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 tensormux/kernel-skills --skill optimize-triton-block-parametersgit 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/optimize-triton-block-parameters)<a href="https://agentmods.dev/skills/tensormux/kernel-skills/optimize-triton-block-parameters"><img src="https://agentmods.dev/badge/skills/tensormux/kernel-skills/optimize-triton-block-parameters/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/tensormux/kernel-skills/optimize-triton-block-parameters"><img src="https://agentmods.dev/badge/skills/tensormux/kernel-skills/optimize-triton-block-parameters.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.03701 |
| Opus 5 | $0.00000 | $0.01851 |
| Sonnet 5 | $0.00000 | $0.00740 |
| Haiku 4.5 | $0.00000 | $0.00370 |
Grade A, and why
optimize-triton-block-parameters 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.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Optimize Triton Block Parameters
Purpose
Guide the agent through the systematic process of choosing and tuning block size parameters in Triton kernels — BLOCK_M, BLOCK_N, BLOCK_K for GEMM-style kernels; BLOCK_SIZE for reduction and pointwise kernels; and the associated num_warps and num_stages values that control parallelism and pipeline depth. This is an optimization skill, not a write-kernel skill. It assumes a correct kernel exists and asks: what configuration makes it fast?
Use this when
- A Triton kernel is functionally correct and you need to choose launch configuration parameters to maximize throughput or minimize latency.
- You are using
triton.autotuneand need to define a principled config search space rather than guessing random power-of-2 combinations. - The kernel performs well on one shape but degrades on another (e.g., trained config for large M/N performing poorly on small M/N).
- You are tuning for a specific hardware target (A100, H100, RTX 4090) and need architecture-aware config decisions.
- You suspect shared memory pressure, register pressure, or pipeline stalls are limiting achieved throughput and want a systematic approach to diagnose and fix them.
Do not use this when
- The kernel has a correctness bug. Fix correctness first. Tuning a broken kernel wastes time and produces misleading measurements.
- The bottleneck is not the Triton kernel itself — for example, Python overhead, data transfer, or memory allocation are dominating the profile. Identify the actual bottleneck before tuning kernel parameters.
- You are running on hardware with radically different SM architecture (e.g., porting a config optimized for A100 to a T4 or V100 without re-profiling). Configs do not transfer across GPU generations without re-measurement.
- The kernel is not on a hot path and a 2x improvement in its execution time has negligible end-to-end impact. Do not optimize prematurely.
Inputs the agent should gather first
Before tuning, confirm:
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 · 204 lines · 0 tokens per session scan A 4271d0fd7a95
optimize-triton-block-parameters is a skill published in the GitHub repository tensormux/kernel-skills (75 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,701 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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