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 agentmods add skills/tensormux/kernel-skills/fuse-elementwise-opsnpx skills add tensormux/kernel-skills --skill fuse-elementwise-opsgit clone --depth 1 https://github.com/tensormux/kernel-skillsWrote 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/tensormux/kernel-skills/fuse-elementwise-ops)<a href="https://agentmods.dev/skills/tensormux/kernel-skills/fuse-elementwise-ops"><img src="https://agentmods.dev/badge/skills/tensormux/kernel-skills/fuse-elementwise-ops.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.02391 |
| Opus 5 | $0.00000 | $0.01196 |
| Sonnet 5 | $0.00000 | $0.00478 |
| Haiku 4.5 | $0.00000 | $0.00239 |
Grade A, and why
fuse-elementwise-ops 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Fuse Elementwise Operations
Purpose
Guide the agent through deciding whether to fuse multiple elementwise operations into a single kernel pass, and if so, how to implement the fusion correctly and efficiently in CUDA or Triton.
Use this when
- Two or more consecutive elementwise operations are applied to the same tensor and the intermediate results are not reused elsewhere.
- Profiling shows the pipeline is memory-bandwidth bound and the operations between loads and stores are cheap arithmetic.
- Epilogue fusion into an existing GEMM or convolution kernel is being considered (e.g., bias add + activation after GEMM).
- The operation chain is simple enough that a fused kernel remains readable and maintainable.
- torch.compile/inductor is unavailable or insufficient (e.g., custom op, non-standard dtype, or strict latency requirements).
Do not use this when
- Any operation in the chain requires inter-element communication — softmax, layernorm, and reductions are not elementwise and require separate synchronization steps or multi-pass designs.
- The operations involve different tensor shapes that require broadcasting logic complex enough to obscure correctness.
- torch.compile with
mode="reduce-overhead"ormode="max-autotune"already fuses the chain adequately — validate this before writing a manual kernel. - The chain is so long that register pressure in the fused kernel would reduce occupancy below the unfused baseline.
- Development and maintenance cost of a custom kernel is not justified by the measured speedup.
Inputs the agent should gather first
- The ordered list of operations to fuse, with their mathematical definitions.
- Input and output dtypes for each operation; flag any dtype transitions (e.g., fp16 input, fp32 accumulation, fp16 output).
- Tensor shapes and memory layouts (contiguous, strided, transposed) for all inputs.
- Whether any intermediate tensor is consumed by a path other than the next operation in the chain. If yes, fusion is not valid.
- Target hardware (compute capability, memory bandwidth, L2 size) and whether the workload is bandwidth-bound or compute-bound on that hardware.
- Whether torch.compile has already been tried and what the result was.
- Epilogue context: if this follows a GEMM, what CUTLASS or cuBLAS epilogue API is available.
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.
- 5d ago First seen · 108 lines · 0 tokens per session scan A 74e6fecfd760
fuse-elementwise-ops is a skill published in the GitHub repository tensormux/kernel-skills (73 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,391 tokens. 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.
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