agentleFS
IgorGanapolskyIgorGanapolsky/ThumbGate/.agents/skills/nvidia-specdecode-al-doctor/SKILL.md

nvidia-specdecode-al-doctor

Skill29 starsChanged 7 months agoMITView on GitHub

Fail-closed speculative-decoding AL/D doctor for ThumbGate. Checks speedup ≤ AL/(1+ρD), attention D=128/G-1, tile alignment, and maps to checkpoint-speculative-decoding-acceptance. Steal from NVIDIA co-design blog — never clone TensorRT/EAGLE/Model-Optimizer. Slash: /nvidia-specdecode-al-doctor.

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Treat missing AL as a pass

    line 36

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

NVIDIA Speculative-Decoding AL/D Doctor

When

Speculative decoding, MTP, EAGLE, draft length, accept length, claimed speedup, SPEED-Bench AL talk.

Do

npx thumbgate nvidia-specdecode-al-doctor \
  --speculative-decoding \
  --accept-length=AL \
  --draft-length=D \
  --draft-depth-ratio=ρ \
  --claimed-speedup=X \
  --json

Pair with:

npx thumbgate deepseek-v4-runtime-guardrails \
  --speculative-decoding --accept-length=AL --draft-length=D \
  --draft-depth-ratio=ρ --claimed-speedup=X --cache-coherence-eval --json

Never

  • Claim speedup without measured AL
  • Ship TensorRT-LLM / Model-Optimizer / EAGLE training clones
  • Treat missing AL as a pass

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.