Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AMD-AGI/Apex --skill mi300-cdna3-architecturegit clone --depth 1 https://github.com/AMD-AGI/ApexWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/amd-agi/apex/mi300-cdna3-architecture)<a href="https://agentmods.dev/skills/amd-agi/apex/mi300-cdna3-architecture"><img src="https://agentmods.dev/badge/skills/amd-agi/apex/mi300-cdna3-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/amd-agi/apex/mi300-cdna3-architecture"><img src="https://agentmods.dev/badge/skills/amd-agi/apex/mi300-cdna3-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00324 |
| Opus 5 | $0.00022 | $0.00162 |
| Sonnet 5 | $0.00009 | $0.00065 |
| Haiku 4.5 | $0.00004 | $0.00032 |
Grade A, and why
mi300-cdna3-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
MI300 CDNA3 Architecture (Hardware Tuning Guide)
Use this when optimizing HIP/Triton kernels for MI300/CDNA3 hardware specifics.
Key points to remember (see reference for details):
- MFMA choices: pick variants by matrix size (small -> high block count like 4x4x1_16B; large -> bigger tiles). Use CBSZ/BLGP for broadcast/permutation.
- Dual register files: minimize VGPR↔AccVGPR traffic; keep hot accumulations in AccVGPR.
- Data formats: FP8/BF8 support, conversions/rounding; validate accuracy when using reduced precision.
- Structured sparsity: 4:2 pattern via SMFMAC; ensure encoding matches pattern to get gains.
- Memory: LDS bank-conflict avoidance; coalesced global buffer ops; use cache control bits (GLC/NV) and schedule loads early with
s_waitcnt. - Registers/occupancy: respect SGPR single-read limits; align VGPR usage; watch overall pressure to keep occupancy.
- Control/exec: wavefront characteristics, dependency scheduling; hide latency via instruction scheduling.
- Best practices section summarizes matrix, memory, register, and optimization checklists.
References:
references/AMD Instinct MI300 CDNA3 Architecture Guide for High-Quality HIP Kernel Development.md
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 22 lines · 44 tokens per session scan A 6b78ff01b8d1
mi300-cdna3-architecture is a skill published in the GitHub repository AMD-AGI/Apex (76 stars, last pushed 7d ago), licensed MIT. It adds 44 tokens to every session and 324 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
choose-launch-configuration
Guide the agent through selecting the correct and efficient thread block dimensions and grid dimensions for a CUDA kernel, covering occupancy analysis, register and shared memory constraints, tail effects, persistent kernels, and when to use cudaOccupancyMaxActiveBlocksPerMultiprocessor as a decision tool.
write-backend-agnostic-kernel-plan
Guide the agent through planning a compute kernel that must run correctly and performantly on multiple hardware backends (NVIDIA, AMD, CPU fallback, or future backends) before any backend-specific implementation is written — covering abstraction strategy, feature compatibility mapping, and the tradeoffs between…
optimize-global-memory-access
Guide the agent through diagnosing and restructuring CUDA global memory access patterns to maximize effective memory bandwidth, covering coalescing requirements, vectorized loads, AoS vs SoA layouts, shared memory staging for non-coalesced patterns, and L2 cache behavior.
write-fp8-kernel
Guide the agent through designing and implementing FP8 compute kernels for inference and training on NVIDIA Hopper (sm90) and Ada Lovelace (sm89) hardware, covering FP8 format selection, scaling strategy, tensor core usage via WGMMA or cuBLAS, and dequantization epilogue design.
gke-compute-classes
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…
jetson-diagnostic
Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.