This skill should be used when optimizing AMD GPU kernels on MI300 using the aiter project, including running op tests, benchmarking, iterating on kernel changes, and recording results in the kernel experiment database.
Performs GPU kernel correctness and performance evaluation and LLM inference benchmarking with Magpie. Analyzes single or multiple kernels (HIP/CUDA/PyTorch), compares kernel implementations, runs vLLM/SGLang benchmarks with profiling and TraceLens, and runs gap analysis on torch traces. Creates kernel config YAMLs, discovers kernels in a project, and queries GPU specs. Use when the user mentions Magpie, kernel analyze or compare, HIP/CUDA kernel evaluation, vLLM/SGLang benchmark, gap analysis, TraceLens, creating kernel configs, or discovering GPU kernels.
Analyze Linux kernel vulnerabilities from KASAN/UBSAN/BUG crash logs or CVE descriptions. Performs full root cause analysis, exploitability assessment, patch development, and verification. Use this skill whenever the user provides a kernel crash log, KASAN report, kernel panic trace, syzbot report, or asks to analyze/patch a kernel vulnerability. Also trigger when the user mentions kernel CVEs, kernel exploit analysis, kernel bug triage, or wants to understand if a kernel bug is exploitable. Even if the user just pastes a raw stack trace from dmesg, this skill applies. --- # Kernel Vulnerability Analyzer A comprehensive skill for analyzing Linux kernel vulnerabilities — from crash log triage through root cause analysis, exploitability assessment, patch development, and verified fix delivery. This skill is designed around a **hive-mode subagent architecture**: break the analysis into parallel workstreams, plan before executing, and coordinate results across agents. ## Core Workflow Overview The analysis follows seven phases. Each phase builds on the previous, but many sub-tasks within a phase can run in parallel via subagents. ```
Validate GPU kernel correctness by comparing reference and optimized outputs. Use when verifying that an optimized or modified kernel matches a reference implementation.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
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