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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: [main]
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pull_request:
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branches: [main]
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permissions:
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contents: read
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jobs:
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validate-skills:
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name: Validate Skills
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Run skill validation
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run: python3 scripts/validate_skills.py
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# ── Add future check jobs below ──────────────────────────
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# Example:
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#
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# lint-markdown:
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# name: Lint Markdown
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# runs-on: ubuntu-latest
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# steps:
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# - uses: actions/checkout@v4
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# - name: Run markdownlint
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# uses: DavidAnson/markdownlint-cli2-action@v19
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# with:
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# globs: "skills/**/*.md"

‎scripts/validate_skills.py‎

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errors.append(
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f"{skill_dir.name}: name '{name}' does not match directory name"
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)
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if not name.endswith("-flagos"):
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errors.append(
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f"{skill_dir.name}: name '{name}' must end with '-flagos' suffix "
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f"(e.g., '{name}-flagos')"
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)
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# Validate description
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desc = fields.get("description", "")

‎skills/kernelgen-flagos/LICENSE.txt‎

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‎skills/kernelgen-flagos/README.md‎

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# kernelgen-flagos: Unified GPU Kernel Operator Generation
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[中文版](README_zh.md)
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## Overview
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`kernelgen-flagos` is a unified AI coding skill that generates GPU kernel operators via the `kernelgen-mcp` MCP service. It automatically detects the target repository type and dispatches to the appropriate specialized workflow.
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### Problem Statement
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Writing high-performance GPU kernels is complex and error-prone. Different projects (FlagGems, vLLM, custom Triton repos) each have unique conventions for operator implementation, testing, and registration. Previously, users needed to install separate skills for each project type.
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This unified skill bundles **four sub-skills** into one package, so users only need a single install:
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| Sub-skill | Purpose |
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|---|---|
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| **kernelgen-general** | Generate GPU kernels for any Python/Triton repository |
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| **kernelgen-for-flaggems** | Specialized for FlagGems (`pointwise_dynamic`, `_FULL_CONFIG`, categorized tests) |
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| **kernelgen-for-vllm** | Specialized for vLLM (SPDX headers, `init_logger`, `@triton.autotune`, custom op registration) |
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| **kernelgen-submit-feedback** | Submit bug reports via GitHub Issues or email |
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### Usage
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```bash
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# Generate a kernel operator (auto-detects repo type)
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/kernelgen-flagos relu
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# Generate with explicit function type
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/kernelgen-flagos rms_norm --func-type normalization
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# Generate for any operator
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/kernelgen-flagos silu_and_mul
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```
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The skill will automatically:
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- Detect if you're in a **FlagGems** repo and use the FlagGems-specific workflow
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- Detect if you're in a **vLLM** repo and use the vLLM-specific workflow
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- Otherwise, use the **general-purpose** workflow with dynamic repo discovery
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| Argument | Required | Default | Description |
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|---|---|---|---|
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| `operator_name` | Yes | — | Operator name in snake_case (e.g. `relu`, `rms_norm`, `silu_and_mul`) |
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| `--func-type` | No | Auto-inferred | Function type category (varies by detected repo type) |
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### Feedback
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If you encounter any issues during generation, say "submit feedback" or "report a bug" and the skill will guide you through submitting a GitHub issue or email.
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---
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## How It Works
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```
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┌──────────────────────────────────────────────────────────┐
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│ Phase 1 Detect repository type │
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│ ├── FlagGems? → kernelgen-for-flaggems.md │
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│ ├── vLLM? → kernelgen-for-vllm.md │
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│ └── Other? → kernelgen-general.md │
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│ │
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│ Phase 2 Execute the selected sub-skill workflow │
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│ (environment check → MCP generation → │
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│ code adaptation → testing → benchmarking) │
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│ │
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│ Phase 3 Feedback handling (on demand) │
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│ → kernelgen-submit-feedback.md │
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└──────────────────────────────────────────────────────────┘
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```
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---
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## Directory Structure
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```
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skills/kernelgen/
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├── SKILL.md # Unified entry point (routing logic)
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├── kernelgen-general.md # General-purpose sub-skill
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├── kernelgen-for-flaggems.md # FlagGems-specific sub-skill
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├── kernelgen-for-vllm.md # vLLM-specific sub-skill
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├── kernelgen-submit-feedback.md # Feedback submission sub-skill
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├── LICENSE.txt # Apache 2.0 license
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├── README.md # This document (English)
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└── README_zh.md # Chinese version
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```
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---
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## File Descriptions
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### `SKILL.md`
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The unified entry point. Contains routing logic that auto-detects the repository type (FlagGems, vLLM, or generic) and reads the appropriate sub-skill file to execute.
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### `kernelgen-general.md`
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Full 10-step workflow for generating GPU kernel operators in any Python/Triton repository. Includes dynamic repo structure discovery, convention detection, and adaptive code placement.
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### `kernelgen-for-flaggems.md`
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9-step workflow specialized for FlagGems repositories. Handles `pointwise_dynamic` wrappers, promotion methods, `_FULL_CONFIG` registration, categorized test files, and FlagGems-specific conventions.
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### `kernelgen-for-vllm.md`
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9-step workflow specialized for vLLM repositories. Handles SPDX license headers, `vllm.logger.init_logger`, `@triton.autotune`, custom op registration, and vLLM directory conventions.
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### `kernelgen-submit-feedback.md`
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Feedback submission workflow. Collects bug reports with auto-detected environment info and submits via GitHub Issues (`gh` CLI) or email fallback.
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---
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## Usage in FlagOS Skills Repository
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### Quick Install (via npx)
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```bash
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# Install the unified kernelgen skill (includes all sub-skills)
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npx skills add flagos-ai/skills --skill kernelgen -a claude-code
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# Or install all Flagos skills at once
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npx skills add flagos-ai/skills -a claude-code
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```
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### Manual Install
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```bash
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# From your project root
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mkdir -p .claude/skills
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cp -r <path-to-this-repo>/skills/kernelgen .claude/skills/
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```
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---
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## License
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This project is licensed under the Apache 2.0 License. See [LICENSE.txt](LICENSE.txt) for details.

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