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/choose-launch-configurationnpx skills add tensormux/kernel-skills --skill choose-launch-configurationgit clone --depth 1 https://github.com/tensormux/kernel-skillsWhat 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 | $0.03581 |
| Opus 5 | $0.00000 | $0.01791 |
| Sonnet 5 | $0.00000 | $0.00716 |
| Haiku 4.5 | $0.00000 | $0.00358 |
Grade A, and why
choose-launch-configuration 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 yesterday.
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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Choose CUDA Launch Configuration
Purpose
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.
Use this when
- Writing a new CUDA kernel and choosing the initial block and grid dimensions
- A kernel is underperforming and you suspect the launch configuration limits occupancy or causes tail effects
- Selecting block dimensions for a kernel with significant register or shared memory usage
- Implementing a persistent kernel or a cooperative grid launch
- Tuning a kernel where block size is a compile-time template parameter and you want to choose the best default
Do not use this when
- The kernel's performance is dominated by a compute or memory bottleneck unrelated to occupancy: fixing the launch config will not help if the kernel is register-bound or has severe memory access inefficiency
- The launch configuration is already determined by an API contract (e.g., cuBLAS, cuDNN, cooperative kernels with fixed grid requirements): do not override the library's choices
- The block size is constrained by the algorithm's data decomposition to a specific value (e.g., exactly 32 threads for a warp-level primitive): respect the algorithmic constraint and do not change it to improve occupancy
Inputs the agent should gather first
- Kernel register usage: from
ptxas -voutput,nvcc --ptxas-options=-v, or from Nsight Compute profiling. Registers per thread is the primary constraint on occupancy when register usage is high. - Static shared memory per block: declared
__shared__arrays. Size in bytes. - Dynamic shared memory per block: the third kernel launch argument. Size in bytes.
- Problem size: total number of work items (elements, rows, output tiles, etc.). Determines the minimum grid size needed to cover all work.
- Hardware target: SM architecture and SM count. Key occupancy limits differ by architecture. Also: the maximum threads per block (1024 on all current GPUs), the maximum blocks per SM (varies: 16–32 depending on SM gen), the register file size per SM (65536 32-bit registers on SM70+), the smem per SM.
- Whether the kernel is latency-sensitive: for latency-critical single-sample inference, occupancy may matter less than minimizing launch overhead and maximizing L2 utilization for a fixed small grid.
- Whether cooperative groups are needed: cooperative grid launches have additional constraints (entire grid must fit resident on the GPU).
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.
- yesterday First seen · 131 lines · 0 tokens per session scan A 69471d48d256
choose-launch-configuration is a skill published in the GitHub repository tensormux/kernel-skills (70 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,581 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.
Other skills, from other repositories
design-impl-audit
The Design-vs-Implementation Audit Copilot. A universal skill for any project, any language, any design document. Before work starts, it reminds engineers what must be delivered; after code lands, it checks what was actually delivered, what drifted, and what silently went beyond the design. Feed it a design doc plus a…
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
brainstorming
Brainstorming design process. Must be used before any creative work - creating features, building components, adding functionality, or modifying behavior. Transforms ideas into complete designs and specifications through collaborative dialogue. Trigger words: brainstorm, design discussion, requirement analysis…
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
mergeable-feature-dev
For new-module development or cross-module refactors, enforce separation of scaffolding from deliverables so that "locally standalone runnable" and "low-cost mergeable into the trunk" hold simultaneously. Trigger words: mergeable, low-cost handoff, standalone run, scaffolding, deliverable layer, refactor merge.
prompt-engineering
Prompt engineering techniques and patterns. Use when writing agent commands, hooks, skills, subagent prompts, or any LLM interaction: optimizing prompts, improving output reliability, and designing production-grade prompt templates. Trigger words: prompt engineering, prompt, prompt optimization, LLM interaction.