mission Control to produce a GPU-aware task plan with build, focused test, benchmark, and profile checkpoints.
Keep cluster-health or pending-pod blockers visible so code and infrastructure do not get confused.
Review the resulting validation evidence and handoff notes.

Mission Control calls

Tools:

mission_control_import_existing_codebase
mission_control_start_task
mission_control_get_status

Resources:

mission-control://projects/{project_id}/codebase-map
mission-control://projects/{project_id}/validation-summary
mission-control://projects/{project_id}/diagnostics

User-facing output

Show whether Mission Control detected a CUDA repo.
Show the planned kernel-edit loop and required validation.
Call out whether the current failure looks like code or infrastructure.

Approval behavior

Builds, tests, benchmark commands, profilers, and dependency changes still follow Mission Control approval policy.

Never do

Do not treat CUDA work like generic Python editing.
Do not claim GPU performance wins without benchmark or profiling evidence.
Do not ignore cluster blockers such as pending pods or saturated GPU memory.

Failure and fallback

If CUDA-specific signals are weak, report that Mission Control could not confirm GPU mode yet and fall back to a narrower investigation before broad kernel edits.

Example invocation

Use Mission Control to generate and validate a CUDA kernel change in this repo.

---name: mission-control-cuda-kernel-generationdescription: Route CUDA kernel generation or repair through Mission Control with GPU-aware validation and infrastructure checks.---# Mission Control CUDA Kernel Generation## PurposeUse Mission Control to plan or execute CUDA kernel generation, repair, or extension with explicit build, test, benchmark, and profiling loops.The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager.## Use when- The repo contains CUDA kernels or GPU extensions.- The user asks Mission Control to write CUDA or CUDA Tile code.- GPU correctness and performance both matter.## Workflow1. Confirm the repo is a CUDA-capable workspace through Mission Control codebase understanding or repo signals.2. Read current status, validation, and diagnostics before changing kernels.3. Ask Mission Control to produce a GPU-aware task plan with build, focused test, benchmark, and profile checkpoints.4. Keep cluster-health or pending-pod blockers visible so code and infrastructure do not get confused.5. Review the resulting validation evidence and handoff notes.## Mission Control callsTools:- `mission_control_import_existing_codebase`- `mission_control_start_task`- `mission_control_get_status`Resources:- `mission-control://projects/{project_id}/codebase-map`- `mission-control://projects/{project_id}/validation-summary`- `mission-control://projects/{project_id}/diagnostics`## User-facing output- Show whether Mission Control detected a CUDA repo.- Show the planned kernel-edit loop and required validation.- Call out whether the current failure looks like code or infrastructure.## Approval behaviorBuilds, tests, benchmark commands, profilers, and dependency changes still follow Mission Control approval policy.## Never do- Do not treat CUDA work like generic Python editing.- Do not claim GPU performance wins without benchmark or profiling evidence.- Do not ignore cluster blockers such as pending pods or saturated GPU memory.## Failure and fallbackIf CUDA-specific signals are weak, report that Mission Control could not confirm GPU mode yet and fall back to a narrower investigation before broad kernel edits.## Example invocation`Use Mission Control to generate and validate a CUDA kernel change in this repo.`

Frequently asked questions

Mission Control Cuda Kernel Generation