🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Kernelgen Flagos
πŸ¦€ ClawHub

Kernelgen Flagos

by @wbavon

Unified GPU kernel operator generation and optimization skill. Automatically detects the target repository type (FlagGems, vLLM, or general Python/Triton) an...

Versionv1.1.0
Downloads701
#crypto#can-make-purchases#clawhub
TERMINAL
clawhub install kernelgen-flagos

πŸ“– About This Skill

name: kernelgen-flagos description: > Unified GPU kernel operator generation skill. Automatically detects the target repository type (FlagGems, vLLM, or general Python/Triton) and dispatches to the appropriate specialized sub-skill. Also includes a feedback submission sub-skill for bug reports. Use this skill when the user wants to generate a GPU kernel operator, create a Triton kernel, or says things like "generate an operator", "create a kernel for X", or "/kernelgen-flagos". This single skill replaces the need to install kernelgen-general, kernelgen-for-flaggems, kernelgen-for-vllm, and kernelgen-submit-feedback separately. argument-hint: " [--func-type ]" user-invokable: true compatibility: "Python 3.8+, PyTorch with CUDA, Triton" metadata: version: "1.0.0" author: flagos-ai category: gpu-kernel-generation tags: [kernelgen, triton, gpu, mcp, operator-generation, flaggems, vllm, feedback] allowed-tools: - Bash - Bash(gh:*) - Bash(python:*) - Bash(python3:*) - Bash(command:*) - Read - Write - Edit - Glob - Grep - AskUserQuestion

kernelgen-flagos β€” Unified GPU Operator Generation Skill

This is a unified entry point that bundles four sub-skills into one:

| Sub-skill file | Purpose | |---|---| | kernelgen-general.md | Generate GPU kernels for any Python/Triton repository | | kernelgen-for-flaggems.md | Specialized generation for FlagGems repositories | | kernelgen-for-vllm.md | Specialized generation for vLLM repositories | | kernelgen-submit-feedback.md | Submit bug reports and feedback via GitHub or email |

All sub-skill files are located in the same directory as this SKILL.md file.

Routing Protocol β€” Follow This BEFORE Doing Anything Else

Phase 1: Detect Repository Type

Use the Glob tool to check for project identity files in the current working directory:

Glob: pyproject.toml
Glob: setup.py
Glob: setup.cfg

Then use the Read tool to read whichever file exists. Determine the project name from the file contents (e.g., name = "flag_gems" in pyproject.toml, or name='vllm' in setup.py).

Also use the Glob tool to check for characteristic directory structures:

FlagGems indicators (match ANY):

  • src/flag_gems/ directory exists
  • Project name is flag_gems or flag-gems or FlagGems
  • import flag_gems appears in test files

    • vLLM indicators (match ANY):

  • vllm/ directory exists at the repo root (with vllm/__init__.py)
  • Project name is vllm
  • csrc/ directory exists alongside vllm/

    • Phase 2: Dispatch to Sub-skill

    Based on the detection result, use the Read tool to read the appropriate sub-skill file from this skill's directory, then follow the instructions in that file exactly.

    To locate the sub-skill files: They are in the same directory as this SKILL.md. Use the Glob tool to find the path:

    Glob: **/skills/kernelgen-flagos/kernelgen-general.md

    Then use the Read tool to read the matched path.

    #### Decision Table

    | Detection Result | Action | |---|---| | FlagGems repository detected | Read kernelgen-for-flaggems.md and follow it | | vLLM repository detected | Read kernelgen-for-vllm.md and follow it | | Neither detected (or unknown) | Read kernelgen-general.md and follow it | | User reports a bug or requests feedback submission | Read kernelgen-submit-feedback.md and follow it |

    Important rules: 1. Always detect first, dispatch second. Never skip detection. 2. Read the entire sub-skill file before starting execution β€” do not partially read it. 3. Follow the sub-skill instructions exactly as if they were the main SKILL.md. All steps, rules, and protocols in the sub-skill apply fully. 4. Do not mix sub-skills. Once you dispatch to a sub-skill, follow it to completion. 5. If the user explicitly requests a specific sub-skill (e.g., "use the FlagGems version"), honor that request regardless of auto-detection results. 6. CRITICAL β€” MCP is mandatory: ALL operator code generation MUST go through the mcp__kernelgen-mcp__generate_operator MCP tool. NEVER generate Triton kernels, PyTorch wrappers, or operator implementations yourself. If MCP is not configured, not reachable, or fails after all retries, STOP and report the issue β€” do NOT fall back to writing code manually.

    Phase 3: Feedback Handling

    At any point during the workflow, if the user reports a bug, says something is broken, or asks to submit feedback about the skill:

    1. Use the Read tool to read kernelgen-submit-feedback.md from this skill's directory. 2. Follow the feedback submission workflow described in that file. 3. After feedback is submitted, ask the user if they want to continue with the operator generation workflow or stop.

    Quick Reference for Users

    # Generate a kernel operator (auto-detects repo type)
/kernelgen-flagos relu
    Generate with explicit function type
    
/kernelgen-flagos rms_norm --func-type normalization
    The skill will automatically:
    

    - Detect if you're in a FlagGems repo β†’ use FlagGems-specific workflow
    

    - Detect if you're in a vLLM repo β†’ use vLLM-specific workflow
    

    - Otherwise β†’ use the general-purpose workflow

    If you encounter any issues during generation, just say "submit feedback" or "report a bug" and the skill will guide you through the feedback submission process.