FlagOS is a fully open-source AI system software stack for heterogeneous AI chips, allowing AI models to be developed once and seamlessly ported to a wide range of AI hardware with minimal effort. This repository collects reusable Skills for FlagOS — injecting domain knowledge, workflow standards, and best practices into AI coding agents.

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What are Skills?

Skills are folder-based capability packages: each skill uses documentation, scripts, and resources to teach agents to reliably and reproducibly complete tasks in a specific domain. Each skill folder contains a SKILL.md file with YAML frontmatter (name + description) followed by detailed agent instructions. Skills can also include reference docs, scripts, and assets.

This repository follows the Agent Skills open standard.

Quick Start

FlagOS Skills are compatible with Claude Code, Cursor, Codex, and any agent supporting the Agent Skills standard.

npx (Recommended — works with all agents)

Use the skills CLI to install skills directly — no cloning needed:

# List available skills in this repository
npx skills add flagos-ai/skills --list

# Install a specific skill into your project
npx skills add flagos-ai/skills --skill model-migrate-flagos

# Install a specific skill globally (user-level)
npx skills add flagos-ai/skills --skill model-migrate-flagos --global

# Install all skills at once
npx skills add flagos-ai/skills --all

# Install for specific agents only
npx skills add flagos-ai/skills --agent claude-code cursor

Other useful commands:

npx skills list              # List installed skills
npx skills find              # Search for skills interactively
npx skills update            # Update all skills to latest versions
npx skills remove            # Interactive remove

Note: No prior installation needed — npx downloads the skills CLI automatically.

Claude Code

1. Register the repository as a plugin marketplace (in Claude Code interactive mode):

/plugin marketplace add flagos-ai/skills

Or from the terminal:

claude plugin marketplace add flagos-ai/skills

2. Install skills:

/plugin install flagos-skills@flagos-skills

Or from the terminal:

claude plugin install flagos-skills@flagos-skills

After installation, mention the skill in your prompt — Claude automatically loads the corresponding SKILL.md instructions.

Cursor

This repository includes Cursor plugin manifests (.cursor-plugin/plugin.json and .cursor-plugin/marketplace.json).

Install from the repository URL or local checkout via the Cursor plugin flow.

Codex

Use the $skill-installer inside Codex:

$skill-installer install model-migrate-flagos from flagos-ai/skills

Or provide the GitHub directory URL:

$skill-installer install https://github.com/flagos-ai/skills/tree/main/skills/model-migrate-flagos

Alternatively, copy skill folders into Codex's standard .agents/skills location:

cp -r skills/model-migrate-flagos $REPO_ROOT/.agents/skills/

See the Codex Skills guide for more details.

Gemini CLI

gemini extensions install https://github.com/flagos-ai/skills.git --consent

This repo includes gemini-extension.json and agents/AGENTS.md for Gemini CLI integration. See Gemini CLI extensions docs for more help.

Manual / Other Agents

For any agent that supports the Agent Skills standard, point it at the skills/ directory in this repository. Each skill is self-contained with a SKILL.md entry point. The agents/AGENTS.md file can also be used as a fallback for agents that don't support skills natively.

Skills Catalog

Category	Sub-category	Skill	Description
Inference & Serving	Model Migration	model-migrate-flagos	Migrate a model from upstream vLLM into vllm-plugin-FL (pinned at v0.13.0). Automates the full 13-step copy-then-patch workflow with E2E verification.
	Serving Deployment	PR #6 flagrelease	Deploy and configure vLLM-FL / SGLang-FL serving instances across multi-chip environments.
	Preflight Check	Planned	Verify GPU/accelerator availability, driver versions, Python env, and chip compatibility before running inference.
Training & RLHF	Training Migration	Planned	Adapt training scripts for FlagScale / Megatron-LM-FL across different AI chips.
	RLHF Pipeline	Planned	Set up and debug verl-FL reinforcement learning workflows.
Operator & Compiler	TLE Primitive Dev	PR #2 tle-developer	Develop TLE (Triton Language Extensions) primitives and build operators using TLE-Lite / TLE-Struct / TLE-Raw across FlagTree backends.
	Operator Optimization	Planned	Guided iterative performance tuning for existing FlagGems / FlagAttention operators — profiling, bottleneck analysis, and optimization suggestions.
	Kernel Generation	PR #10 kernelgen	General-purpose GPU kernel generation via KernelGen MCP for any Python/Triton project, covering multi-chip targets (NVIDIA, Ascend, Cambricon, Moore Threads, Iluvatar, etc.).
	Kernel Gen for FlagGems	PR #10 kernelgen-for-flaggems	FlagGems-specific kernel generation with promotion rules, pointwise_dynamic wrappers, and _FULL_CONFIG registration.
	Kernel Gen for vLLM	PR #10 kernelgen-for-vllm	vLLM-specific kernel generation with SPDX headers, vllm.logger, @triton.autotune, and custom op registration.
	KernelGen Feedback	PR #10 kernelgen-submit-feedback	Submit bug reports and improvement suggestions for KernelGen as structured GitHub issues.
	Operator Diagnosis	Planned	Diagnose abnormal operators in the FlagOS stack — identify precision errors, performance regressions, and backend-specific failures across chips.
	Compiler Backend Adaptation	Planned	Port and debug FlagTree / Triton compiler backends for new AI chip architectures.
Communication	Collective Ops	Planned	Adapt and benchmark FlagCX cross-chip communication primitives (AllReduce, AllGather, Send/Recv, etc.) across 11+ backends (NCCL, IXCCL, CNCL, MCCL, etc.).
Benchmarking & Eval	Performance Benchmark	PR #6 perf-test	Run and analyze FlagPerf benchmarks; generate multi-dimensional comparison reports (throughput, memory, scaling) across chips.
	E2E Accuracy Eval	PR #6 model-verify	Token-level accuracy verification between different serving backends or chip targets.
Environment & Deployment	Stack Installation	PR #6 install-stack	One-click FlagOS software stack installation on a target chip — auto-detect hardware, resolve dependencies, and configure the full toolchain (FlagTree + FlagGems + vLLM-FL + FlagCX).
	Base Image Selection	PR #5 gpu-container-setup	Find and recommend the optimal base Docker image for domestic AI chip model deployment — matching chip type, driver version, CUDA/SDK compatibility, and framework requirements.
	Container Build	Planned	Build and publish multi-chip Docker images with correct driver/library dependencies.
	CI Pipeline	Planned	Configure and debug FlagOps CI/CD pipelines for multi-chip build matrices.
Developer Tooling	Skill Development	skill-creator-flagos	Create, improve, and validate skills for this repository. Scaffolding, conventions check, and test case evaluation.
	Chip Onboarding	Planned	Guide new chip vendors through the FlagOS adaptation process end-to-end.

Using skills in your agent

Once a skill is installed, mention it directly in your prompt:

• "Use model-migrate-flagos to migrate the Qwen3-5 model from upstream vLLM"
• "/model-migrate-flagos qwen3_5"
• "Port the DeepSeek-V4 model to vllm-plugin-FL"

Your agent automatically loads the corresponding SKILL.md instructions and helper scripts.

Repository Structure

├── .claude-plugin/          # Claude Code plugin manifest
│   └── marketplace.json
├── .cursor-plugin/          # Cursor plugin manifest
│   ├── marketplace.json
│   └── plugin.json
├── agents/                  # Codex / Gemini CLI fallback
│   └── AGENTS.md
├── assets/                  # Repository-level static resources
├── contributing.md          # Contribution guidelines
├── gemini-extension.json    # Gemini CLI extension manifest
├── scripts/                 # Repository-level utility scripts
│   └── validate_skills.py   # Batch validate all skills
├── skills/                  # Skill directories
│   ├── model-migrate-flagos/    # Model migration workflow
│   └── ...
├── spec/                    # Agent Skills standard & local conventions
│   ├── README.md
│   └── agent-skills-spec.md
└── template/                # Template for creating new skills
    └── SKILL.md

Creating a New Skill

1. Create directory & copy template

   mkdir skills/<skill-name>
   cp template/SKILL.md skills/<skill-name>/SKILL.md

2. Edit frontmatter — name (lowercase + hyphens, must match directory name) and description (what it does + when to trigger)

3. Write the body — Overview, Prerequisites, Execution steps, Examples (2-3), Troubleshooting

4. Add supporting files (optional) — references/, scripts/, assets/, LICENSE.txt

5. Validate

   python scripts/validate_skills.py

See contributing.md for the full contribution guide.

License

Apache License 2.0